IROS 2011 Awards - Lirmm
IROS 2011 Awards - Lirmm
IROS 2011 Awards - Lirmm
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<strong>2011</strong> IEEE/RSJ International Conference<br />
on Intelligent Robots and Systems<br />
September 25-30, <strong>2011</strong><br />
San Francisco, USA<br />
Celebrating 50 Years of Robotics<br />
Co-Sponsoring Societies<br />
Corporate Sponsors
Foreword<br />
The spring of 1961 marks an important milestone in the history of robotics. The world first<br />
working robot, the Unimate, developed by Devol and Engelberger, was deployed in the<br />
assembly line at General Motors for die casting handling. The centuries-old robot concept<br />
finally came to fruition in the form of a working machine in production. Fifty years later<br />
today, robotics is expanding beyond its success in manufacturing. Our field is entering a<br />
new era of human-centered developments attracting collaborations and interactions<br />
between diverse research communities from different disciplines.<br />
<strong>IROS</strong> <strong>2011</strong> is both a celebration and a renewal. Celebrating the achievements of the field,<br />
the <strong>2011</strong> edition focuses on human-centered robotics bringing a wide coverage of its subfields<br />
and diverse communities. <strong>IROS</strong> <strong>2011</strong> was designed to achieve the highest quality in<br />
technical content and to promote discussion and social interactions. A major feature of this<br />
conference is the interactive presentation model. Authors of selected contributions are<br />
given the opportunity to first outline their work during regular sessions and then to fully<br />
engage small groups of conference attendees, presenting and discussing their work using<br />
videos, slides, and simulators. With the interactive presentations, the number of parallel<br />
tracks is significantly reduced, thus promoting better interaction among the participants.<br />
The conference also features a significant number of special-topic symposia; each<br />
associated with an active research area of robotics introduced by an invited expert.<br />
In addition, the program includes robot demonstrations, thematic plenaries on design, biorobotics,<br />
and intelligent transportation, and four forums: (i) Robots, the Next Generation; (ii)<br />
Medical Robotics; (iii) Robots, the New Platforms; and (iv) Robotics: Beyond the Horizon.<br />
The program also includes a Town Hall Meeting on Robotics Conferences, 27 workshops<br />
and tutorials, technical tours, and a large exhibition showing some of the latest<br />
developments in the field.<br />
<strong>IROS</strong> <strong>2011</strong> received 2459 paper submissions. The reviews were carried out by the<br />
Conference Editorial Board (CEB), which involved 9 editors, 258 associate editors, and<br />
over 3500 reviewers coordinated by the Editor-in-Chief. At its meeting, the Senior Program<br />
Committee discussed the submissions and their corresponding reviews and CEB<br />
recommendations, and finally selected 790 papers for presentation at the conference,<br />
organized in 130 sessions in ten parallel tracks.<br />
A vibrant social program including four events: a Cruise on the San Francisco Bay, an<br />
Evening at the de Young Museum, a Welcome, and a Farewell Reception. This program is<br />
designed to provide an atmosphere for fruitful discussions and meaningful interactions<br />
among the participants.<br />
We would like to take this opportunity to express our thanks and appreciations to all the<br />
members of the Organizing Committee and the Conference Editorial Board for the effort<br />
they devoted to the program and organization of this conference. Our special thanks go to<br />
the volunteers and staffs for the long hours and hard work they have generously given to<br />
the conference. Above all, <strong>IROS</strong> <strong>2011</strong> is the result of its technical contributions, and we<br />
would like to thank all the authors, speakers, and participants for taking part in and<br />
contributing to this conference. We warmly welcome you to San Francisco!<br />
Oussama Khatib Gaurav Sukhatme<br />
General Chair, <strong>IROS</strong> <strong>2011</strong> Program Chair, <strong>IROS</strong> <strong>2011</strong>
Foreword<br />
Table of Contents<br />
<strong>IROS</strong> Organization .…...………………………………………………………………..… ii<br />
<strong>IROS</strong> <strong>2011</strong> Organizing Committee .…………………………………………………….. iii<br />
<strong>IROS</strong> <strong>2011</strong> Conference Editorial Board .……………………………………………….. v<br />
<strong>IROS</strong> <strong>2011</strong> Reviewers .…………………………………………………………………… viii<br />
Plenary Sessions .………………………………………………………………………… xxii<br />
Workshops and Tutorials .………………………………………………………..……… xxv<br />
Demonstration Sessions .………………………………………………………………... xxviii<br />
Exhibition .………………………………………………………………………………..... xxxiv<br />
<strong>IROS</strong> <strong>2011</strong> Corporate Sponsors ..………………………………………………………. xxxvi<br />
<strong>IROS</strong> <strong>2011</strong> <strong>Awards</strong> ……………....………………………………………………………. xxxvii<br />
Social Events .…………………………………………………………………………….. xxxviii<br />
Map of Conference Venue .……………………………………………………………… xl<br />
Conference Overview .…………………………………………………………………… xli<br />
Technical Program at a Glance<br />
Monday, September 26 ..…………………………………………………………... xliii<br />
Tuesday, September 27 ..………………………………………………...………... xliv<br />
Wednesday, September 28 .……………………………………………………….. xlv<br />
Thurdsay, September 29 .....…………………………………………………..….... xlvi<br />
Technical Program Digest<br />
Monday, September 26 ..……………………………………...…………………… 1<br />
Tuesday, September 27.…..………………………………………………...……… 39<br />
Wednesday, September 28 ..….….………………………………………………... 93<br />
Thursday, September 29 …..………………………………………………..……… 167<br />
Author Index ..……………………………………………………………………………... 231<br />
Keyword Index ..…………………………………………………………………………… 261<br />
Corporate Sponsors Ads<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–i–
<strong>IROS</strong> Advisory/Steering Committee<br />
<strong>IROS</strong> Organization<br />
Advisory Council Founding Honorary Chair<br />
Fumio Harashima, Tokyo Metropolitan University, Japan<br />
Past Advisory Council Honorary Chair<br />
Tzyh-Jong Tarn, Washington University, USA<br />
Advisory Council Honorary Chair<br />
C.S. George Lee, Purdue University, USA<br />
Advisory Council Chair<br />
Toshio Fukuda, Nagoya University, Japan<br />
Advisory Council Vice Chair<br />
Shin'ichi Yuta, University of Tsukuba, Japan<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–ii–<br />
Co-sponsoring Societies<br />
IEEE Robotics and Automation Society (RAS)<br />
www.ieee-ras.org<br />
IEEE Industrial Electronics Society (IES)<br />
www.ieee-ies.org<br />
Robotics Society of Japan (RSJ)<br />
www.rsj.or.jp<br />
Society of Instrument and Control Engineers (SICE)<br />
www.sice.or.jp<br />
New Technology Foundation (NTF)<br />
www.ntf.or.jp<br />
Technical Co-sponsoring Society<br />
Institute of Control, Robotics and Systems (ICROS)<br />
www.icros.org
General Chair<br />
<strong>IROS</strong> <strong>2011</strong> Organizing Committee<br />
Oussama Khatib, Stanford University, USA<br />
Program Chair<br />
Gaurav Sukhatme, University of Southern California, USA<br />
Program Co-Chair<br />
Roland Siegwart, Swiss Federal Institute of Technology Zurich, Switzerland<br />
CEB Chair<br />
Nancy M. Amato, Texas A&M University, USA<br />
50 Year of Robotics Symposia<br />
Vijay Kumar (Chair), University of Pennsylvania, USA<br />
Henrik Christensen, Georgia Institute of Technology, USA<br />
Oliver Brock, Technische Universität Berlin, Germany<br />
Interactive Presentations<br />
Daniela Rus (Chair), Massachusetts Institute of Technology, USA<br />
Peter Corke, Queensland University of Technology, Australia<br />
<strong>Awards</strong><br />
Raja Chatila (Chair), Centre National de la Recherche Scientifique, France<br />
Shigeki Sugano, Waseda University, Japan<br />
Allison Okamura, The Johns Hopkins University, USA<br />
Workshops & Tutorials<br />
C.S. George Lee (Chair), Purdue University, USA<br />
Christian Laugier, INRIA Rhône-Alpes, France<br />
Shin’ichi Yuta, University of Tsukuba, Japan<br />
Local Arrangements<br />
Torsten Kroeger (Chair), Stanford University, USA<br />
Francois Conti, Stanford University, USA<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–iii–
Finance Chair<br />
Xiaoping Yun, Naval Postgraduate School, USA<br />
Publications Chair<br />
Alessandro De Luca, Università di Roma "La Sapienza", Italy<br />
Exhibits<br />
Kurt Konolige (Chair), Willow Garage, USA<br />
Luis Sentis, University of Texas at Austin, USA<br />
Kai Oliver Arras, Albert-Ludwigs-Universität Freiburg, Germany<br />
Publicity<br />
Aude Billard (Chair), Swiss Federal Institute of Technology Lausanne, Switzerland<br />
Ken Goldberg, University of California Berkeley, USA<br />
Yoshi Nakamura, University of Tokyo, Japan<br />
Student Travel <strong>Awards</strong><br />
Dezhen Song (Chair), Texas A&M University, USA<br />
M. Ani Hsieh, Drexel University, USA<br />
Dylan Shell, Texas A&M University, USA<br />
Technical Tours<br />
Erin Rapacki, Adept Technology, Inc., USA<br />
Senior Program Committee<br />
Marcelo Ang, National University of Singapore, Singapore<br />
Wolfram Burgard, Albert-Ludwigs-Universität Freiburg, Gemrany<br />
Mark Cutkosky, Stanford University, USA<br />
Rüdiger Dillmann, Karlsruhe Institute of Technology, Germany<br />
Ken Goldberg, University of California Berkeley, USA<br />
Makoto Kaneko, Osaka University, Japan<br />
Lydia Kavraki, Rice University, USA<br />
Dong-soo Kwon, Korea Advanced Institute of Science and Technology, Korea<br />
Kewin Lynch, Northwestern University, USA<br />
Eduardo nebot, University of Sydney, Australia<br />
Brad Nelson, Swiss Federal Institute of Technology Zurich, Switzerland<br />
Bruno Siciliano, Università degli Studi di Napoli Federico II, Italy<br />
Satoshi Tadokoro, Tohoku University, Japan<br />
Seth Hutchinson, University of Illinois at Urbana-Champaign, USA<br />
Stefan Schaal, University of Southern California, USA<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–iv–
<strong>IROS</strong> <strong>2011</strong> Conference Editorial Board<br />
Editor-in-Chief<br />
Nancy M. Amato, Texas A&M University, USA<br />
Editors<br />
I-Ming Chen, Nanyang Technological University, Singapore<br />
Alessandro De Luca, Università di Roma "La Sapienza", Italy<br />
Chad Jenkins, Brown University, USA<br />
Danica Kragic, Royal Institute of Technology, Sweden<br />
Nikos Papanikolopoulos, University of Minnesota, USA<br />
Frank Park, Seoul National University, Korea<br />
Lynne Parker, University of Tennessee, USA<br />
Shigeki Sugano, Waseda University, Japan<br />
Frank van der Stappen, Utrecht University, Netherlands<br />
Associate Editors<br />
Pieter Abbeel, UC Berkeley, USA<br />
Farhad Aghili, Canadian Space Agency, Canada<br />
Aaron Ames, Texas A&M Univ., USA<br />
Yacine Amirat, Univ. Paris Est Créteil, France<br />
Monica Anderson, Univ. of Alabama, USA<br />
Juan Andrade-Cetto, CSIC-UPC, Spain<br />
Wei Tech Ang, Nanyang Tech. Univ., Singapore<br />
Adnan Ansar, JPL, USA<br />
Gianluca Antonelli, Univ. Cassino, Italy<br />
Hirohiko Arai, AIST, Japan<br />
Helder Araujo, Univ. Coimbra, Portugal<br />
Kai Oliver Arras, Univ. Freiburg, Germany<br />
Panagiotis Artemiadis, MIT, USA<br />
Tamim Asfour, KIT, Germany<br />
Olivier Aycard, Joseph Fourier Univ., France<br />
Jan Babic, Inst. Jozef Stefan, Slovenia<br />
Shaoping Bai, Aalborg Univ., Denmark<br />
Devin Balkcom, Dartmouth College, USA<br />
Patrick Beeson, TracLabs Inc, USA<br />
Michael Beetz, TU Munich, Germany<br />
Sven Behnke, Univ. Bonn, Germany<br />
Kostas Bekris, Univ. of Nevada-Reno, USA<br />
Maren Bennewitz, Univ. Freiburg, Germany<br />
Ryad Benosman, UPMC/ISIR, France<br />
Dmitry Berenson, CMU, USA<br />
Sarah Bergbreiter, Univ. of Maryland, USA<br />
Philippe Bidaud, UPMC, Paris 6, France<br />
Nathaniel Bird, Ohio Northern University, USA<br />
Christoph Borst, DLR, Germany<br />
Alan Bowling, Univ. of Texas Arlington, USA<br />
Tim Bretl, Univ. of Illinois Urbana, USA<br />
Michael Bruenig, CSIRO, Australia<br />
Etienne Burdet, Imperial College London, UK<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–v–<br />
Wolfram Burgard, Univ. Freiburg, Germany<br />
Darius Burschka, TU Munich, Germany<br />
David Cappelleri, Stevens Inst. of Tech., USA<br />
Giuseppe Carbone, Univ. Cassino, Italy<br />
Stefano Carpin, Univ. of California Merced, USA<br />
Alicia Casals, UPC, Spain<br />
Jose Castellanos, Univ. Zaragoza, Spain<br />
Francois Chaumette, RIA Rennes, France<br />
XiaoQi Chen, Univ. Canterbury, New Zealand<br />
Sonia Chernova, Worcester Polytech. Inst., USA<br />
Chee Meng Chew, National Univ., Singapore<br />
Kiyo Chinzei, AIST, Japan<br />
Sachin Chitta, Willow Garage Inc., USA<br />
Kyu-Jin Cho, Seoul National Univ., Korea<br />
Youngjin Choi, Hanyang Univ., Korea<br />
Howie Choset, CMU, USA<br />
Changmook Chun, KIST, Korea<br />
Timothy Chung, Naval Postgraduate School, USA<br />
Woojin Chung, Korea Univ., Korea<br />
Chris Clark, California Polytech. State Univ., USA<br />
Nikolaus Correll, Univ. of Colorado Boulder, USA<br />
Juan Cortes, LAAS, France<br />
Jacob Crandall, MIT/Masdar, USA<br />
Elizabeth Croft, Univ. of British Columbia, Canada<br />
Jian Dai, King's College London, UK<br />
Aveek Das, Sarnoff Corporation, USA<br />
Angel del Pobil, Univ. Jaume I, Spain<br />
Jorge Dias, Univ. Coimbra, Portugal<br />
Ryan Eustice, Univ. of Michigan, USA<br />
Manuel Ferre, UP Madrid, Spain<br />
Antoine Ferreira, Univ. Orleans, France<br />
Nicola Ferrier, Univ. of Wisconsin Madison, USA<br />
Paolo Fiorini, Univ. Verona, Italy
Robert Fitch, Univ. Sydney, Australia<br />
Thierry Fraichard, INRIA Rhone-Alpes, France<br />
Emilio Frazzoli, MIT, USA<br />
Eric Frew, Univ. of Colorado, USA<br />
Antonio Frisoli, Scuola Superiore Sant'Anna, Italy<br />
Nicholas Gans, Univ. of Texas Dallas, USA<br />
Roland Geraerts, Utrecht Univ., Netherlands<br />
Christopher Geyer, iRobot Corp., USA<br />
Bill Goodwine, Notre Dame Univ., USA<br />
Ambarish Goswami, Honda Research Inst., USA<br />
Giorgio Grisetti, Univ. Freiburg, Germany<br />
Josechu Guerrero, Univ. Zaragoza, Spain<br />
Eugenio Gugliemelli, Campus Bio-Medico, Italy<br />
Yi Guo, Stevens Inst. of Technology, USA<br />
Kamal Gupta, Simon Fraser Univ., Canada<br />
Li Han, Clark Univ., USA<br />
Kensuke Harada, AIST, Japan<br />
Kris Hauser, Indiana Univ., USA<br />
Shinichi Hirai, Ritsumeikan Univ., Japan<br />
Yasuhisa Hirata, Tohuku Univ., Japan<br />
Koh Hosoda, Osaka Univ., Japan<br />
Franz Hover, MIT, USA<br />
Stefan Hrabar, CSIRO, Australia<br />
M. Ani Hsieh, Drexel Univ., USA<br />
Wes Huang, iRobot Corp., USA<br />
Javier Ibanez-Guzman, Renault, France<br />
Tetsunari Inamura, Nat’l Inst. of Informatics, Japan<br />
Kazuo Ishii, Kyushu Inst. of Technology, Japan<br />
Volkan Isler, Univ. of Minnesota, USA<br />
Hiroyasu Iwata, Waseda Univ., Japan<br />
Michael Jakuba, Univ. Sydney, Australia<br />
Patric Jensfelt, Royal Inst. of Technology, Sweden<br />
Rolf Johansson, Lund Univ., Sweden<br />
Chris Jones, iRobot Corp., USA<br />
Ming-Shaun Ju, Nat’l Cheng Kung Univ., Taiwan<br />
Simon Julier, University College London, UK<br />
Takayuki Kanda, ATR, Japan<br />
Balajee Kannan, Carnegie Mellon Univ., USA<br />
Peter Kazanzides, Johns Hopkins Univ., USA<br />
Abderrahmane Kheddar, CNRS, France/Japan<br />
Ryo Kikuuwe, Kyushu Univ., Japan<br />
Xianwen Kong, Heriot-Watt Univ., UK<br />
Jana Kosecka, George Mason Univ., USA<br />
Hadas Kress-Gazit, Northwestern Univ., USA<br />
Venkat Krovi, Univ. Buffalo (SUNY), USA<br />
Naoyuki Kubota, Tokyo Metropolitan Univ., Japan<br />
Takashi Kubota, JAXA, Japan<br />
James Kuffner, CMU, USA<br />
Dana Kulic, Univ. Waterloo, Canada<br />
Daisuke Kurabayashi, Tokyo Inst. of Tech., Japan<br />
Hanna Kurniawati, SMART, Singapore<br />
Kostas Kyriakopoulos, NTU Athens, Greece<br />
Ville Kyrki, Lappeenranta Univ. of Tech., Finland<br />
Dongjun Lee, Univ. of Tennessee, USA<br />
Ji Yeong Lee, Hanyang Univ., Korea<br />
Weihua Li, Univ. Wolloongong, Australia<br />
Xingyan Li, Univ. Auckland, New Zealand<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–vi–<br />
Yangmin Li, Univ. of Macau, Macau<br />
Jyh-Ming Lien, George Mason Univ., USA<br />
Maxim Likhachev, CMU, USA<br />
Achim Lilienthal, Orebro Univ., Sweden<br />
Pedro Lima, Lisbon Technical Univ., Portugal<br />
Dikai Liu, Univ. of Technology Sydney, Australia<br />
Honghai Liu, Univ. Portsmouth, UK<br />
Peter X. Liu, Carleton Univ., Canada<br />
Xin-Jun Liu, Tsinghua Univ., China<br />
Savvas Loizou, Cyprus Univ. of Tech., Cyprus<br />
Meghann Lomas, Lockheed Martin, USA<br />
Manuel Lopes, INRIA, France<br />
Gonzalo Lopez-Nicolas, Univ. Zaragoza, Spain<br />
Yunjiang Lou, Harbin Inst. of Tech., China<br />
Ou Ma, New Mexico State Univ., USA<br />
Shugen Ma, Ritsumeikan Univ., Japan<br />
Yusuke Maeda, Yokohama National Univ., Japan<br />
Mohammad Mahoor, Denver Univ., USA<br />
Eric Marchand, INRIA Rennes, France<br />
Gian Luca Mariottini, Univ. Texas Arlington, USA<br />
Agostino Martinelli, INRIA Rhone-Alpes, France<br />
Philippe Martinet, Blaise Pascal Univ., France<br />
Alcherio Martinoli, EPFL, Switzerland<br />
Favio Masson, Univ. Nacional del Sur, Argentina<br />
Fumitoshi Matsuno, Kyoto Univ., Japan<br />
Claudio Melchiorri, Univ. Bologna, Italy<br />
Arianna Menciassi, Scuola Sup. Sant'Anna, Italy<br />
Giorgio Metta, IIT, Italy<br />
Javier Minguez, Univ. Zaragoza, Spain<br />
Sarthak Misra, Univ. Twente, Netherlands<br />
Mark Moll, Rice Univ., USA<br />
Katja Mombaur, Univ. Heidelberg, Germany<br />
Hyungpil Moon, Sungkyunkwan Univ., Korea<br />
Antonio Morales, Univ. Jaume I, Spain<br />
Marco Morales, Inst. Tecn. Autónomo, Mexico<br />
Taketoshi Mori, Univ. Tokyo, Japan<br />
Jun Morimoto, ATR, Japan<br />
El Mustapha Mouaddib, Univ. of Picardie, France<br />
Andreas Mueller, Univ. Duisburg-Essen, Germany<br />
Hiroki Murakami, Ishikawajima-Harima Ind., Japan<br />
Rafael Murrieta-Cid, CIMAT, Mexico<br />
Fawzi Nashashibi, INRIA, France<br />
Jose Neira, Univ. Zaragoza, Spain<br />
Jason O'Kane, Univ. of South Carolina, USA<br />
Tetsuya Ogata, Kyoto Univ., Japan<br />
Edwin Olson, Univ. of Michigan, USA<br />
Giuseppe Oriolo, Univ. Roma “La Sapienza”, Italy<br />
Koichi Osuka, Osaka Univ., Japan<br />
Erika Ottaviano, Univ. Cassino, Italy<br />
Erhan Oztop, ATR/NICT, Japan<br />
Taskin Padir, WPI, USA<br />
Evangelos Papadopoulos, NTU Athens, Greece<br />
Jong Hyeon Park, Hanyang Univ., Korea<br />
Ioannis Pavlidis, Univ. Houston, USA<br />
Janice Pearce, Berea College, USA<br />
Angelika Peer, TU Munchen, Germany<br />
Jan Peters, Max Planck Inst., Germany
Thierry Peynot, Univ. Sydney, Australia<br />
Erion Plaku, Catholic Univ. of America, USA<br />
Erwin Prassler, Hoch. Bonn-Rein-Sieg, Germany<br />
Joerg Raczkowsky, Univ. Karlsruhe, Germany<br />
Subramanian Ramamoorthy, Univ. Edinburgh, UK<br />
Yiannis Rekleitis, McGill Univ., Canada<br />
Patrick Rives, INRIA, France<br />
Cameron Riviere, CMU, USA<br />
Paolo Robuffo Giordano, Max Planck Inst., Germany<br />
Paolo Rocco, Politecnico di Milano, Italy<br />
Raul Rojas, Freie Universität Berlin, Germany<br />
Radu Bogdan Rusu, Willow Garage Inc., USA<br />
Paul Rybski, Carnegie Mellon Univ., USA<br />
Jee-Hwan Ryu, Korea Univ. Tech. & Edu., Korea<br />
Carlos Sagues, Univ. Zaragoza, Spain<br />
Srikanth Saripalli, Arizona State Univ., USA<br />
Paul Scerri, Carnegie Mellon Univ., USA<br />
Jim Schmiedeler, Univ. of Notre Dame, USA<br />
Wei-Min Shen, USC, USA<br />
Yantao Shen, Univ. of Nevada, Reno, USA<br />
Anton Shiriaev, Umea Univ., Sweden<br />
Thierry Simeon, LAAS-CNRS, France<br />
Reid Simmons, CMU, USA<br />
Metin Sitti, CMU, USA<br />
Ryan Smith, Queensland Univ. of Tech., Australia<br />
Jorge Solis, Waseda Univ., Japan<br />
Jae-Bok Song, Korea Univ., Korea<br />
Mohan Sridharan, Texas Tech, USA<br />
Siddhartha Srinivasa, Intel Labs, USA<br />
Cyrill Stachniss, Univ. Freiburg, Germany<br />
Mike Stilman, Georgia Tech, USA<br />
Kasper Stoy, Univ of Southern Denmark, Denmark<br />
Ethan Stump, ARL, USA<br />
Jianbo Su, Shanghai Jiao Tong Univ., China<br />
Attawith Sudsang, Chulalongkorn Univ., Thailand<br />
Il Hong Suh, Hanyang Univ., Korea<br />
Salah Sukkarieh, Univ. Sydney, Australia<br />
Dong Sun, City Univ. Hong Kong, China<br />
Yu Sun, Univ. Toronto, Canada<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–vii–<br />
Yukio Takeda, Tokyo Inst. of Technology, Japan<br />
Jindong Tan, MTU, USA<br />
Fang Tang, California State Polytech. Pomona, USA<br />
Bert Tanner, Univ. of Delaware, USA<br />
Adriana Tapus, ParisTech, France<br />
Juan Tardos, Univ. Zaragoza, Spain<br />
Ashley Tews, CSIRO, Australia<br />
Marc Toussaint, TU Berlin, Germany<br />
Jeff Trinkle, Rensselaer Polytechnic Inst., USA<br />
Nikolaos Tsagarakis, IIT, Italy<br />
Ales Ude, Jozef Stefan Inst., Slovenia<br />
Jun Ueda, Georgia Tech, USA<br />
Kazunori Umeda, Chuo Univ., Japan<br />
Pascal Vasseur, Univ. Rouen, France<br />
Manuela Veloso, CMU, USA<br />
Marilena Vendittelli, Univ. Roma “La Sapienza”, Italy<br />
Markus Vincze, Vienna Univ. of Tech., Austria<br />
Marsette Vona, Northeastern Univ., USA<br />
Richard Voyles, Denver Univ., USA<br />
Danwei Wang, Nanyang Techn. Univ., Singapore<br />
Fei-Yue Wang, Univ. of Arizona, USA<br />
Hesheng Wang, Shanghai Jiao Tong Univ., China<br />
Stefan Williams, Univ. Sydney, Australia<br />
Reg Wilson, JPL, USA<br />
Denis Wolf, Univ. Sao Paulo, Brazil<br />
Fengfeng Xi, Ryerson Univ., Canada<br />
Jing Xiao, UNCC, USA<br />
Yasushi Yagi, Osaka Univ., Japan<br />
Katsu Yamane, Disney Research, USA<br />
Guilin Yang, Inst. of Manuf. Tech., Singapore<br />
Anna Yershova, Duke Univ., USA<br />
Jingang Yi, Rutgers, USA<br />
Eiichi Yoshida, AIST, Japan<br />
Takashi Yoshimi, Shibaura Inst. of Tech., Japan<br />
Peijang Yuan, Beihang Univ., China<br />
Fumin Zhang, Georgia Tech, USA<br />
Hong Zhang, Univ. of Alberta, Canada<br />
Jiang-Yu Zheng, IUPUI, USA
<strong>IROS</strong> <strong>2011</strong> Reviewers<br />
Joel Abadie<br />
Gursel Alici<br />
Bilal Ahmed Arain<br />
Jose Baca<br />
Jake Abbott<br />
Hatem Alismail<br />
Nancy Arana-Daniel Abraham Bachrach<br />
Tariq Abdelrahman<br />
Ben Allan<br />
Joan Aranda<br />
Wael Bachta<br />
Mohamed Abderrahim Peter Allen<br />
Miguel Aranda<br />
Leonardo Badino<br />
Hamid Abdi<br />
River Allen<br />
Jumpei Arata<br />
Norm Badler<br />
Nichola Abdo<br />
Guillaume Allibert<br />
Helder Araujo<br />
Ji-Hun Bae<br />
Farzaneh Abdollahi<br />
James Allington<br />
Rui Araújo<br />
Joonbum Bae<br />
A. H. Abdul Hafez<br />
Benedetto Allotta<br />
Mario Arbulu<br />
Stanley Baek<br />
Alexandre Abellard<br />
Jose Almeida<br />
Laurent Arcese<br />
Mitra Bahadorian<br />
Leon Abelmann<br />
Luis Almeida<br />
James Archibald<br />
Alexander Bahr<br />
Pramod Abichandani Oscar Alonso<br />
Gustavo Arechavaleta He Bai<br />
Satoko Abiko<br />
Redwan Alqasemi<br />
Juan Carlos Arevalo Qadeer Baig<br />
Elias Abou Zeid<br />
Pablo Javier Alsina<br />
Adrian Arfire<br />
Tim Bailey<br />
Konstantinos Aboudolas Ron Alterovitz<br />
Brenna Argall<br />
John Baillieul<br />
Dino Accoto<br />
Salah Althloothi<br />
Sylvain Argentieri<br />
Andrea Bajo<br />
Jeffrey Ackerman<br />
Kaspar Althoefer<br />
Miguel Arias<br />
Max Bajracharya<br />
Carlos A. Acosta Calderon Daniel Althoff<br />
Hiroaki Arie<br />
Christopher R Baker<br />
Martin Adams<br />
Armando Alves Neto Keisuke Arikawa<br />
Benjamin Balaguer<br />
Antonio Adan<br />
Angelos Amanatiadis Masakazu Arima<br />
Carlos Balaguer<br />
Olaoluwa Adeniba<br />
Yoshiharu Amano<br />
Hichem Arioui<br />
Stephen Balakirsky<br />
Nagesh Adluru<br />
Josep Amat<br />
Christopher Armbrust Ravi Balasubramanian<br />
Lounis Adouane<br />
Nancy M. Amato<br />
Leopoldo Armesto<br />
Sivakumar Balasubramanian<br />
Erwin Aertbelien<br />
Cinzia Amici<br />
Jose Armingol<br />
Tucker Balch<br />
Zohaib Aftab<br />
Luis Ernesto Amigo<br />
Kai Oliver Arras<br />
Devin Balkcom<br />
Gabriel Agamennoni Francesco Amigoni<br />
Filippo Arrichiello<br />
Dana Ballard<br />
Priyanshu Agarwal<br />
Vahid Aminzadeh<br />
Marc Arsenault<br />
Hansjorg Baltes<br />
Ali-akbar Agha-mohammadi Farshid Amirabdollahian Marc Arsicault<br />
Haris Baltzakis<br />
Navid Aghasadeghi<br />
Mehdi Ammi<br />
Panagiotis Artemiadis Xiaojun Ban<br />
Mahdi Agheli<br />
Arvind Ananthanarayanan Jordi Artigas<br />
Jared Bancroft<br />
Farhad Aghili<br />
Margarita Anastassova Minoru Asada<br />
Antonio Bandera<br />
Noa Agmon<br />
Michael Skipper Andersen Ali Asadian<br />
Antonio Bandera Rubio<br />
Lucio Agostinho Rocha John Anderson<br />
Masoud Asadpour<br />
Juan Pedro Bandera Rubio<br />
Amer Agovic<br />
Monica Anderson<br />
Hajime Asama<br />
Tirthankar Bandyopadhyay<br />
Gabriel Aguirre-Ollinger Sterling Anderson<br />
Fumihiko Asano<br />
Mayank Bansal<br />
Carl Ahlberg<br />
Stuart Anderson<br />
Tamim Asfour<br />
Emilia I. Barakova<br />
Aamir Ahmad<br />
Sean Andersson<br />
Lars Asplund<br />
Adrien Baranes<br />
Mojtaba Ahmadi<br />
Franz Andert<br />
Seyed Farokh Atashzar Jose Barata<br />
Hossein Ahmadzadeh Takeshi Ando<br />
J. Alan Atherton<br />
Timothy Barfoot<br />
Nisar Ahmed<br />
Juan Andrade-Cetto Christopher Atkeson Stephen Barkby<br />
Sang Chul Ahn<br />
Henrik Andreasson<br />
Jason Atkin<br />
Adrien Barral<br />
SungHwan Ahn<br />
Nicolas Andreff<br />
Samuel Au<br />
Alejandra Barrera<br />
Omar Ait-Aider<br />
Alexander Andreopoulos Tsz-Chiu Au<br />
João P. Barreto<br />
Yasumichi Aiyama<br />
Claude Andriot<br />
Fernando Auat Cheein David Barrett<br />
Batu Akan<br />
Wei Tech Ang<br />
Pierre Avanzini<br />
Antonio Barrientos<br />
Adel Akbarimajd<br />
Jorge Angeles<br />
J. Gabriel Avina-Cervantes Jordi Barrio Gragera<br />
Abdullah Akce<br />
Adrien Angeli<br />
Carlo Alberto Avizzano Luis J. Barrios<br />
Erhan Akdogan<br />
Andreas Angerer<br />
Kean C. Aw<br />
Adrien Bartoli<br />
Srinivas Akella<br />
David A. Anisi<br />
Ben Axelrod<br />
Luca Bascetta<br />
Baris Akgun<br />
Roberta Annicchiarico Mustafa Ayad<br />
Cagatay Basdogan<br />
Aadeel Akhtar<br />
David Miguel Antunes Victor Ayala-Ramirez Nicola Basilico<br />
H. Levent Akin<br />
Salvatore Maria Anzalone Nora Ayanian<br />
Maxim Batalin<br />
Yoshitake Akiyama<br />
Shinya Aoi<br />
Olivier Aycard<br />
Adam Bates<br />
Samer Al Moubayed Takeshi Aoki<br />
Alper Aydemir<br />
Jorge Batista<br />
Sven Albrecht<br />
Yannick Aoustin<br />
Pedram Azad<br />
Gabriel Baud-Bovy<br />
Alin Albu-Schäffer<br />
Tadayoshi Aoyama<br />
Christine Azevedo Coste Marcus Baum<br />
Javier Adolfo Alcazar Sumeet S. Aphale<br />
Asma Azim<br />
Eric Baumgartner<br />
Feras Alchama<br />
Ilias Apostolopoulos Jose Raul Azinheira Enrique Bauzano<br />
David Aldavert<br />
Rosario Aragues<br />
Mahdi Azizian<br />
Eduardo-J. Bayro-Corrochano<br />
Alen Alempijevic<br />
Fumihito Arai<br />
Jose M. Azorin<br />
Matthew Bays<br />
Guillem Alenyà<br />
Takeshi Arai<br />
Y. Babazadeh Bedoustani Cabbar V. Baysal<br />
Jacopo Aleotti<br />
Tatsuo Arai<br />
Jan Babic<br />
Ali Bazaei<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–viii–
Stephane Bazeille<br />
Jean-Charles Bazin<br />
Chris Beall<br />
Ozkan Bebek<br />
Israel Becerra<br />
Aaron Becker<br />
Brian C. Becker<br />
Jan Becker<br />
Marcelo Becker<br />
Randall Beer<br />
Kristopher Beevers<br />
Jounghoon Beh<br />
Aman Behal<br />
Evan Behar<br />
Laxmidhar Behera<br />
Michael Behrens<br />
Roland Behrens<br />
Maximilian Beinhofer<br />
Kostas E. Bekris<br />
Anna Belardinelli<br />
Rachid Belaroussi<br />
Karim Belharet<br />
Martin Bello<br />
Nicola Bellotto<br />
Tony Belpaeme<br />
Faiz Ben Amar<br />
Heni Ben Amor<br />
Fathi Ben Ouezdou<br />
Pinhas Ben-Tzvi<br />
Mehdi Benallegue<br />
Asher Bender<br />
Rodrigo Benenson<br />
Ben Benfold<br />
Beno Benhabib<br />
Mohamed Bensalah<br />
Darrin Bentivegna<br />
Ahmed Benzerrouk<br />
Dmitry Berenson<br />
Massimo Bergamasco<br />
Luis Miguel Bergasa<br />
Sarah Bergbreiter<br />
Christos Bergeles<br />
Cyrille Berger<br />
Marcel Bergerman<br />
Peter Berkelman<br />
Spring Berman<br />
Alexandre Bernardino<br />
Karsten Berns<br />
Christian Bersch<br />
Giovanni Berselli<br />
Jean-Marc Berthommé<br />
Sylvain Bertrand<br />
Eva Besada-Portas<br />
Ross Beveridge<br />
David Bevly<br />
Felix Beyeler<br />
Jürgen Beyerer<br />
Nicola Bezzo<br />
Amit Bhatia<br />
Rajankumar Bhatt<br />
Sourabh Bhattacharya<br />
Subhrajit Bhattacharya<br />
Luigi Biagiotti<br />
Joshua J Bialkowski<br />
Antonio Bicchi<br />
Manuele Bicego<br />
Estela Bicho<br />
Philippe Bidaud<br />
Julien Bidot<br />
Alexander Bierbaum<br />
Geoffrey Biggs<br />
Hakan Bilen<br />
Aude Billard<br />
Ghassan Bin Hammam<br />
Jonathan Binney<br />
Oliver Birbach<br />
Stan Birchfield<br />
Nathaniel Bird<br />
Andreas Birk<br />
Rachael Bis<br />
Horst Bischof<br />
Bradley Bishop<br />
Hannes Bistry<br />
Joydeep Biswas<br />
Sebastian Bitzer<br />
Lars Blackmore<br />
François Blais<br />
Jose-Luis Blanco<br />
M. Dolores Blanco<br />
Sebastian Blumenthal<br />
Antônio Padilha Lanari Bó<br />
Michael Boardman<br />
Botond Bocsi<br />
Jeannette Bohg<br />
Aude Bolopion<br />
Vanderlei Bonato<br />
Adrian Bonchis<br />
Gary Bone<br />
Ilian Bonev<br />
Marcello Bonfe<br />
Silvere Bonnabel<br />
Devin Bonnie<br />
Philippe Bonnifait<br />
Wayne Book<br />
Shaunak D. Bopardikar<br />
Mirko Bordignon<br />
Johann Borenstein<br />
Paulo Vinicius K. Borges<br />
Gianni Borghesan<br />
Per Henrik Borgstrom<br />
Ali Borji<br />
Branislav Borovac<br />
Michael Bosse<br />
Silvia Botelho<br />
Debora Botturi<br />
Samir Bouabdallah<br />
Patrick Michael Bouffard<br />
Abdelbaki Bouguerra<br />
Mehdi Boukallel<br />
Abdeslam Boularias<br />
Mohamed Bouri<br />
Karim Bouyarmane<br />
Alan Bowling<br />
Isil Bozma<br />
David Bradley<br />
David Bradley<br />
Gary Bradski<br />
Julia Braman<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–ix–<br />
Alexandre Santos Brandao<br />
David Brandt<br />
Michael Branicky<br />
Kusy Brano<br />
David J. Braun<br />
Cynthia Breazeal<br />
Sebastian Brechtel<br />
Andreas Breitenmoser<br />
Michael Brenner<br />
Jose Breñosa<br />
Timothy Bretl<br />
Reuben Brewer<br />
Adrien Briod<br />
Sébastien Briot<br />
Oliver Brock<br />
Roland Brockers<br />
Yury Brodskiy<br />
Graham Brooker<br />
Christopher Brooks<br />
Jonathan Brookshire<br />
Xavier Broquere<br />
H. Ben Brown<br />
Travis Brown<br />
Brett Browning<br />
Frank Broz<br />
Drazen Brscic<br />
James Robert Bruce<br />
Davide Brugali<br />
Alberto Brunete<br />
Emma Brunskill<br />
Herman Bruyninckx<br />
Adam Bry<br />
Mitch Bryson<br />
Dirk Buchholz<br />
Jonas Buchli<br />
Jennifer Buehler<br />
Heiko Buelow<br />
Jose M. Buenaposada<br />
Olivier Buffet<br />
Magdalena Bugajska<br />
Chris Burbridge<br />
Wolfram Burgard<br />
Xavier Burgos-Artizzu<br />
Antoni Burguera<br />
Arne Burisch<br />
Jenny Burke<br />
Corves Burkhard<br />
Darius Burschka<br />
Thomas Buschmann<br />
Antoine Bussy<br />
Evan Butler<br />
Zack Butler<br />
Jonathan Butzke<br />
Georg Bätz<br />
Berthold Bäuml<br />
Fernando Caballero<br />
Flavio Cabrera-Mora<br />
Gonçalo Cabrita<br />
Fabrizio Caccavale<br />
Cesar Dario Cadena Lerma<br />
Binghuang Cai<br />
Hongyuan Cai<br />
Yueri Cai<br />
Andrea Caiti<br />
Maya Cakmak<br />
Andrea Calanca<br />
Darwin G. Caldwell<br />
Timothy Caldwell<br />
Daniele Caligiore<br />
Sylvain Calinon<br />
Daniele Calisi<br />
Massimo Callegari<br />
Andrew Calway<br />
Stephen Cameron<br />
Jason Campbell<br />
Mark Campbell<br />
Domenico Campolo<br />
Jordi Campos<br />
Mario Montenegro Campos<br />
Salvatore Candido<br />
Angelo Cangelosi<br />
Giorgio Cannata<br />
Jiangtao Cao<br />
Jesus Capitan<br />
David Cappelleri<br />
Barbara Caputo<br />
Alexander Carballo<br />
Giuseppe Carbone<br />
Philippe Cardou<br />
Ricardo Carelli<br />
Anthony Carfang<br />
Craig Carignan<br />
Christophe Cariou<br />
Nicolas Carlési<br />
Luca Carlone<br />
Raffaella Carloni<br />
Tom Carlson<br />
Stéphane Caro<br />
Guillaume Caron<br />
Stefano Carpin<br />
Giorgio Carpino<br />
Marc Carreras<br />
Juan A. Carretero<br />
Henry Carrillo<br />
J. Carlos Mendes Carvalho<br />
Maura Casadio<br />
Giuseppe Casalino<br />
Gianni Castelli<br />
Virginia Castelli<br />
Claudio Castellini<br />
Pedro Castillo<br />
Eduardo Castillo-Castaneda<br />
Modesto Castrillón<br />
Glauco A. de Paula Caurin<br />
Albert Causo<br />
Adriano Cavalcanti<br />
Filippo Cavallo<br />
Gary Cavanough<br />
M. Cenk Cavusoglu<br />
Miguel Cazorla<br />
Enric Celaya<br />
Zhiwei Cen<br />
Andrea Censi<br />
Pablo Cerrada<br />
Enric Cervera<br />
Damien Chablat<br />
Marco A. Chacin Torres<br />
Brahim Chaib-draa
Nicolas Chaillet<br />
Luiz Chaimowicz<br />
Nilanjan Chakraborty<br />
Punarjay Chakravarty<br />
Suman Chakravorty<br />
Hugo Chale-Gongora<br />
Maxime Chalon<br />
Lyle Chamberlain<br />
Thierry Chaminade<br />
Ambrose Chan<br />
Chen-Yu Chan<br />
Mervin Chandrapal<br />
Doyoung Chang<br />
Jen-Yuan (James) Chang<br />
Lillian Chang<br />
Richard Chang<br />
Yung-Jung Chang<br />
Crystal Chao<br />
Roland Chapuis<br />
Jean-Remy Chardonnet<br />
Marcela Charfuelan<br />
Francois Charpillet<br />
Thierry Chateau<br />
Dimitris Chatzigeorgiou<br />
Pratik Chaudhari<br />
Francois Chaumette<br />
Ricardo O. Chavez Garcia<br />
C. C. Cheah<br />
Paul Checchin<br />
Denis Chekhlov<br />
Ryad Chellali<br />
Ahmed Chemori<br />
Baojun Chen<br />
Chao Chen<br />
Haoyao Chen<br />
Heping Chen<br />
Hongjun Chen<br />
Ian Yen-Hung Chen<br />
Jian Chen<br />
Qijun Chen<br />
Qing Chen<br />
Weidong Chen<br />
Weihai Chen<br />
Wenjie Chen<br />
Wenjie Chen<br />
Xin Chen<br />
Xin Chen<br />
Yongquan Chen<br />
Yuren Chen<br />
Zhaopeng Chen<br />
Zheng Chen<br />
Zhiyong Chen<br />
Gordon Cheng<br />
Harry Cheng<br />
Victor H.L. Cheng<br />
Yang Cheng<br />
Joono Cheong<br />
Véronique Cherfaoui<br />
Anoop Cherian<br />
Sonia Chernova<br />
Andrea Cherubini<br />
Graziano Chesi<br />
Mohamed Chetouani<br />
Christine Chevallereau<br />
Chee Meng Chew<br />
Héctor Chiacchiarini<br />
Mu Chiao<br />
Ryosuke Chiba<br />
Cheng Chin<br />
Eris Chinellato<br />
Francesco Chinello<br />
Kiyoyuki Chinzei<br />
Gregory Chirikjian<br />
Hamidreza Chitsaz<br />
Han-Pang Chiu<br />
Margarita Chli<br />
Baek-Kyu Cho<br />
Jang Ho Cho<br />
Kyu-Jin Cho<br />
Han-Lim Choi<br />
Hee-Byoung Choi<br />
Jeong-Sik Choi<br />
Young-ho Choi<br />
Youngjin Choi<br />
Nikhil Chopra<br />
Safwan Choudhury<br />
Anders Lyhne Christensen<br />
David Johan Christensen<br />
Henrik Iskov Christensen<br />
Anna-Karin Christiansson<br />
Eftychios Christoforou<br />
Kang-Ching Chu<br />
Daisuke Chugo<br />
Changmook Chun<br />
Timothy H. Chung<br />
Wan Kyun Chung<br />
Matteo Cianchetti<br />
Anna Lisa Ciancio<br />
Grzegorz Cielniak<br />
Matei Ciocarlie<br />
Christian Cipriani<br />
Xavier Clady<br />
Mark Claffee<br />
Christopher M. Clark<br />
James Clark<br />
Jonathan Clark<br />
José Claver<br />
Kevin Cleary<br />
Charles Clercq<br />
Cédric Clévy<br />
Salvador Cobos Guzman<br />
José M. Cogollor Delgado<br />
Benjamin Cogrel<br />
Benjamin Cohen<br />
Paul Cohen<br />
A. Paulo Coimbra<br />
Michael Coleman<br />
Edward Colgate<br />
Alvaro Collet<br />
Christophe Collewet<br />
Roberto Colombo<br />
Julian Colorado<br />
Brian Coltin<br />
Frederic Comby<br />
Olivier Company<br />
Andrew Ian Comport<br />
Michele Conconi<br />
Shuang Cong<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–x–<br />
David Conner<br />
Christian Pascal Connette<br />
Jorg Conradt<br />
Daniela Constantinescu<br />
Francois Conti<br />
Marco Controzzi<br />
Atlas F. Cook IV<br />
Joseph Cooper<br />
Jeremy Cooperstock<br />
Lennon Cork<br />
Peter Corke<br />
Alex Cornejo<br />
Jordi Cornella<br />
Nikolaus Correll<br />
Nick Corson<br />
Rui Cortesao<br />
Anna H. R. Costa<br />
Joao Paulo Costeira<br />
Sebastien Cotton<br />
Estelle Courtial<br />
Noah J. Cowan<br />
Anthony Cowley<br />
Eric Coyle<br />
John Crassidis<br />
Alessandro Crespi<br />
Vincent Creuze<br />
Vincent Crocher<br />
André Crosnier<br />
Charles R. Crowell<br />
Xavier Cufi<br />
Jinshi Cui<br />
Lei Cui<br />
Yanzhe Cui<br />
Mark Joseph Cummins<br />
Alexander Cunningham<br />
Robert Cupec<br />
Mark Cutkosky<br />
Filippo D'Ippolito<br />
Boubaker Daachi<br />
Torbjorn Dahl<br />
Christian Dahmen<br />
Xiaochen Dai<br />
Matthew N. Dailey<br />
Konstantinos Dalamagkidis<br />
Sebastien Dalibard<br />
Fabio DallaLibera<br />
Skyler Dalley<br />
Mohsen Dalvand<br />
Bruno Damas<br />
Patrick Danès<br />
Hao Dang<br />
Kostas Daniilidis<br />
Todd Danko<br />
Neil Dantam<br />
Thanh-Son Dao<br />
Behzad Dariush<br />
Colin Das<br />
Jnaneshwar Das<br />
Raj Dasgupta<br />
Hisashi Date<br />
Chandan Datta<br />
Philip David<br />
Duane Davis<br />
James Davis<br />
Andrew J Davison<br />
Alireza Davoodi<br />
Shameka Dawson<br />
Feras Dayoub<br />
Anibal de Almeida<br />
Raoul de Charette<br />
Jesús M de la Cruz<br />
Arturo de la Escalera<br />
Tinne De Laet<br />
Kathryn De Laurentis<br />
Alessandro De Luca<br />
Giuseppe De Maria<br />
A. A. de Menezes Pereira<br />
Elena De Momi<br />
Agostino De Santis<br />
Michael Defoort<br />
Sarah Degallier<br />
Amir Degani<br />
Ehsan Dehghan<br />
Marc Peter Deisenroth<br />
Jaime del Cerro<br />
Frédéric Delaunay<br />
Anne Delettre<br />
Frank Dellaert<br />
Babette Dellen<br />
Vivien Delsart<br />
Eric Demeester<br />
Emel Demircan<br />
Yiannis Demiris<br />
Cédric Demonceaux<br />
Oscar Déniz<br />
Jory Denny<br />
Anna Derbakova<br />
Jason Derenick<br />
Konstantinos Dermitzakis<br />
Ashish Deshpande<br />
Guilherme DeSouza<br />
Renaud Detry<br />
Carrick Detweiler<br />
Michel Devy<br />
Debadeepta Dey<br />
Travis Deyle<br />
Ezequiel Di Mario<br />
Rosen Diankov<br />
Xiumin Diao<br />
Jorge Dias<br />
M. Bernardine Dias<br />
M. Freddie Dias<br />
Iñaki Díaz<br />
James Diaz<br />
Jakob Lund Dideriksen<br />
Alexander Dietrich<br />
Charles Dietrich<br />
Michael Dille<br />
Eric D. Diller<br />
Rüdiger Dillmann<br />
Edward Dillon<br />
Haris Dindo<br />
Feng Ding<br />
HuaFeng Ding<br />
Liang Ding<br />
Xilun Ding<br />
Xu Chu Ding<br />
Thang Dinh
Albert Diosi<br />
Gamini Dissanayake<br />
Warren Dixon<br />
Vladimir Djapic<br />
Dalila Djoudi<br />
Martin Do<br />
Phuong Anh Do Hoang<br />
Andrew Dobson<br />
Zachary Dodds<br />
Mehmet Remzi Dogar<br />
Nakju Doh<br />
Miwato Doi<br />
Christophe Doignon<br />
John M. Dolan<br />
Dmitri Dolgov<br />
Mihai Emanuel Dolha<br />
Irina Dolinskaya<br />
Aaron Dollar<br />
Etienne Dombre<br />
Paolo Domenici<br />
Peter Ford Dominey<br />
Haiwei Dong<br />
Hao Dong<br />
Jun Feng Dong<br />
Lixin Dong<br />
Shuonan Dong<br />
Wenjie Dong<br />
Strahinja Dosen<br />
Finale Doshi<br />
Bertrand Douillard<br />
Zoe Doulgeri<br />
Anca Dragan<br />
Andrew Drenner<br />
Paulo Drews Jr<br />
Evan Drumwright<br />
Sebastien Druon<br />
Haiping Du<br />
Noel E. Du Toit<br />
Steven Dubowsky<br />
Tom Duckett<br />
Gregory Dudek<br />
Elliot Duff<br />
Matthew David Dunbabin<br />
Claire Dune<br />
Matthew Dunnigan<br />
Tuan Duong<br />
Edmond DuPont<br />
Pierre Dupont<br />
Francesco Durante<br />
Christian Duriez<br />
Jason Durrie<br />
Alexander Duschau-Wicke<br />
Ethan Eade<br />
Matthew Earl<br />
Aaron Edsinger<br />
Magnus Egerstedt<br />
Ruwan Egoda Gamage<br />
Amy Eguchi<br />
Donald Eickstedt<br />
Robert Eidenberger<br />
Carl Henrik Ek<br />
Chinwe Ekenna<br />
Oussama El Hamzoui<br />
Sahar El Khoury<br />
Ahmed K. El-Shenawy<br />
Imad Elhajj<br />
Haytham Elhawary<br />
Robert Ellenberg<br />
Jan Elseberg<br />
Jack Elston<br />
Phillips Emilie<br />
Gen Endo<br />
Takahiro Endo<br />
Felix Endres<br />
Erik Daniel Engeberg<br />
Brendan Englot<br />
Clemens Eppner<br />
Alina M. Eqtami<br />
Kemalettin Erbatur<br />
Tom Erez<br />
Lawrence H Erickson<br />
Stefan Ericson<br />
Gorkem Erinc<br />
Aydan Erkmen<br />
Ismet Erkmen<br />
Duygun Erol Barkana<br />
Adrien Escande<br />
Bernard Espiau<br />
Joel Esposito<br />
Claudia Esteves<br />
Carlos Estrada<br />
Simos Evangelou<br />
William C. Evans<br />
Jani Even<br />
Jacob Everist<br />
Friederike Eyssel<br />
Stephan Fabel<br />
Tibor Fabian<br />
Andrew Fagg<br />
Farbod Fahimi<br />
Nathaniel Fairfield<br />
Pietro Falco<br />
Riccardo Falconi<br />
Maurice Fallon<br />
Egidio Falotico<br />
Yongchun Fang<br />
Pietro Fanghella<br />
Isabelle Fantoni<br />
Gregory Faraut<br />
Alessandro Farinelli<br />
Ildar Farkhatdinov<br />
Shane Farritor<br />
Imraan Faruque<br />
Juan Fasola<br />
John Fasoulas<br />
Sergej Fatikow<br />
Abbas Fattah<br />
Ehsan Fazl-Ersi<br />
Pooyan Fazli<br />
Ronald Fearing<br />
Duc Fehr<br />
David Feil-Seifer<br />
Javier Felip<br />
Martin Felis<br />
Mirko Felisa<br />
Vicente Feliu<br />
Aaron Fenster<br />
Cornelia Fermuller<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xi–<br />
Leandro Carlos Fernandes<br />
Eduardo Fernandez<br />
P. Fernández Alcantarilla<br />
M. Fernandez-Carmona<br />
Jesus Fernandez-Lozano<br />
J.-A. Fernandez-Madrigal<br />
C. Fernandez-Maloigne<br />
Vincenzo Ferrari<br />
Manuel Ferre<br />
Alexander Ferrein<br />
Antoine Ferreira<br />
João Filipe Ferreira<br />
Manuel Ferreira<br />
Gonzalo Ferrer<br />
Gianni Ferretti<br />
Giancarlo Ferrigno<br />
António Ferrolho<br />
Mark Fiala<br />
Gabor Fichtinger<br />
MaryAnne Fields<br />
Rafael Fierro<br />
Giorgio Figliolini<br />
Ioannis Filippidis<br />
David Filliat<br />
Holger Finger<br />
Allan Daniel Finistauri<br />
Jonathan Fink<br />
Bernd Finkemeyer<br />
Paolo Fiorini<br />
Gregory Fischer<br />
Jan Fischer<br />
Wolfgang Fischer<br />
Robert Charles Fitch<br />
Kevin Fite<br />
Fabrizio Flacco<br />
David Flavigné<br />
Daniel Montrallo Flickinger<br />
Dario Floreano<br />
Mihaela Cecilia Florescu<br />
Michele Focchi<br />
David Fofi<br />
Torea Foissotte<br />
Amalia Foka<br />
David Folio<br />
John Folkesson<br />
Romeo Fomena Tatsambon<br />
Josep Maria Font-Llagunes<br />
Marco Fontana<br />
Daniele Fontanelli<br />
James Richard Forbes<br />
Amir Ali Forough Nassiraei<br />
Per-Erik Forssen<br />
Clément Fouque<br />
Michael Fox<br />
Jan-Michael Frahm<br />
Juan-Carlos Fraile<br />
Philippe Fraisse<br />
Antonio Franchi<br />
Barbara Frank<br />
Heinz Frank<br />
Jörg Franke<br />
Michel Franken<br />
Friedrich Fraundorfer<br />
Emilio Frazzoli<br />
Luigi Freda<br />
Regina Frei<br />
Leonid Freidovich<br />
Vincent Fremont<br />
Udo Frese<br />
Eric W. Frew<br />
Georges Fried<br />
Werner Friedl<br />
Martin Friedmann<br />
Simone Frintrop<br />
Mario Fritz<br />
Anselmo Frizera Neto<br />
Emanuele Frontoni<br />
Dominic R. Frutiger<br />
Li-Chen Fu<br />
Michael J. Fu<br />
Karl Cheng-Heng Fua<br />
Ohmi Fuchiwaki<br />
Masakatsu G. Fujie<br />
Hideo Fujimoto<br />
Kikuo Fujimura<br />
Toshihiro Fujita<br />
Takanori Fukao<br />
Toshio Fukuda<br />
Kotaro Fukui<br />
Rui Fukui<br />
Yasuhiro Fukuoka<br />
Edwardo F. Fukushima<br />
Yosuke Fukushima<br />
Matteo Fumagalli<br />
Ryu Funase<br />
Tetsuro Funato<br />
Juan G. Victores<br />
Péter Galambos<br />
Ignacio Galiana<br />
Cipriano Galindo<br />
Juan Alvaro Gallego<br />
Kevin Galloway<br />
Stefano Galvan<br />
Dorian Galvez Lopez<br />
Javier Gamez Garcia<br />
Andrej Gams<br />
Dongming Gan<br />
Seng Keat Gan<br />
Jacques Gangloff<br />
Bingtuan Gao<br />
Haibo Gao<br />
Elena Garcia<br />
Fernando Garcia<br />
Pablo Garcia<br />
Rafael Garcia<br />
Sebastian García<br />
Alfonso García-Cerezo<br />
Simon Garnier<br />
Carlos Garre<br />
Anais Garrell<br />
Jose Gaspar<br />
Alessandro Gasparetto<br />
Andrea Gasparri<br />
Roger Gassert<br />
Philippe Gaussier<br />
Michael Gauthier<br />
Russell Gayle<br />
Yunjian Ge
Anne-Lise Gehin<br />
Christopher Geib<br />
Andreas Geiger<br />
Peter Gemeiner<br />
Sebastien Gemme<br />
Michael Gennert<br />
Brian Gerkey<br />
Gregory J. Gerling<br />
Sven Gestegård Robertz<br />
Hartmut Geyer<br />
Robert Ghrist<br />
Michael Gienger<br />
Philippe Giguere<br />
Arturo Gil<br />
Stephanie Gil<br />
Jeremy Gillula<br />
Antonio Gimenez<br />
Luca Giona<br />
Xavier Giralt<br />
Nivedhitha Giri<br />
Dylan F. Glas<br />
Sebastien Glaser<br />
Guy Godin<br />
Roy Godzdanker<br />
Robert Goeddel<br />
Christian Goerick<br />
Martin Goerner<br />
Grigore Gogu<br />
Orcun Goksel<br />
Ken Goldberg<br />
Michael Goldfarb<br />
Fernando Gomez<br />
A. Gómez de Silva Garza<br />
Nelson Goncalves<br />
Paulo Gonçalves<br />
Tiago Gonçalves<br />
Javier Gonzalez<br />
Juan Pablo Gonzalez<br />
Víctor González<br />
Yolanda Gonzalez<br />
Luis E. González-Jiménez<br />
Bill Goodwine<br />
Faramarz Gordaninejad<br />
Francisco Gordillo<br />
Clement Gosselin<br />
Florian Gosselin<br />
Frederick P. Gosselin<br />
Ambarish Goswami<br />
Dip Goswami<br />
Michèle Gouiffès<br />
Stephen Gould<br />
François Goulette<br />
Marc Gouttefarde<br />
Sven Gowal<br />
Nuno Gracias<br />
Devin Grady<br />
Birgit Graf<br />
Christophe Grand<br />
Grzegorz Granosik<br />
Oscar G. Grasa<br />
Valdir Grassi Junior<br />
Antoni Grau<br />
Jan Tommy Gravdahl<br />
David Gravel<br />
Andrew Gray<br />
Steven Gray<br />
Markus Grebenstein<br />
Colin Green<br />
Keith Evan Green<br />
Robert D. Gregg<br />
Nicola Greggio<br />
Lorenzo Grespan<br />
Matthew Greytak<br />
Elena Gribovskaya<br />
Matthew Koichi Grimes<br />
Garrett Grindle<br />
Giorgio Grioli<br />
Ben Grocholsky<br />
Martin Groeger<br />
Daniel Grollman<br />
William Grosky<br />
Roderich Gross<br />
Thilo Grundmann<br />
Rod Grupen<br />
Slawomir Grzonka<br />
Jason Gu<br />
Yisheng Guan<br />
Corrado Guarino Lo Bianco<br />
Wail Gueaieb<br />
Eugenio Guglielmelli<br />
Mohamed Guiatni<br />
Jose Guivant<br />
James Gunderson<br />
Keith Gunura<br />
Chunzhao Guo<br />
Shuxiang Guo<br />
Yan Guo<br />
Yi Guo<br />
Rakesh Gupta<br />
Sabri Gurbuz<br />
Hakan Gurocak<br />
Jens-Steffen Gutmann<br />
Stephen J. Guy<br />
Felix Hackbarth<br />
Yassine Haddab<br />
Amir Haddadi<br />
Sami Haddadin<br />
Hicham Hadj-Abdelkader<br />
Testuji Haga<br />
Nicola Hagemeister<br />
Gregory Hager<br />
Norihiro Hagita<br />
Masaya Hagiwara<br />
Tamas Haidegger<br />
Adam Halasz<br />
Dogan Sinan Haliyo<br />
Aarne J. Halme<br />
Dan Halperin<br />
Masaki Hamamoto<br />
Tarek Hamel<br />
William R. Hamel<br />
Chang-Soo Han<br />
Gyu Chull Han<br />
Jianda Han<br />
Kyung min Han<br />
Li Han<br />
Long Han<br />
Ryo Hanai<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xii–<br />
Ankur Handa<br />
Uwe D. Hanebeck<br />
Guangbo Hao<br />
Masayuki Hara<br />
Naoyuki Hara<br />
Susumu Hara<br />
Kanako Harada<br />
Kensuke Harada<br />
Takashi Harada<br />
Tatsuya Harada<br />
Frederick Harris<br />
Stephen Hart<br />
Robert Haschke<br />
Osamu Hasegawa<br />
Tadahiro Hasegawa<br />
Tsutomu Hasegawa<br />
Yasuhisa Hasegawa<br />
Kenji Hashimoto<br />
Koichi Hashimoto<br />
Minoru Hashimoto<br />
Shuji Hashimoto<br />
Takuya Hashimoto<br />
Naohisa Hashimoto<br />
Takeshi Hatanaka<br />
Naotaka Hatao<br />
Ross Hatton<br />
Sabine Hauert<br />
Helmut Hauser<br />
Kris Hauser<br />
Nicolas Hautière<br />
Nick Hawes<br />
Yoshikazu Hayakawa<br />
Mitsuhiro Hayashibe<br />
Jean-Bernard Hayet<br />
Galen Clark Haynes<br />
Vincent Hayward<br />
Klas Hedenberg<br />
Markus Hehn<br />
Hordur K Heidarsson<br />
Björn Hein<br />
Milton Heinen<br />
Achim Hekler<br />
Patrick Helmer<br />
Gustaf Hendeby<br />
Gregory Henderson<br />
Thomas C. Henderson<br />
Philipp Hennig<br />
Peter Henry<br />
Damitha Chandana Herath<br />
Evan Herbst<br />
Just Herder<br />
Andrei Herdt<br />
Bruno Herisse<br />
Sorin Herle<br />
Benoît Herman<br />
Alejandro Hernandez Arieta<br />
Daniel Hernández-Sosa<br />
Albert Hernansanz<br />
Micha Hersch<br />
Joachim Hertzberg<br />
Joel Hesch<br />
Tarcisio Hess-Coelho<br />
Matthew Scott Hester<br />
Todd Hester<br />
Clint Heyer<br />
Mitsuru Higashimori<br />
Masaru Higuchi<br />
Ulrich Hillenbrand<br />
Nick Hillier<br />
Volker Hilsenstein<br />
Salim Hima<br />
Lindsey Hines<br />
James Hing<br />
Tatsuya Hirahara<br />
Hiroaki Hirai<br />
Yasuharu Hirasawa<br />
Yasuhisa Hirata<br />
Heiko Hirschmüller<br />
Gerd Hirzinger<br />
Handa Hisashi<br />
Sean Ho<br />
Van Ho<br />
John Hoare<br />
Warren Hoburg<br />
Nico Hochgeschwender<br />
Antony Hodgson<br />
Michael Hofbaur<br />
Nicholas Hoff<br />
Alwin Hoffmann<br />
Gabriel Hoffmann<br />
Heiko Hoffmann<br />
Andrew Hogue<br />
Shinji Hokamoto<br />
Geoffrey Hollinger<br />
Patrick Hollis<br />
Rebecca Hollmann<br />
Dirk Holz<br />
Masaaki Honda<br />
Man Bok Hong<br />
Carl D. Hoover<br />
Ben Horan<br />
Toshio Hori<br />
Armin Hornung<br />
Satoshi Hoshino<br />
Koh Hosoda<br />
Shigeyuki Hosoe<br />
Feili Hou<br />
Jonathan How<br />
Ayanna Howard<br />
Matthew Howard<br />
Tom Howard<br />
Robert D. Howe<br />
Stefan Hrabar<br />
Kaijen Hsiao<br />
M. Ani Hsieh<br />
David Hsu<br />
John Hsu<br />
Bo Hu<br />
Chao Hu<br />
Chunhua Hu<br />
Guoqiang Hu<br />
Hongsheng Hu<br />
Jwu-Sheng Hu<br />
Tianjiang Hu<br />
Zhencheng Hu<br />
Changchun Hua<br />
Ana Huaman<br />
Albert S. Huang
Cheng-Ming Huang<br />
Chih-Fang Huang<br />
Haibo Huang<br />
Han-Pang Huang<br />
Haomiao Huang<br />
Jian Huang<br />
Jian Huang<br />
Ke Huang<br />
Qiang Huang<br />
Ruining Huang<br />
Shoudong Huang<br />
Shuguang Huang<br />
Shuo Huang<br />
Tzu-Hao Huang<br />
Yazhou Huang<br />
Daniel Huber<br />
Manfred Huber<br />
Christian Hubicki<br />
Kai Huebner<br />
Joel C. Huegel<br />
Zhao Huihua<br />
Jan Huissoon<br />
Thomas Hulin<br />
James Sean Humbert<br />
Terry Huntsberger<br />
Jonathan Hurst<br />
Ammar Husain<br />
Islam Hussein<br />
Manfred Husty<br />
Seth Hutchinson<br />
Gilgueng Hwang<br />
Heeseon Hwang<br />
Jung-Hoon Hwang<br />
Kazuyuki Hyodo<br />
Sang-Ho Hyon<br />
Baro Hyun<br />
Heikki Sakari Hyyti<br />
Karl Iagnemma<br />
Ion Iancu<br />
Akihiko Ichikawa<br />
Romain Iehl<br />
Siohoi Leng<br />
Fumiya Iida<br />
Kojiro Iizuka<br />
Atsutoshi Ikeda<br />
Yoshito Ikemata<br />
Shuhei Ikemoto<br />
Yusuke Ikemoto<br />
Viorela Ila<br />
Boyko Iliev<br />
Jarmo Ilonen<br />
Atsushi Imiya<br />
Gokhan Ince<br />
Giovanni Indiveri<br />
Marina Indri<br />
Francois Felix Ingrand<br />
Kenji Inoue<br />
Takahiro Inoue<br />
Luca Iocchi<br />
Iulian Iordachita<br />
Ioannis Iossifidis<br />
Masatsugu Iribe<br />
Josep Isern-González<br />
Genya Ishigami<br />
Akio Ishiguro<br />
Hiroyuki Ishii<br />
Kazuo Ishii<br />
Jun Ishikawa<br />
Volkan Isler<br />
Daigoro Isobe<br />
Kazuyuki Ito<br />
Keiichiro Ito<br />
Tomotaka Itoh<br />
Iñaki Iturrate<br />
Serena Ivaldi<br />
Ioan Alexandru Ivan<br />
Yoshio Iwai<br />
Masami Iwase<br />
Yumi Iwashita<br />
Hiroyasu Iwata<br />
Yasushi Iwatani<br />
Martin Jagersand<br />
Leonard Jaillet<br />
Advait Jain<br />
Dominik Jain<br />
Ravi Kant Jain<br />
Michael Jakuba<br />
Doug L. James<br />
Rodrigo Jamisola<br />
Mo Jamshidi<br />
Farrokh Janabi-Sharifi<br />
Balazs Janko<br />
Pakpong Jantapremjit<br />
Alberto Jardon Huete<br />
Nathanaël Jarrassé<br />
Luc Jaulin<br />
C.V. Jawahar<br />
Graylin Jay<br />
Suhada Jayasuriya<br />
Chandimal Jayawardena<br />
Jagadeesan Jayender<br />
Changsoo Je<br />
Michael Jenkin<br />
Leif P. Jentoft<br />
Jeong hwan Jeon<br />
Jay Jeong<br />
Mun-Ho Jeong<br />
Seonghee Jeong<br />
Sabina Jeschke<br />
Nikolay Jetchev<br />
Sang Hoon Ji<br />
Zhengping Ji<br />
Yan-Bin Jia<br />
Zhaoyin Jia<br />
Guangying Jiang<br />
Shu Jiang<br />
Xin Jiang<br />
Yun Jiang<br />
Jiyong Jin<br />
Sungho Jo<br />
Rolf Johansson<br />
Aaron Johnson<br />
Andrew Johnson<br />
Collin Johnson<br />
Miles Johnson<br />
Matthew Johnson-Roberson<br />
Magnus Johnsson<br />
Dominik Joho<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xiii–<br />
Cyril Joly<br />
Edward Gil Jones<br />
Ben Jonker<br />
Brett Jordan<br />
Ajay Joshi<br />
Ming-Shaung Ju<br />
Zhaojie Ju<br />
Simon Justin Julier<br />
Hong-Gul Jun<br />
Boyoon Jung<br />
Changbae Jung<br />
Min Yang Jung<br />
Yoon C. Jung<br />
Michael Kaess<br />
Satoshi Kagami<br />
Lueder Alexander Kahrs<br />
Shuuji Kajita<br />
Mrinal Kalakrishnan<br />
Vinutha Kallem<br />
Pasi Johannes Kallio<br />
Marcelo Kallmann<br />
Tetsushi Kamegawa<br />
Mitsuhiro Kamezaki<br />
Akiya Kamimura<br />
Hiroshi Kaminaga<br />
Sören Kammel<br />
Roman Kamnik<br />
Stratis Kanarachos<br />
Takefumi Kanda<br />
Fumio Kanehiro<br />
Kenji Kaneko<br />
Makoto Kaneko<br />
Yeonsik Kang<br />
Balajee Kannan<br />
Dimitrios Kanoulas<br />
George Kantor<br />
Imin Kao<br />
Ankur Kapoor<br />
Sertac Karaman<br />
Michael Karg<br />
Nikhil Karnad<br />
Amir Karniel<br />
Joanna Karpinska<br />
George Karras<br />
Sisir Karumanchi<br />
Dusko Katic<br />
Naomi Kato<br />
Max Katsev<br />
Pantelis Katsiaris<br />
Dov Katz<br />
Lydia Kavraki<br />
Hideki Kawahara<br />
Tatsuya Kawahara<br />
Atsushi Kawakami<br />
Kazuya Kawamura<br />
Sadao Kawamura<br />
Hiroshi Kawano<br />
Hiroshi Kawasaki<br />
Hiroaki Kawashima<br />
Kenji Kawashima<br />
Hirohiko Kawata<br />
Kentaro Kayama<br />
Richard Kelley<br />
Jonathan Kelly<br />
Anssi Juhani Kemppainen<br />
Farid Kendoul<br />
Benjamin Kent<br />
Lychek Keo<br />
Mehrdad R. Kermani<br />
Serge Kernbach<br />
Kristian Kersting<br />
Samuel B. Kesner<br />
Saad Khan<br />
Piyush Khandelwal<br />
Abderrahmane Kheddar<br />
Kazuo Kiguchi<br />
Takehito Kikuchi<br />
Ryo Kikuuwe<br />
Byeong-Sang Kim<br />
Byoung-Ho Kim<br />
Chang Young Kim<br />
ChangHwan Kim<br />
Chanki Kim<br />
Chyon Hae Kim<br />
Doik Kim<br />
Dong Hwan Kim<br />
Donghyeon Kim<br />
H. Jin Kim<br />
Hee-Young Kim<br />
Hongho Kim<br />
Jinwook Kim<br />
Jiwoong Kim<br />
Jong-Hwan Kim<br />
Jong-Wook Kim<br />
Jonghoek Kim<br />
Jongwon Kim<br />
Joo H. Kim<br />
Jung Kim<br />
Jung-Yup Kim<br />
Junggon Kim<br />
Keehoon Kim<br />
Kyung-Joong Kim<br />
MinJun Kim<br />
Sangbae Kim<br />
Whee Kuk Kim<br />
Yongtae Kim<br />
Young J. Kim<br />
Youngmoo Kim<br />
Shinichi Kimura<br />
Chih-Hung King<br />
H. Hawkeye King<br />
Hitoshi Kino<br />
James Kinsey<br />
Tetsuya Kinugasa<br />
Zsolt Kira<br />
Frank Kirchner<br />
Nathan Kirchner<br />
Alexis Kirke<br />
Nobuyuki Kita<br />
Yasuyo Kita<br />
Bernd Kitt<br />
Gregor Klancar<br />
Ulrich Klank<br />
Alexander Kleiner<br />
Andrew Klesh<br />
Denis Klimentjew<br />
Stephen Kloder<br />
Markus Klotzbuecher
Boris Kluge<br />
Pawel Kmiotek<br />
Laurent Kneip<br />
Ross A Knepper<br />
Heather Knight<br />
Alois Knoll<br />
George Knopf<br />
Jeremie Knuesel<br />
Kheng Lee Koay<br />
Etsuko Kobayashi<br />
Futoshi Kobayashi<br />
Hiroyuki Kobayashi<br />
Taizo Kobayashi<br />
Yo Kobayashi<br />
Yuichi Kobayashi<br />
Jens Kober<br />
Marin Kobilarov<br />
Sarath Kodagoda<br />
Kenri Kodaka<br />
Masanao Koeda<br />
James Koeneman<br />
Nathan Koenig<br />
Koichi Koganezawa<br />
Kiminao Kogiso<br />
Norihiro Koizumi<br />
Nigel Kojimoto<br />
Ewa Kolakowska<br />
Maxim Kolesnikov<br />
Thomas Kollar<br />
Andreas Kolling<br />
J. Zico Kolter<br />
Erik Komendera<br />
Haldun Komsuoglu<br />
Taku Komura<br />
Takashi Komuro<br />
Kazuyuki Kon<br />
Toshiyuki Kondo<br />
Fanyu Kong<br />
Xianwen Kong<br />
Rainer Konietschke<br />
Atsushi Konno<br />
Kurt Konolige<br />
Michail Kontitsis<br />
Ioannis Kontolatis<br />
Masashi Konyo<br />
Ig Mo Koo<br />
Ja Choon Koo<br />
Gert Kootstra<br />
Petar Kormushev<br />
G. Ayorkor Korsah<br />
David Kortenkamp<br />
Gabor Kosa<br />
Hatice Kose-Bagci<br />
Jana Kosecka<br />
Yoshihiko Koseki<br />
D. I. Kosmopoulos<br />
Kazuhiro Kosuge<br />
Shinya Kotosaka<br />
Navinda Kottege<br />
Mirko Kovac<br />
Gerhard Kraetzschmar<br />
Dirk Kraft<br />
Florian Kraft<br />
Danica Kragic<br />
Michael Krainin<br />
Bradley Kratochvil<br />
Frédéric Kratz<br />
Hermano Igo Krebs<br />
Philipp Kremer<br />
Ingo Kresse<br />
Henrik Kretzschmar<br />
Simon Kriegel<br />
Madhava Krishna<br />
Torsten Kroeger<br />
Oliver Kroemer<br />
Athanasios Krontiris<br />
Kristian Kroschel<br />
Volker Krueger<br />
Alexandre Krupa<br />
Sebastien Krut<br />
Maarja Kruusmaa<br />
Jiun-Yih Kuan<br />
Wilfried Kubinger<br />
Takashi Kubota<br />
Daniel Kubus<br />
Shunsuke Kudoh<br />
Bernhard Kuebler<br />
Rainer Kuemmerle<br />
James Kuffner<br />
Benjamin Kuipers<br />
Dana Kulic<br />
Ganesh Kumar<br />
Manish Kumar<br />
Prasanth Kumar<br />
Rajesh Kumar<br />
Vijay Kumar<br />
Yasuharu Kunii<br />
Yoshinori Kuno<br />
Tobias Kunz<br />
I Han Kuo<br />
Victor Kuo<br />
Kentarou Kurashige<br />
Ryo Kurazume<br />
Masamitsu Kurisu<br />
Yuichi Kurita<br />
Yoji Kuroda<br />
Haruhisa Kurokawa<br />
Yoshiaki Kuwata<br />
Dong-Soo Kwon<br />
Hyukseong Kwon<br />
Hyunki Kwon<br />
Ohung Kwon<br />
SangJoo Kwon<br />
Yong Moo Kwon<br />
Young-Sik Kwon<br />
Kolja Kühnlenz<br />
Ville Kyrki<br />
Erik Kyrkjebø<br />
Bruno L'Esperance<br />
Hung La<br />
Janne Laaksonen<br />
Ouiddad Labbani-Igbida<br />
Francisco Lacerda<br />
Bakir Lacevic<br />
Simon Lacroix<br />
Matteo Laffranchi<br />
Chin-Lun Lai<br />
Kevin Lai<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xiv–<br />
Yongjun Lai<br />
Sara Lal<br />
Thierry Laliberte<br />
Jean-Francois Lalonde<br />
Chi Pang Lam<br />
Raymond Lam<br />
Olivier Lambercy<br />
Florent Lamiraux<br />
Pierre Lamon<br />
Roberto Lampariello<br />
Christoph H. Lampert<br />
Chao-Chieh Lan<br />
David Lane<br />
Christian Lang<br />
Jochen Lang<br />
Tobias Lang<br />
Friedrich Lange<br />
Sascha Lange<br />
Jacob Langelaan<br />
Jørgen Larsen<br />
Amy Larson<br />
Cecilia Laschi<br />
Boris Lau<br />
Darwin Lau<br />
Manfred Lau<br />
Nuno Lau<br />
Tak Kit Lau<br />
Tim Laue<br />
Martin Lauer<br />
Christian Laugier<br />
Steven M. LaValle<br />
Brian Lawson<br />
Maria Teresa Lazaro<br />
Olivier Le Meur<br />
Jerome Le Ny<br />
Brigitte Le-Pévédic<br />
Chil-Woo Lee<br />
Daewon Lee<br />
Daniel D. Lee<br />
Dongheui Lee<br />
Doo Yong Lee<br />
Hyunglae Lee<br />
Hyunsuk Lee<br />
Jae Hoon Lee<br />
Jaewoo Lee<br />
Ji-Yeong Lee<br />
Jihong Lee<br />
Jonghyun Lee<br />
Ju-Jang Lee<br />
Juhyun Lee<br />
Kiju Lee<br />
Kyoung Mu Lee<br />
Loo Hay Lee<br />
Mark Lee<br />
Sanghoon Lee<br />
Sangyoon Lee<br />
Se-Jin Lee<br />
Seongsoo Lee<br />
Seung-Mok Lee<br />
Sukhan Lee<br />
Wen-Yo Lee<br />
Woosub Lee<br />
Yun-Jung Lee<br />
Benjamin Lefaudeux<br />
Dirk Lefeber<br />
Stéphanie Lefèvre<br />
Jie Lei<br />
Antonio C. Leite<br />
Sylvie Lelandais<br />
Roland Lenain<br />
Sebastien Lengagne<br />
Scott Lenser<br />
Tommaso Lenzi<br />
John Leonard<br />
Simon Leonard<br />
Matteo Leonetti<br />
Vincent Lepetit<br />
Ilkka M. Leppänen<br />
Frederic Lerasle<br />
Charles Lesire<br />
Adrian Leu<br />
Keith Yu Kit Leung<br />
Christopher Lewis<br />
Jeremy Lewis<br />
M. Anthony Lewis<br />
Michael Lewis<br />
Christian Lexcellent<br />
Sigrid Leyendecker<br />
Bin Li<br />
Changchun Li<br />
Cheng Gang Li<br />
Guangyong Li<br />
Haizhou Li<br />
Hui Li<br />
Jingliang Li<br />
Min Li<br />
Ming Li<br />
Peng Li<br />
Qinchuan Li<br />
Ruonan Li<br />
Shigang Li<br />
Tsai-Yen Li<br />
Xiang Li<br />
Xingyan Li<br />
Yangming Li<br />
Yi Li<br />
Yuan Yuan Li<br />
Yuanping Li<br />
Yuwen Li<br />
Zhibin Li<br />
Zhibin Li<br />
Zhijun Li<br />
Qiaokang Liang<br />
Xinwu Liang<br />
Yao Liang<br />
Hongen Liao<br />
Minas Liarokapis<br />
Somchaya Liemhetcharat<br />
Jyh-Ming Lien<br />
Neal Y Lii<br />
Pål Liljebäck<br />
Chee Wang Lim<br />
Jongwoo Lim<br />
Kyeong Bin Lim<br />
Sejoon Lim<br />
Ser-Nam Lim<br />
Wenbin Lim<br />
Bor-Shen Lin
Chia-How Lin<br />
Kuen-Han Lin<br />
Lanny Lin<br />
Pei-Chun Lin<br />
Shih Chi Lin<br />
Wei Lin<br />
Yu Lin<br />
Yucong Lin<br />
Zhiyun Lin<br />
Zhuohua Lin<br />
Magnus Linderoth<br />
Laura Lindzey<br />
Qiang Ling<br />
Kai Lingemann<br />
Vittorio Lippi<br />
Vincenzo Lippiello<br />
Michael Lipsett<br />
Bingbing Liu<br />
Chao Liu<br />
Chenggang Liu<br />
Fangfang Liu<br />
Guanfeng Liu<br />
Guangjun Liu<br />
Hanwei Liu<br />
Hong Liu<br />
Hong Liu<br />
Hongbin Liu<br />
Honghai Liu<br />
Jie Liu<br />
Jindong Liu<br />
Jing Liu<br />
Jing-Sin Liu<br />
Jinguo Liu<br />
Karen Liu<br />
Lianqing Liu<br />
Minjie Liu<br />
Rongqiang Liu<br />
Tao Liu<br />
Tong Liu<br />
Xinyu Liu<br />
Yang Liu<br />
Yang Liu<br />
Yong Liu<br />
Yugang Liu<br />
Fernando Lizarralde<br />
Xavier Lladó<br />
Jorge Lobo<br />
Vo-Gia Loc<br />
Dario Lodi Rizzini<br />
Sauro Longhi<br />
Rosemarijn Looije<br />
Manuel Lopes<br />
Natalia Lopez Celani<br />
Carlos Al. López-Franco<br />
Ismael Lopez-Juarez<br />
Rigoberto Lopez-Padilla<br />
Antonio Loria<br />
Yunjiang Lou<br />
Miguel Lourenço<br />
Bryan Kian Hsiang Low<br />
K. H. Low<br />
Thomas Low<br />
Robert Lowe<br />
Rogelio Lozano<br />
Tomas Lozano-Perez<br />
Qi Lu<br />
Tien-Fu Lu<br />
Yan Lu<br />
Yanyan Lu<br />
Yi Lu<br />
Tomasz Lubecki<br />
Matthias Luber<br />
Carlos Luck<br />
Brandon Luders<br />
Greg R. Luecke<br />
Ching-Hu Luh<br />
Wen Lik Dennis Lui<br />
Henk Luinge<br />
Christopher Lum<br />
Ryan Luna<br />
Jun Luo<br />
Ren Luo<br />
Zhiqiang Luo<br />
Sergei Lupashin<br />
Alexis Lussier Desbiens<br />
Goran Lynch<br />
Kevin Lynch<br />
Daniel Lyons<br />
Ingo Lütkebohle<br />
Martin Lösch<br />
Hongbo Ma<br />
Jeremy Ma<br />
Shugen Ma<br />
Heiko Maass<br />
Bruce MacDonald<br />
D. Guimarães Macharet<br />
Doug MacKenzie<br />
Robert A. MacLachlan<br />
David MacNair<br />
Yasushi Mae<br />
Guilherme Jorge Maeda<br />
Shingo Maeda<br />
Andreas Maeder<br />
Shoichi Maeyama<br />
Gianantonio Magnani<br />
Martin Magnusson<br />
Aditya Mahadevan<br />
Ian Mahon<br />
Robert Mahony<br />
Mohammad Mahoor<br />
Mohsen Mahvash<br />
Daniel Maier<br />
Jim Mainprice<br />
Perla Maiolino<br />
Elmar Mair<br />
Jeremy Maitin-Shepard<br />
Andras Majdik<br />
Carmel Majidi<br />
Toshihiro Maki<br />
Masaaki Makikawa<br />
Satoshi Makita<br />
Michail Makrodimitris<br />
Alexis Maldonado<br />
Anthony Mallet<br />
Angelos Mallios<br />
Kenneth Mallory<br />
Abed Malti<br />
Noureddine Manamanni<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xv–<br />
Kasra Manavi<br />
Ian Manchester<br />
Roberto Manduchi<br />
Olivier Mangin<br />
Kalyan Mankala<br />
Justin Mann<br />
Nicolas Mansard<br />
Zhi-Hong Mao<br />
Panos Marantos<br />
James Marble<br />
Eric Marchand<br />
Andrew Marchese<br />
Luca Marchetti<br />
Lorenzo Marconi<br />
Rebeca Marfil<br />
Luis Felipe Marin-Urias<br />
Dimitri Marinakis<br />
Alessandro Marino<br />
Gian Luca Mariottini<br />
Ali Marjovi<br />
Ivan Markovic<br />
Lino Marques<br />
Steven Marra<br />
Jose Luis Marroquin<br />
Joshua A. Marshall<br />
Sylvain Martel<br />
Anne Martin<br />
Patrick Martin<br />
Steven Colin Martin<br />
Ester Martinez<br />
Oscar Martinez Mozos<br />
Harold R. Martinez Salazar<br />
Ruben Martinez-Cantin<br />
J.R. Martínez-de Dios<br />
Simone Martini<br />
Alcherio Martinoli<br />
Eric Martinson<br />
Zoltan-Csaba Marton<br />
Noriaki Maru<br />
Hisataka Maruyama<br />
Ignacio Mas<br />
Ken Masamune<br />
Lorenzo Masia<br />
Julian Mason<br />
Ahmad A. Masoud<br />
Hiroyuki Masuta<br />
Maja Mataric<br />
Bogdan Matei<br />
T, William Mather<br />
George Mathews<br />
Fernando Matia<br />
Takamitsu Matsubara<br />
Osamu Matsumoto<br />
Yoshio Matsumoto<br />
Takayuki Matsuno<br />
Daisuke Matsuura<br />
Kenichiro Matsuzaki<br />
Dennis Matthews<br />
Larry Matthies<br />
Giuliana Mattiazzo<br />
Rosana Matuk Herrera<br />
Saburo Matunaga<br />
Christophe Maufroy<br />
Francesco Maurelli<br />
Iñaki Maurtua<br />
S. Mohammad Mavadati<br />
Constantinos Mavroidis<br />
Bruce Maxwell<br />
Stephen Maybank<br />
Jerome Maye<br />
Christoph Mayer<br />
Walterio Mayol<br />
Anirban Mazumdar<br />
Barbara Mazzarino<br />
Barbara Mazzolai<br />
Stefano Mazzoleni<br />
Lachlan McCalman<br />
Chris McCarthy<br />
Samuel McCarthy<br />
Erik McDermott<br />
Christopher McFarland<br />
Martin McGinnity<br />
Michael McHenry<br />
Gerard McKee<br />
David McKinnon<br />
T.W. McLain<br />
James McLurkin<br />
John J. McPhee<br />
Timothy McPherson<br />
Rafik Mebarki<br />
Lashika Medagoda<br />
Gustavo Medrano-Cerda<br />
Debbie Meduna<br />
Sanford Meek<br />
Wim Meeussen<br />
David Paul Meger<br />
Malika Meghjani<br />
Syed Bilal Mehdi<br />
Mehran Mehrandezh<br />
Siddhartha Mehta<br />
Christopher Mei<br />
Luis Mejias<br />
Claudio Melchiorri<br />
Daniel Mellinger<br />
Rochelle Mellish<br />
Arianna Menciassi<br />
Caio Mendes<br />
Ricardo F. Mendoza Garcia<br />
Emanuele Menegatti<br />
Felipe Meneguzzi<br />
Paulo Menezes<br />
Yan Meng<br />
Tekin Mericli<br />
Luis Merino<br />
Jean-Pierre Merlet<br />
Gregory Mermoud<br />
Torsten Merz<br />
Elena Messina<br />
Uwe Mettin<br />
Daniel Meyer-Delius<br />
Youcef Mezouar<br />
Krzysztof Mianowski<br />
Alain Micaelli<br />
Nathan Michael<br />
Andreas Michaels<br />
Francois Michaud<br />
Fabien Michel<br />
Bernard Michini
Davide Migliore<br />
Matjaž Mihelj<br />
David Mikesell<br />
Michael J Milford<br />
Isaac Miller<br />
James K. Mills<br />
Bojan Milosevic<br />
Adam Milstein<br />
Dejan Milutinovic<br />
Hyeun Jeong Min<br />
Mamoru Minami<br />
Aiguo Ming<br />
Nicoleta Minoiu Enache<br />
Mark Minor<br />
Sylvain Miossec<br />
Luiz Gustavo Mirisola<br />
Faraz Mirzaei<br />
Marcell Missura<br />
Michael Mistry<br />
Seiichi Mita<br />
Atsushi Mitani<br />
Kazuhisa Mitobe<br />
Nikos Mitsou<br />
Mamoru Mitsuishi<br />
Noriaki Mitsunaga<br />
Jun Miura<br />
Kanako Miura<br />
Toyomi Miyagawa<br />
Seiichi Miyakoshi<br />
Shuhei Miyashita<br />
Takahiro Miyashita<br />
Mehrdad Moallem<br />
Hiromi Mochiyama<br />
Joseph Modayil<br />
Birgit Moeller<br />
Richard Phillip Mohamed<br />
Mahmood Mohammadi<br />
Samer Mohammed<br />
Vishwanathan Mohan<br />
Kamran Mohseni<br />
Shahram Mohseni-vahed<br />
Rezia Molfino<br />
Mark Moll<br />
Vito Monaco<br />
Francesco Mondada<br />
Raúl Monroy<br />
Luis Montano<br />
Luis Montesano<br />
Eduardo Montijano<br />
Chang-bae Moon<br />
Hyungpil Moon<br />
Jae-Sung Moon<br />
Brian Moore<br />
Joseph Moore<br />
Richard J.D. Moore<br />
Frank Moosmann<br />
Andres Mora<br />
Hadi Moradi<br />
Antonio Morales<br />
Eduardo Morales<br />
Jesús Morales<br />
Marco Morales<br />
Luis Yoichi Morales Saiki<br />
Fabio Morbidi<br />
Guillaume Morel<br />
Yannick Morel<br />
Vassilios Morellas<br />
Jan Morén<br />
Juan Camilo Moreno<br />
Luis Moreno<br />
Francesc Moreno-Noguer<br />
Kristi Morgansen<br />
Takeshi Mori<br />
Taketoshi Mori<br />
Yasushi Morikawa<br />
Mitsuharu Morisawa<br />
Takuro Moriyama<br />
Nicholas Morizio<br />
Nicholas Morozovsky<br />
Daniel D. Morris<br />
Peter Morton<br />
Ryan Morton<br />
Mark Moseley<br />
Alejandro R. Mosteo<br />
El Mustapha Mouaddib<br />
Tetsuya Mouri<br />
Anastasios Mourikis<br />
Annan Mozeika<br />
Andreas Mueller<br />
Joerg Mueller<br />
Katharina Muelling<br />
Jonathan Mugan<br />
Ranjan Mukherjee<br />
Rudranarayan Mukherjee<br />
Albert Mukovskiy<br />
Enzo Mumolo<br />
Marko Munih<br />
Luis Miguel Munoz<br />
Victor Muñoz<br />
Riccardo Muradore<br />
Kenichi Murakami<br />
Kouji Murakami<br />
Toshiyuki Murakami<br />
Ana Cristina Murillo<br />
Todd Murphey<br />
Chris Murphy<br />
David Murray<br />
Richard Murray<br />
Rafael Murrieta-Cid<br />
Giovanni Muscato<br />
Shabbir K. Mustafa<br />
Bilge Mutlu<br />
George Mylonas<br />
Holger Mönnich<br />
Thomas Mörwald<br />
S. Nadubettu Yadukumar<br />
Hajime Nagahara<br />
Kiyoshi Nagai<br />
Takayuki Nagai<br />
Yukie Nagai<br />
Hiroyuki Nagamatsu<br />
Kenji Nagaoka<br />
Umashankar Nagarajan<br />
Kazuyuki Nagata<br />
Keiji Nagatani<br />
Radhika Nagpal<br />
Prathap Nair<br />
Michael D. Naish<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xvi–<br />
Eiichi Naito<br />
Zoran Najdovski<br />
Homayoun Najjaran<br />
Kazuhiro Nakadai<br />
Ryu Nakadate<br />
Masahiro Nakajima<br />
Akio Nakamura<br />
Ryoichi Nakamura<br />
Taro Nakamura<br />
Yutaka Nakamura<br />
Hiroaki Nakanishi<br />
Hiroki Nakanishi<br />
Jun Nakanishi<br />
Yuto Nakanishi<br />
Akira Nakashima<br />
Atsushi Nakazawa<br />
Alireza Nakhaei<br />
Roberto Naldi<br />
Akio Namiki<br />
Mehrzad Namvar<br />
Thrishantha Nanayakkara<br />
Jun Nango<br />
Kostas Nanos<br />
Ashley Napier<br />
Nils Napp<br />
Kenichi Narioka<br />
Oleg Naroditsky<br />
David Naso<br />
Sameh Nassar<br />
Ciro Natale<br />
Lorenzo Natale<br />
Shuvra Nath<br />
Robert Nawrocki<br />
Shahriar Negahdaripour<br />
José Neira<br />
Goldie Nejat<br />
Bradley J. Nelson<br />
Carl Nelson<br />
Bojan Nemec<br />
Dragomir Nenchev<br />
Esha Nerurkar<br />
Issa Nesnas<br />
Stephen Nestinger<br />
Eric Nettleton<br />
Jeremiah Neubert<br />
Peter Neuhaus<br />
Bradford Neuman<br />
Jan Neumann<br />
Teck Chew Ng<br />
Victor Ng-Thow-Hing<br />
Trung Dung Ngo<br />
Hai Nguyen<br />
Duy Nguyen-Tuong<br />
Kai Ni<br />
Marta Niccolini<br />
Monica Nicolescu<br />
Gunter Niemeyer<br />
Juan Nieto<br />
Carlos Nieto-Granda<br />
Misato Nihei<br />
Ryuma Niiyama<br />
Janosch Nikolic<br />
Nattee Niparnan<br />
Shun Nishide<br />
Yasutaka Nishioka<br />
Koichi Nishiwaki<br />
Ilana Nisky<br />
Verena Nitsch<br />
Tomoyuki Noda<br />
David Noelle<br />
Hiroshi Noguchi<br />
Masahiro Nohmi<br />
Scott Nokleby<br />
Narges Noori<br />
Toshiro Noritsugu<br />
Navid Nourani Vatani<br />
Illah Reza Nourbakhsh<br />
Walter Nowak<br />
Cameron Nowzari<br />
Shahin Nudehi<br />
Jesus Nuevo<br />
Urbano Nunes<br />
Pedro Núñez Trujillo<br />
Emmanuel Nuño<br />
Stephen Nuske<br />
Rehan O'Grady<br />
Keith O'Hara<br />
Jason O'Kane<br />
Damien O'Rourke<br />
Oliver Obst<br />
Tobias Obst<br />
Manuel Ocaña<br />
Mitsushige Oda<br />
Lael Odhner<br />
Jean-Marc Odobez<br />
Denny Oetomo<br />
Tsukasa Ogasawara<br />
Tetsuya Ogata<br />
Tetsuji Ogawa<br />
Koichi Ogawara<br />
Dimitri Ognibene<br />
Petter Ogren<br />
Paul Y. Oh<br />
Se-Young Oh<br />
Sehoon Oh<br />
Songhwai Oh<br />
Kenichi Ohara<br />
Kazunori Ohno<br />
Apollon Oikonomopoulos<br />
Lauro Ojeda<br />
Kei Okada<br />
Masafumi Okada<br />
Jun Okamoto<br />
Jun Okamoto Junior<br />
Allison M. Okamura<br />
Hiroshi G. Okuno<br />
Andrej Olenšek<br />
Nejat Olgac<br />
Miguel A. Olivares-Mendez<br />
Paulo Oliveira<br />
Tiago Roux Oliveira<br />
Gabriel A. Oliver<br />
Anibal Ollero<br />
Daniel Olmeda<br />
Bjorn Olofsson<br />
Clark Olson<br />
Edwin Olson<br />
Tomas Olsson
Toru Omata<br />
Cagdas Denizel Onal<br />
Ahmet Onat<br />
Hiromu Onda<br />
Masahiro Ono<br />
Yizhar Or<br />
David Orin<br />
Agustin A. Ortega Jimenez<br />
Alberto Ortiz<br />
Daniel Ortiz Morales<br />
Tobias Ortmaier<br />
Fernando Osório<br />
James Ostrowski<br />
Koichi Osuka<br />
Jun Ota<br />
Masatsugu Otsuki<br />
Christian Ott<br />
Lionel Ott<br />
Michael W. Otte<br />
Mark Ottensmeyer<br />
Zhicai Ou<br />
Puren Ouyang<br />
Dai Owaki<br />
Jason Owens<br />
Eimei Oyama<br />
John O. Oyekan<br />
Ryuta Ozawa<br />
Onur Ozcan<br />
Ali Gürcan Özkil<br />
Erhan Oztop<br />
Taskin Padir<br />
Vincent Padois<br />
Ali Paikan<br />
Nicholas Paine<br />
Ana Lucia Pais<br />
Jordi Palacin<br />
Gianluca Palli<br />
Rainer Palm<br />
Luther R. Palmer III<br />
Matteo Claudio Palpacelli<br />
Jia Pan<br />
Qinxue Pan<br />
Ya-Jun Pan<br />
Dimitra Panagou<br />
Amit Kumar Pandey<br />
Shuo Pang<br />
Dejan Pangercic<br />
Giorgio Panin<br />
Athanasia Panousopoulou<br />
Julien Pansiot<br />
Caroline Pantofaru<br />
Evangelos Papadopoulos<br />
Georgios Papadopoulos<br />
Nikos Papanikolopoulos<br />
George J. Pappas<br />
Byungjae Park<br />
Edward J. Park<br />
Ga-Ram Park<br />
Hyongju Park<br />
Hyungmin Park<br />
Jae Byung Park<br />
Jaeheung Park<br />
Jong Jin Park<br />
Sangdeok Park<br />
Shinsuk Park<br />
Soonyong Park<br />
Sukho Park<br />
Sung-Kee Park<br />
Wooram Park<br />
Yong-Jai Park<br />
Youngjin Park<br />
Aaron Parness<br />
Vicente Parra Vega<br />
Martin Peter Parsley<br />
Federica Pascucci<br />
Anatol Pashkevich<br />
Carolina Passenberg<br />
Peter Pastor<br />
Maria Pateraki<br />
Kaustubh Pathak<br />
Volkan Patoglu<br />
Alexandru Patriciu<br />
Ugo Pattacini<br />
Alexander Patterson IV<br />
Rohan Paul<br />
Frederick Pauling<br />
Liam Paull<br />
Dietrich Paulus<br />
Marco Pavone<br />
Jonathan Paxman<br />
Shahram Payandeh<br />
Lina María Paz<br />
Roger Pearce<br />
Federico Pecora<br />
Luis Pedraza<br />
Angelika Peer<br />
Claude Pégard<br />
Yuanteng Pei<br />
Paulo Peixoto<br />
Pietro Peliti<br />
Ron Pelrine<br />
Virginia Pensabene<br />
Ivan Penskiy<br />
Romain Pepy<br />
Guilherme Pereira<br />
Jose Nuno Pereira<br />
Douglas Perrin<br />
Nicolas Yves Perrin<br />
Francesco Pescarmona<br />
Gustavo Pessin<br />
Steven Peters<br />
Henrik Gordon Petersen<br />
Klaus Petersen<br />
Cammy Peterson<br />
Kevin Peterson<br />
Lars Petersson<br />
Antoine Petit<br />
Florian Petit<br />
Clément Pêtrès<br />
Tadej Petric<br />
Ron Petrick<br />
Anna Petrovskaya<br />
Kathrin Eva Peyer<br />
Zachary Pezzementi<br />
Patrick Pfaff<br />
Friedrich Pfeiffer<br />
Frank Pfenning<br />
Max Pfingsthorn<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xvii–<br />
Martin Pfurner<br />
Minh Tu Pham<br />
Louis Phee<br />
Sylvie Philipp-Foliguet<br />
Roland Philippsen<br />
Johan Philips<br />
Mike Phillips<br />
Thanathorn Phoka<br />
Emmanuel Piat<br />
Stefano Piazza<br />
Brennand Pierce<br />
Vinay Pilania<br />
Luciano Pimenta<br />
Benny Ping Lai Lo<br />
Pedro Pinies<br />
Charles Pinto<br />
Peam Pipattanasomporn<br />
Tony Pipe<br />
Charles Pippin<br />
Gabriel Pires<br />
Hamed Pirsiavash<br />
Antonio Pistillo<br />
Benjamin Pitzer<br />
Mihail Pivtoraiko<br />
Oscar Pizarro<br />
Christian Plagemann<br />
Erion Plaku<br />
Robert Platt<br />
Patrick Plonski<br />
Frederic Plumet<br />
Paul G. Plöger<br />
Kishore Pochiraju<br />
Janez Podobnik<br />
Sameera Poduri<br />
Philippe Poignet<br />
Dragoljub Pokrajac<br />
Nancy S Pollard<br />
Lorenzo Pollini<br />
Ilia G. Polushin<br />
Jorge Pomares<br />
Hasan Poonawala<br />
Dan Popa<br />
Josep M Porta<br />
Ingmar Posner<br />
Dave Post<br />
Alexis Potelle<br />
Veljko Potkonjak<br />
Andreas Pott<br />
Ioannis Poulakakis<br />
Paul Pounds<br />
Soha Pouya<br />
Nathan Powel<br />
Cedric Pradalier<br />
José Augusto Prado<br />
David Prasser<br />
Dilip Kumar Pratihar<br />
Mario Prats<br />
Jerry Pratt<br />
Domenico Prattichizzo<br />
Samuel Prentice<br />
Muriel Pressigout<br />
Edson Prestes<br />
Alberto Pretto<br />
Libor Preucil<br />
Carsten Preusche<br />
Andrzej Pronobis<br />
Nicolas Pronost<br />
Amanda Prorok<br />
Markus Przybylski<br />
Gustavo Armando Puerto<br />
Luis Puig<br />
Luca Pulina<br />
Bing Qiao<br />
Hong Qiao<br />
Tian Qiu<br />
Xingda Qu<br />
Giuseppe Quaglia<br />
Morgan Quigley<br />
Roger, D. Quinn<br />
Annika Raatz<br />
Amina Radgui<br />
Katayon Radkhah<br />
Amir Rahmani<br />
Talal Rahwan<br />
Jurdak Raja<br />
Micky Rakotondrabe<br />
Srikumar Ramalingam<br />
Subramanian Ramamoorthy<br />
Amar Ramdane-Cherif<br />
Nacim Ramdani<br />
Arnau Ramisa<br />
Nadeesha Ranasinghe<br />
Ananth Ranganathan<br />
Pradeep Ranganathan<br />
Markus Rank<br />
Shrisha Rao<br />
Mohammad Rastgaar<br />
Ravi Kulan Rathnam<br />
Nathan Ratliff<br />
Banahalli Ratna<br />
Georg Rauter<br />
Barak Raveh<br />
Prabu Ravindran<br />
Konrad Rawlik<br />
Anjan Kumar Ray<br />
Francesco Rea<br />
Pierluigi Rea<br />
Christopher M. Reardon<br />
Brice Rebsamen<br />
Gabriel Recatala<br />
Signe Redfield<br />
Brooks Reed<br />
Kyle Brandon Reed<br />
Monica Reggiani<br />
Stéphane Régnier<br />
Rob Reilink<br />
Oscar Reinoso<br />
Luís Paulo Reis<br />
Ulrich Reiser<br />
Dustin Reishus<br />
Georgios Rekleitis<br />
C. David Remy<br />
Ping Ren<br />
Wei Ren<br />
Xiaofeng Ren<br />
Pierre Renaud<br />
Maria Joao Rendas<br />
Mark Rentschler
Filoktimon Repoulias<br />
Ari Requicha<br />
David Ribas<br />
Isabel Ribeiro<br />
Rogerio Richa<br />
Arthur Richards<br />
Andrew Richardson<br />
Markus Rickert<br />
Jose-de-Jesus Rico<br />
Robert Riener<br />
Hala Rifai<br />
Ludovic Righetti<br />
Gerhard Rigoll<br />
Marcia Riley<br />
Helge Joachim Ritter<br />
Alejandro Rituerto<br />
Cameron Riviere<br />
Maximo A. Roa<br />
Richard Roberts<br />
Rodney Roberts<br />
Anders Robertsson<br />
Paolo Rocco<br />
Rui Paulo Rocha<br />
Eduardo Rocon<br />
Tobias Rodemann<br />
Adrian Rodriguez<br />
Alberto Rodriguez<br />
Carlos Rodriguez<br />
F. de Borja Rodríguez<br />
Marcela Patricia Rodriguez<br />
Samuel Rodriguez<br />
A.Rodríguez Tsouroukdissian<br />
F. Rodriguez y Baena<br />
Erick J. Rodriguez-Seda<br />
Arne Roennau<br />
John G. Rogers III<br />
Se-gon Roh<br />
John Roland<br />
Chris Roman<br />
Joseph M. Romano<br />
Eric Rombokas<br />
Lotfi Romdhane<br />
Antonio Romeo<br />
Javier Romero<br />
R. Ap. Francelin Romero<br />
M.-A. Romero-Ramirez<br />
Lluis Ros<br />
Carlos Rosales<br />
Jan Rosell<br />
Jacob Rosen<br />
Stephanie Rosenthal<br />
Robert Ross<br />
Claudio Rossi<br />
Juergen Rossmann<br />
Gerhard Roth<br />
Axel Rottmann<br />
Pierre Rouanet<br />
Stergios Roumeliotis<br />
Giannis Roussos<br />
Anthony Rowe<br />
Nicholas Roy<br />
Eric Royer<br />
Michael Rubenstein<br />
Martin Rufli<br />
Fabio Ruggiero<br />
Michael Ruhnke<br />
Andy Ruina<br />
Fabio Ruini<br />
Federico Ruiz<br />
Ubaldo Ruiz<br />
Javier Ruiz-del-Solar<br />
Wheeler Ruml<br />
Juergen Rummel<br />
Ian Rust<br />
Malcolm Ryan<br />
Paul E. Rybski<br />
Julian Ryde<br />
Jee-Hwan Ryu<br />
Jeha Ryu<br />
Thomas Röfer<br />
Thorsteinn Rögnvaldsson<br />
Selma Sabanovic<br />
Jose M. Sabater Navarro<br />
Lorenzo Sabattini<br />
Christophe Sabourin<br />
Parvaneh Saeedi<br />
Ryo Saegusa<br />
Alessandro Saffiotti<br />
Satoshi Saga<br />
Jody Alessandro Saglia<br />
Ranjana Sahai<br />
Erol Sahin<br />
Satoru Sakai<br />
Paolo Salaris<br />
Camille Salaun<br />
Septimiu E. Salcudean<br />
Antonino Salerno<br />
Curt Salisbury<br />
Roque Saltaren<br />
Giampiero Salvi<br />
Joaquim Salvi<br />
Gionata Salvietti<br />
Paul Samuel<br />
Siddharth Sanan<br />
Emilio Sánchez<br />
Abraham Sánchez López<br />
Gildardo Sanchez-Ante<br />
William Sandberg<br />
Arthur Sanderson<br />
Giulio Sandini<br />
Corina Sandu<br />
Alberto Sanfeliu<br />
Hyon Sang-Ho<br />
Kiattisak Sangpradit<br />
Ganesh Sankaranarayanan<br />
Pedro Santana<br />
Cristina Santos<br />
Veronica J. Santos<br />
Vitor Santos<br />
Amin Sarafraz<br />
Sezimaria F. P. Saramago<br />
Afsar Saranli<br />
Uluc Saranli<br />
Shigeru Sarata<br />
Irene Sardellitti<br />
Sanem Sariel-Talay<br />
Nilanjan Sarkar<br />
Hiroshi Saruwatari<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xviii–<br />
Yoko Sasaki<br />
Martin Saska<br />
Shivakumar Sastry<br />
RM Satava<br />
M. Sathia Narayanan<br />
Aykut Cihan Satici<br />
Massimo Satler<br />
Daisuke Sato<br />
Noritaka Sato<br />
Junaed Sattar<br />
Joe Saunders<br />
Guillaume Saupin<br />
Eric Sauser<br />
Jean-Philippe Saut<br />
Jesus Savage<br />
Ketan Savla<br />
Glauco Garcia Scandaroli<br />
Davide Scaramuzza<br />
Umberto Scarfogliero<br />
Paul Scerri<br />
Stefan Schaal<br />
Thomas Schauß<br />
Stefano Scheggi<br />
Michael Scheint<br />
Sebastian Scherer<br />
Paul Schermerhorn<br />
Alexis Scheuer<br />
Giuseppina Schiavone<br />
Andre Schiele<br />
Andreas Schierl<br />
Julian Schill<br />
Lars Schillingmann<br />
Joseph Schimmels<br />
Alexander Schlaefer<br />
Christian Schlegel<br />
C. Schmidt-Wetekam<br />
Alexander Schmitz<br />
Axel Schneider<br />
Sven Schneider<br />
Christian Schnier<br />
Jonathan Scholz<br />
Debra Schreckenghost<br />
Hans Peter Schrocker<br />
Derik Schroeter<br />
Björn Schuller<br />
Niclas Schult<br />
Ulrik Pagh Schultz<br />
Dirk Schulz<br />
Hannes Schulz<br />
Mac Schwager<br />
Maxim Schwartz<br />
Bernd Schäfer<br />
Enea Scioni<br />
Stephen Se<br />
Luís Seabra Lopes<br />
Jose Maria Sebastian<br />
Cristian Secchi<br />
Vlad Seghete<br />
Stephan Sehestedt<br />
Masahiro Sekimoto<br />
Claudio Semini<br />
Kei Senda<br />
Taku Senoo<br />
Jonathon Sensinger<br />
Luis Sentis<br />
TaeWon Seo<br />
Young-Woo Seo<br />
Joao Sequeira<br />
Fabrizio Sergi<br />
Andrea Serrani<br />
Fabien Servant<br />
Travis Service<br />
Andre Seyfarth<br />
Antonio Sgorbissa<br />
Robert Shade<br />
Azad Shademan<br />
Danelle Shah<br />
Shridhar Shah<br />
Hossein Shahbazi<br />
Azamat Shakhimardanov<br />
Elie Shammas<br />
Zeyong Shan<br />
Weiwei Shang<br />
Xiaowei Shao<br />
Zhufeng Shao<br />
Amir Shapiro<br />
Inna Sharf<br />
Rajnikant Sharma<br />
Dvijesh Shastri<br />
Cheng Yap Shee<br />
Raymond Ka-Man Sheh<br />
Amarda Shehu<br />
Dylan Shell<br />
Jinglin Shen<br />
Shuhan Shen<br />
Wei-Min Shen<br />
Xiangrong Shen<br />
Yanjun Shen<br />
Yantao Shen<br />
Apoorva Shende<br />
Weihua Sheng<br />
Jinbo Shi<br />
Qing Shi<br />
Mizuho Shibata<br />
Takanori Shibata<br />
Tomohiro Shibata<br />
Zvi Shiller<br />
David Hyunchul Shim<br />
Masahiro Shimizu<br />
Masayuki Shimizu<br />
Toshimi Shimizu<br />
Shingo Shimoda<br />
Makoto Shimojo<br />
Masamichi Shimosaka<br />
Dongjun Shin<br />
YongDeuk Shin<br />
Hiroyuki Shinoda<br />
Patrick Yuri Shinzato<br />
Masahiro Shiomi<br />
Shun Shiramatsu<br />
Bijan Shirinzadeh<br />
Babak Shirmohammadi<br />
Ming-Chiuan Shiu<br />
Alexander Shkolnik<br />
Florian Shkurti<br />
Steven Shladover<br />
Shraga Shoval<br />
Gabe Sibley
Bruno Siciliano<br />
Candace Sidner<br />
Daniel Sidobre<br />
Roland Siegwart<br />
Basilio Sierra<br />
Olivier Sigaud<br />
Ricardo Silva Souza<br />
Geraldo Silveira<br />
David Silver<br />
Carlos Silvestre<br />
Rob Sim<br />
Nabil Simaan<br />
Olivier Simonin<br />
Richard Simpson<br />
Jivko Sinapov<br />
Amarjeet Singh<br />
Sanjiv Singh<br />
Surya Singh<br />
Edoardo Sinibaldi<br />
Ryan W. Sinnet<br />
Hebertt Sira-Ramírez<br />
Daniel Sirkett<br />
Shahin Sirouspour<br />
Emrah Akin Sisbot<br />
Metin Sitti<br />
Dimitrios Skarlatos<br />
Henrik Skibbe<br />
Danijel Skocaj<br />
Alexander Skoglund<br />
Goran Skorja<br />
William Smart<br />
Claes Christian Smith<br />
James Smith<br />
Joshua R. Smith<br />
Ryan Smith<br />
Stephen L. Smith<br />
Ruben Smits<br />
Jamie Snape<br />
Paolo Soda<br />
Joan Solà<br />
Massimiliano Solazzi<br />
Jorge Solis<br />
Hyoung Il Son<br />
Dan Song<br />
Dezhen Song<br />
Guangming Song<br />
Kai-Tai Song<br />
Qi Song<br />
Xuan Song<br />
Zhangjun Song<br />
Zhibin Song<br />
Olof Sornmo<br />
Domenico G. Sorrenti<br />
Edoardo Sotgiu<br />
Francois Soumis<br />
Cristóvão Sousa<br />
Giacomo Spampinato<br />
Diana F. Spears<br />
Stefanie Speidel<br />
Matthew Spenko<br />
Fabien Spindler<br />
Luciano Spinello<br />
John Spletzer<br />
Mark Spong<br />
Alexander Sproewitz<br />
Christoph Sprunk<br />
Mohan Sridharan<br />
Siddhartha Srinivasa<br />
Manoj Srinivasan<br />
Cyrill Stachniss<br />
Maciej Stachura<br />
Sergiu-Dan Stan<br />
Bogdan Stanciulescu<br />
Milos Stankovic<br />
Aaron Staranowicz<br />
Olivier Stasse<br />
Christoph Staub<br />
Bastian Steder<br />
Mark Steedman<br />
Paolo Stegagno<br />
Jochen J. Steil<br />
Gerald Steinbauer<br />
Aaron Steinfeld<br />
Erik Steltz<br />
Annett Stelzer<br />
Björn Stenger<br />
Benjamin Stephens<br />
Yiannis Stergiopoulos<br />
Bruno Steux<br />
Nicholas Stiffler<br />
Christoph Stiller<br />
Daniel Stilwell<br />
Serge Stinckwich<br />
Timothy Stirling<br />
Leo Stocco<br />
Dan Stoianovici<br />
Rustam Stolkin<br />
Andreas Stolt<br />
Kasper Stoy<br />
Danail Stoyanov<br />
Todor Stoyanov<br />
Alexander Stoytchev<br />
Stefano Stramigioli<br />
Morten Strandberg<br />
Hauke Strasdat<br />
Olivier Strauss<br />
Johannes H. Strom<br />
Freek Stulp<br />
Jürgen Sturm<br />
Peter Sturm<br />
Nathan Sturtevant<br />
Jörg Stückler<br />
Michael Styer<br />
Hao Su<br />
Francisco Suarez<br />
Raul Suarez<br />
Halit Bener Suay<br />
Ram Subramanian<br />
Ioan Alexandru Sucan<br />
Jozef Suchý<br />
Attawith Sudsang<br />
Hiromichi Suetani<br />
Yusuke Sugahara<br />
Taisuke Sugaiwa<br />
Thomas Sugar<br />
Tomomichi Sugihara<br />
Norikazu Sugimoto<br />
Yasuhiro Sugimoto<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xix–<br />
Komei Sugiura<br />
Yuta Sugiura<br />
Masashi Sugiyama<br />
Osamu Sugiyama<br />
P.B. Sujit<br />
Gaurav Sukhatme<br />
Wael Suleiman<br />
Khan Suleman<br />
Cornel Sultan<br />
Yasushi Sumi<br />
Dong Sun<br />
Hanxu Sun<br />
Kuan-Chun Sun<br />
Kui Sun<br />
Qiao Sun<br />
Xiaoxun Sun<br />
Zhenglong Sun<br />
Joseph Sun de la Cruz<br />
YoonChang Sung<br />
Michael Suppa<br />
Kenji Suzuki<br />
Takashi Suzuki<br />
Tatsuya Suzuki<br />
Tsuyoshi Suzuki<br />
Mikhail Svinin<br />
Lee Sword<br />
Filip Szufnarowski<br />
Klementyna Szwaykowska<br />
Duc Anh Ta<br />
Seyed Nasr Tabatabaei<br />
Armando Tacchella<br />
Kenjiro Tadakuma<br />
Riichiro Tadakuma<br />
Satoshi Tadokoro<br />
Fabrizio Taffoni<br />
Hamid Taghirad<br />
Kenji Tahara<br />
Adnan Tahirovic<br />
Dave Tahmoush<br />
Omar Tahri<br />
Michel Taïx<br />
Kouichi Taji<br />
Keiki Takadama<br />
Motoki Takagi<br />
Junji Takahashi<br />
Masaki Takahashi<br />
Toru Takahashi<br />
Takeshi Takaki<br />
Atsuo Takanishi<br />
Wataru Takano<br />
Hidenori Takauji<br />
Leila Takayama<br />
Toshinobu Takei<br />
Hiroshi Takemura<br />
Yasunori Takemura<br />
Hiroki Takeuchi<br />
Hironori Takimoto<br />
Tomohito Takubo<br />
Takashi Takuma<br />
Mehdi Tale Masouleh<br />
Ali Talebi<br />
Levente Tamas<br />
Minija Tamosiunaite<br />
U-Xuan Tan<br />
Xiaobo Tan<br />
Youhua Tan<br />
Kanji Tanaka<br />
Masayuki Tanaka<br />
Yoshihiro Tanaka<br />
Yoshiyuki Tanaka<br />
Chaoquan Tang<br />
Chinpei Tang<br />
Hsiao-Wei Tang<br />
Xinyu Tang<br />
James Tangorra<br />
Kazuhiro Taniguchi<br />
Tadahiro Taniguchi<br />
Yong Tao<br />
Lydia Tapia<br />
Adriana Tapus<br />
Jean-Philippe Tarel<br />
Mihai Olimpiu Tatar<br />
Fujiura Tateshi<br />
Mahmoud Tavakoli<br />
Abdelhamid Tayebi<br />
Camillo Jose Taylor<br />
Matthew Taylor<br />
Yuichi Tazaki<br />
Krzysztof Tchon<br />
Russ Tedrake<br />
Bruno O.S. Teixeira<br />
Onur Tekdas<br />
Seth Teller<br />
Thomas Temple<br />
Ernesto H. Teniente Avilés<br />
Moritz Tenorth<br />
Kenji Terabayashi<br />
Carlos R. Tercero Villagran<br />
Alexander V. Terekhov<br />
Marco Henrique Terra<br />
Zdravko Terze<br />
Matthew Tesch<br />
Luka Teslic<br />
Celine Teuliere<br />
Evangelos Theodorou<br />
Dirk Thomas<br />
Federico Thomas<br />
Stephen James Thomas<br />
Andrea Lockerd Thomaz<br />
David Thompson<br />
Paul Thompson<br />
Blair Thornton<br />
Ivar Thorson<br />
Benoit Thuilot<br />
Jiang Tian<br />
David Tick<br />
Szu-Chi Tien<br />
Yung Ting<br />
Gian Diego Tipaldi<br />
Ethan Tira-Thompson<br />
Rashi Tiwari<br />
David Tlalolini<br />
Andreas Tobergte<br />
Selene Tognarelli<br />
Juan Marcos Toibero<br />
Pratap Tokekar<br />
Michael Thomas Tolley<br />
Federico Tombari
Masahiro Tomono<br />
Lachlan Toohey<br />
Elin Anna Topp<br />
Carme Torras<br />
Abril Torres<br />
Fernando Torres<br />
Eduardo Torres-Jara<br />
Alexander Toshev<br />
Iwaki Toshima<br />
David S. Touretzky<br />
Marc Toussaint<br />
Benjamin Tovar<br />
Panos Trahanias<br />
Duc Trong Tran<br />
Aksel Andreas Transeth<br />
Alberto Traslosheros<br />
Alberto Traslosheros-M<br />
Peter Trautman<br />
V. Javier Traver<br />
Ana Luisa Trejos<br />
Philip Tresadern<br />
Alberto Trevisani<br />
Vito Trianni<br />
George Triantafyllou<br />
Jean Triboulet<br />
Rudolph Triebel<br />
Marco Trincavelli<br />
Chiara Troiani<br />
Nikolaos Tsagarakis<br />
Chia-Hung Tsai<br />
Dimitris Tsakiris<br />
Nikolaos Tsekos<br />
Panagiotis Tsiotras<br />
Manolis Tsogas<br />
John Tsotsos<br />
Antonios Tsourdos<br />
Sadayuki Tsugawa<br />
Toshiaki Tsuji<br />
Hideyuki Tsukagoshi<br />
Yuichi Tsumaki<br />
Mihran Tuceryan<br />
Stephen Tully<br />
Win Tun Latt<br />
Alessio Turetta<br />
Masaru Uchiyama<br />
Ryuichi Ueda<br />
Mitsunori Uemura<br />
Hiroshi Ueno<br />
Toshio Ueshiba<br />
Tsuyoshi Ueyama<br />
Emre Ugur<br />
Barkan Ugurlu<br />
Klaus Uhl<br />
Heinz Ulbrich<br />
Giovanni Ulivi<br />
Paul Umbanhowar<br />
James Patrick Underwood<br />
Alois Unterholzner<br />
Ben Upcroft<br />
Takateru Urakubo<br />
Alfonso Urquia<br />
Nobuhiro Ushimi<br />
Yuzuko Utsumi<br />
Nikolaus Vahrenkamp<br />
Ravi Vaidyanathan<br />
Pablo Valdivia y Alvarado<br />
Philip Valencia<br />
Luis M. Valentin-Coronado<br />
Alberto Valero-Gomez<br />
Heike Vallery<br />
Jaime Valls Miro<br />
Marco Valtorta<br />
Michaël Van Damme<br />
M.J.G. van de Molengraft<br />
Michiel van de Panne<br />
Joop van de Ven<br />
Jur van den Berg<br />
Ferdi van der Heijden<br />
H.F. Machiel Van der Loos<br />
Frank van der Stappen<br />
Ronald Van Ham<br />
Joshua Vander Hook<br />
Bram Vanderborght<br />
Paul Varnell<br />
Pablo Varona<br />
Panagiotis Vartholomeos<br />
Francisco Vasconcelos<br />
Eric Vasselin<br />
Pascal Vasseur<br />
Gabriele Vassura<br />
Shrihari Vasudevan<br />
Monica Vatteroni<br />
Richard Vaughan<br />
Marynel Vazquez<br />
Harini Veeraraghavan<br />
Prasanna Velagapudi<br />
Matteo Venanzi<br />
Marilena Vendittelli<br />
Jan Veneman<br />
Subramaniam Venkatraman<br />
Rodrigo Ventura<br />
Gentiane Venture<br />
Kevin Veon<br />
Alexander Verl<br />
Paul Vernaza<br />
John Vial<br />
Janis Viba<br />
Marco Vicentini<br />
Alessandro Correa Victorino<br />
Rene Vidal<br />
Fernando Vidal-Verdú<br />
José Vieira<br />
Diego Viejo<br />
Pierre Vieyres<br />
Bjørnar Vik<br />
Vishesh Vikas<br />
Carlos Villalpando<br />
Michael Villamizar Vergel<br />
Luigi Villani<br />
Markus Vincze<br />
Stephane Viollet<br />
Francesco Visentin<br />
Arnoud Visser<br />
Michael Vistein<br />
Nicola Vitiello<br />
Valentina Vitiello<br />
Marie-Aude Vitrani<br />
Iason Vittorias<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xx–<br />
Michael Vitus<br />
Kostas Vlachos<br />
Christopher Vo<br />
Luca Vollero<br />
Marsette Vona<br />
Randolph Voorhies<br />
Philipp Vorst<br />
Stavros Vougioukas<br />
Trung-Dung Vu<br />
Sven Wachsmuth<br />
Masayoshi Wada<br />
Neeti Wagle<br />
Glenn Wagner<br />
Mattias Wahde<br />
Friedrich M. Wahl<br />
Keith Wait<br />
Shuichi Wakimoto<br />
Ian Walker<br />
Andrew M. Wallace<br />
Jennifer Walter<br />
Matthew Walter<br />
Michael Leonard Walters<br />
Chaoli Wang<br />
Chieh-Chih Wang<br />
Danwei Wang<br />
David Wang<br />
Fei-Yue Wang<br />
Han Wang<br />
Hao Wang<br />
Hesheng Wang<br />
Hongbo Wang<br />
Huadong Wang<br />
Hui Wang<br />
Huifang Wang<br />
Jianxun Wang<br />
Jing Wang<br />
Jingguo Wang<br />
Jiuguang Wang<br />
Junping Wang<br />
Kai Wang<br />
Kundong Wang<br />
Long Wang<br />
Lu Wang<br />
Meimei Wang<br />
Nianfeng Wang<br />
Qining Wang<br />
Shuguo Wang<br />
Shuo Wang<br />
Wei-Wen Wang<br />
Weifu Wang<br />
Wenhui Wang<br />
X. Rosalind Wang<br />
Xinqing Wang<br />
Yao-Dong Wang<br />
Yue Wang<br />
Zheng Wang<br />
Zhidong Wang<br />
Zhifeng Wang<br />
Zhijie Wang<br />
Zhongkui Wang<br />
Tim Wark<br />
Steven Lake Waslander<br />
John Wason<br />
Mutsumi Watanabe<br />
Tetsuyou Watanabe<br />
Jens Wawerla<br />
Stephen Leslie Scott Webb<br />
Sarah E. Webster<br />
Robert James Webster III<br />
Hongxing Wei<br />
Andrew P.H. Weightman<br />
Felipe A. Weilemann Belo<br />
Stephan Weiss<br />
Alfredo Weitzenfeld<br />
Uchechukwu C. Wejinya<br />
Kai Welke<br />
Briana Wellman<br />
Johannes Wendeberg<br />
Philippe Wenger<br />
Patrick Wensing<br />
Justin Werfel<br />
Moritz Werling<br />
E.P. Westebring – v.d. Putten<br />
Nicholas Wettels<br />
David Wettergreen<br />
Paul White<br />
Jacob Whitehill<br />
Eric Whitman<br />
Mark Albert Whitty<br />
Pierre-Brice Wieber<br />
Nicholas Wilkinson<br />
Volker Willert<br />
Richard Willgoss<br />
Aaron L. Williams<br />
Brian Patrick Williams<br />
Ian Williams<br />
Kjerstin Williams<br />
Mary-Anne Williams<br />
Stefan Bernard Williams<br />
Stephen Williams<br />
Reg Willson<br />
Thomas Wimboeck<br />
Alan Winfield<br />
Alexander Winkler<br />
Amos Greene Winter<br />
Raul Wirz Gonzalez<br />
Heinz Woern<br />
Walter Wohlkinger<br />
Hans Woithe<br />
Michael Wolf<br />
Sebastian Wolf<br />
Bryn Wolfe<br />
Carsten Wolff<br />
Dirk Wollherr<br />
Lok Sang Lawson Wong<br />
Tichakorn Wongpiromsarn<br />
John Wood<br />
Nathan Wood<br />
Robert Wood<br />
Oliver Woodford<br />
Oliver Woodman<br />
Matthew Woodward<br />
Craig Woolsey<br />
James Worcester<br />
Franz Wotawa<br />
Britta Wrede<br />
Sebastian Wrede<br />
Ban Wu
Bing-Fei Wu<br />
Chao Wu<br />
Guanglei Wu<br />
Guosheng Wu<br />
Haiyuan Wu<br />
Huapeng Wu<br />
Jianxin Wu<br />
Po Wu<br />
Shandong Wu<br />
Wencen Wu<br />
Burkhard Wuensche<br />
Kai M. Wurm<br />
Gordon Wyeth<br />
Christian Wögerer<br />
FengFeng Xi<br />
Tian Xia<br />
Zhiyu Xiang<br />
Jizhong Xiao<br />
Jun Xiao<br />
Shunli Xiao<br />
Ming Xie<br />
Shaorong Xie<br />
Anqi Xu<br />
Bin Xu<br />
Changhai Xu<br />
De Xu<br />
Jijie Xu<br />
Kai Xu<br />
Qingsong Xu<br />
Tao Xu<br />
Yang Xu<br />
Yangsheng Xu<br />
Yiliang Xu<br />
Yunfei Xu<br />
Zhe Xu<br />
Zhixing Xue<br />
Takehisa Yairi<br />
Hiroya Yamada<br />
Katsuhiko Yamada<br />
Takayoshi Yamada<br />
Hiroaki Yamaguchi<br />
Masaki Yamakita<br />
Ikuo Yamamoto<br />
Motoji Yamamoto<br />
Yoko Yamanishi<br />
Mitsuhiro Yamano<br />
Natsuki Yamanobe<br />
Atsushi Yamashita<br />
Hiromasa Yamashita<br />
Junji Yamato<br />
Brian Yamauchi<br />
Tasuku Yamawaki<br />
Kimitoshi Yamazaki<br />
Gangfeng Yan<br />
Rujiao Yan<br />
Holly Yanco<br />
Chenguang Yang<br />
Guilin Yang<br />
Huizhen Yang<br />
Junho Yang<br />
Ming Yang<br />
Ruiguo Yang<br />
Sungwook Yang<br />
Tao Yang<br />
Woosung Yang<br />
Xiaoli Yang<br />
Yousheng Yang<br />
Zhenwang Yao<br />
Masahito Yashima<br />
Sergey Yatsun<br />
Jano Yazbeck<br />
M. J. Yazdanpanah<br />
Changlong Ye<br />
Hsin-Yi (Cindy) Yeh<br />
Je Sung Yeon<br />
Dmitry Yershov<br />
Anna Yershova<br />
Byung-Ju Yi<br />
Jianqiang Yi<br />
Jingang Yi<br />
Chih-Chen Yih<br />
Mark Yim<br />
Sehyuk Yim<br />
KangKang Yin<br />
John David Yoder<br />
Kazuhito Yokoi<br />
Yasuyoshi Yokokohji<br />
Naokazu Yokoya<br />
Kan Yoneda<br />
Hyun-Soo Yoon<br />
Jungwon Yoon<br />
Yong-San Yoon<br />
Eiichi Yoshida<br />
Morio Yoshida<br />
Takami Yoshida<br />
Kenji Yoshigoe<br />
Kazuyoshi Yoshii<br />
Tomoaki Yoshikai<br />
Taizo Yoshikawa<br />
Kitaro Yoshimitsu<br />
Jason Yosinski<br />
Kuu-young Young<br />
Shelley S.C. Young<br />
Ryuh Youngsun<br />
Karim Youssef<br />
Hongnian Yu<br />
Huili Yu<br />
Hung-Hsiu Yu<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xxi–<br />
Jiancheng Yu<br />
Jingjin Yu<br />
Jingjun Yu<br />
Ningbo Yu<br />
Qian Yu<br />
Shengwei Yu<br />
Son Cheol Yu<br />
Yong Yu<br />
Yue-Qing Yu<br />
Chunrong Yuan<br />
Jianjun Yuan<br />
Peijiang Yuan<br />
Dongwon Yun<br />
Seung-kook Yun<br />
Kris Zacny<br />
Spyros Zafeiropoulos<br />
Michael Friedrich Zäh<br />
Nazanin Zaker<br />
Alexey Zakharov<br />
Avideh Zakhor<br />
Eduardo Zalama<br />
Andrea Maria Zanchettin<br />
Damiano Zanotto<br />
René Zapata<br />
Michael M. Zavlanos<br />
Massimiliano Zecca<br />
Milos Zefran<br />
Said Zeghloul<br />
Garth J Zeglin<br />
John S. Zelek<br />
Andreas Zell<br />
Hendrik Zender<br />
Shuqing Zeng<br />
Wenwu Zeng<br />
Davide Zerbato<br />
Chengkun Zhang<br />
Dongjun Zhang<br />
Guoxuan Zhang<br />
Haihong Zhang<br />
Hong Zhang<br />
Hong Zhang<br />
Houxiang Zhang<br />
Jian Zhang<br />
Jiangbo Zhang<br />
Jianwei Zhang<br />
Junjie Zhang<br />
Li Zhang<br />
Li Zhang<br />
Liang Zhang<br />
Liangjun Zhang<br />
Mingjun Zhang<br />
Shiqi Zhang<br />
Tao Zhang<br />
Weizhong Zhang<br />
Wenzeng Zhang<br />
Xianmin Zhang<br />
Xuping Zhang<br />
Yan Zhang<br />
Yan Liang Zhang<br />
Yanwu Zhang<br />
Ying Zhang<br />
Yizhai Zhang<br />
Yong Zhang<br />
Yongsheng Zhang<br />
Yu (Tony) Zhang<br />
Yunong Zhang<br />
Zhao Zhang<br />
Dongbin Zhao<br />
Huijing Zhao<br />
Jianguo Zhao<br />
Jing Zhao<br />
Mingguo Zhao<br />
Tao Zhao<br />
Nanning Zheng<br />
Xiaoming Zheng<br />
Yu Zheng<br />
Debao Zhou<br />
Kai Zhou<br />
Lelai Zhou<br />
Quan Zhou<br />
Xiaobo Zhou<br />
Xun Zhou<br />
Yan Zhou<br />
Yu Zhou<br />
Zhi Zhou<br />
Wen-Hong Zhu<br />
Zhiwei Zhu<br />
Stefan Zickler<br />
Julius Ziegler<br />
Marc Ziegler<br />
Michael Zillich<br />
Primo Zingaretti<br />
Michael Zinn<br />
Zoran Zivkovic<br />
Alessandro Antonio Zizzari<br />
Robert Zlot<br />
M.H. Zokaei Ashtiani<br />
Johann Marius Zöllner<br />
Raoul Zöllner<br />
Loredana Zollo<br />
Qingze Zou<br />
Wei Zou<br />
Matthew Zucker<br />
Zhiyuan Zuo<br />
Matthijs Jan Zwinderman
Plenary Sessions<br />
Plenary I: Design<br />
Tuesday, September 27, <strong>2011</strong>, 12:30-13:45, Continental Ballroom<br />
Moderator: Professor Bernard Roth, Stanford University<br />
The Design Plenary features three design pioneers (Hirose, Hirzinger, and Raibert)<br />
discussing their perspectives on various aspects of mechanism design. After initial<br />
presentations by the panelists, the chair (Roth) will moderate a panel discussion with the<br />
experts and the audience.<br />
Professor Shigeo Hirose was born in Tokyo in 1947. He received his B.Eng.<br />
Degree with First Class Honors in Mechanical Engineering from Yokohama<br />
National University in 1971, and his M. Eng. and Ph.D. Eng. Degrees in<br />
Control Engineering from Tokyo Institute of Technology in 1973 and 1976,<br />
respectively. From 1976 to 1979 he was a Research Associate, and from<br />
1979 to 1992 an Associate Professor. Since 1992 he has been a Professor<br />
in the Department of Mechanical and Aerospace Engineering at the Tokyo<br />
Institute of Technology. He is a fellow of JSME and IEEE. He is engaged in creative design<br />
of robotic systems. Prof. Hirose has been awarded more than twenty prizes.<br />
Professor Gerd Hirzinger is director at DLR’s institute for „Robotics and<br />
Mechatronics“, which is one of the biggest and most acknowledged<br />
Institutes in the field worldwide. He was prime investigator of the space robot<br />
technology experiment ROTEX, the first remote controlled robot in space,<br />
which flew onboard shuttle COLUMBIA in April 1993. He has published<br />
more than 600 papers in robotics. He received numerous national and<br />
international awards, e.g., in 1994 the Joseph-Engelberger-Award for<br />
achievements in robotic science and in 1995 the Leibniz-Award, the highest scientific<br />
award in Germany and the JARA (Japan robotics association) Award. In 2005 he received<br />
the IEEE Pioneer Award of the Robotics and Automation Society, and in 2007 the IEEE<br />
Field Award “Robotics and Automation”.<br />
Dr. Marc Raibert was Professor of Electrical Engineering and Computer<br />
Science at MIT and a member of the Artificial Intelligence Laboratory from<br />
1986 through 1995. He is co-founder and President of Boston Dynamics Inc,<br />
(BDI), which is located near MIT in Cambridge. Raibert's research is devoted<br />
to the study of systems that move dynamically, including physical robots and<br />
animated creatures. Raibert received a B.S. degree in Electrical Engineering<br />
from Northeastern University in 1973, and a Ph.D from the Massachusetts<br />
Institute of Technology in 1977. He is author of Legged Robots That Balance published by<br />
MIT Press, and is on the Editorial Board of the International Journal of Robotics Research.<br />
He is a fellow of the AAAI.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xxii–
Plenary II: BioRobotics<br />
Wednesday, September 28, <strong>2011</strong>, 12:30-13:45, Continental Ballroom<br />
Moderator: Professor Ruzena Bajcsy, University of California, Berkeley<br />
The BioRobotics Plenary features three biorobotics pioneers (Berthoz, Buelthoff, and<br />
Srinivasan) discussing their perspectives on various aspects of biorobotics and biomimetic<br />
robotics. After initial presentations by the panelists, the chair (Bajcsy) will moderate a panel<br />
discussion with the experts and the audience.<br />
Professor Alain Berthoz was born in 1939. He became Civil Engineer at<br />
École des Mines in 1963, Ph.D. in 1973. As researcher at (CNRS) (1966-<br />
1981), he established and coordinated the Neurosensory Physiology<br />
Laboratory (1981-1993). Since 1993, he is professor at Collège de France<br />
and director of UMR CNRS/Collège de France "Physiology of perception<br />
and action". He has over 200 scientific publications in international journals<br />
on physiology of sensori-motor functions and more specifically on the<br />
oculomotor system, the vestibular system, balance control, and movement perception. He<br />
had given about 90 invited talks across the world.<br />
Professor Heinrich Bülthoff is scientific member of the Max Planck<br />
Society and director at the Max Planck Institute for Biological Cybernetics<br />
in Tübingen. He is head of the Department Human Perception, Cognition<br />
and Action in which a group of about 70 researchers investigate<br />
psychophysical and computational aspects of higher level visual processes<br />
in object and face recognition, sensory-motor integration, spatial cognition,<br />
and perception and action in virtual environments. He holds a Ph.D. degree<br />
in the natural sciences from the Eberhard-Karls-Universität in Tübingen. He was Assistant,<br />
Associate and Full Professor of Cognitive Science at Brown University in Providence from<br />
1988-1993 before becoming director at the Max Planck Institute for Biological Cybernetics.<br />
Professor Mandyam Srinivasan is at the Queensland Brain Institute and<br />
the School of Information Technology and Electrical Engineering of the<br />
University of Queensland. He holds an undergraduate degree in Electrical<br />
Engineering from Bangalore University, a Master's degree in Electronics<br />
from the Indian Institute of Science, a Ph.D. in Engineering and Applied<br />
Science from Yale University, a D.Sc. in Neuroethology from the Australian<br />
National University, and an Honorary Doctorate from the University of<br />
Zurich. Among his awards and honors are Fellowships of the Australian Academy of<br />
Science, of the Royal Society of London, and of the Academy of Sciences for the<br />
Developing World, an Inaugural Federation Fellowship, the 2006 Australia Prime Minister’s<br />
Science Prize, and the 2008 U.K. Rank Prize for Optoelectronics.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xxiii–
Plenary III: Self-Driving Cars<br />
Thursday, September 29, <strong>2011</strong>, 13:30-14:30, Continental Ballroom<br />
Professor Sebastian Thrun, Stanford University/Google<br />
Abstract: Most of us use a car every day. But unlike airplanes, which have been flying on<br />
autopilots for decades, cars are still driven manually - just the way they were driven 100<br />
years ago. This talk will introduce the transformative concept of a self-driving car. Following<br />
early research in the 1990 in Germany and the US, and more recently the DARPA<br />
Challenges, this technology has now been advanced to a point where it is within reach of<br />
commercial realizations that may provide benefits to pretty much anyone who drives a car.<br />
At the core of this progress is a new generation of cutting-edge artificial intelligence, which<br />
enables a self-driving car to understand its environment and to interact with other traffic.<br />
The speaker will discuss the Google Self-Driving Car project, in which a fleet of self-driving<br />
cars navigated more than 160,000 miles on public roads in California and Nevada,<br />
including the downtowns of San Francisco and Los Angeles. The speaker will also discuss<br />
some of the societal implications of this new technology.<br />
Sebastian Thrun is a Professor of Computer Science at Stanford<br />
University and director of the Stanford Artificial Intelligence Laboratory<br />
(SAIL). He led the development of the robotic vehicle Stanley that won<br />
the 2005 DARPA Grand Challenge. His team also developed Junior,<br />
which placed second at the DARPA Urban Challenge in 2007. Thrun led<br />
the development of the Google self-driving car and is well known for his<br />
work on probabilistic programming techniques in robotics, with<br />
applications including robotic mapping. He was elected into the National<br />
Academy of Engineering and also into the German Academy of Sciences<br />
Leopoldina in 2007. In <strong>2011</strong>, he received the Max-Planck-Research Award and the<br />
inaugural AAAI Ed Feigenbaum Prize. Thrun received his Diplom (master's degree) in<br />
1993 and a PhD (summa cum laude) in 1995 in computer science and statistics from the<br />
University of Bonn. He was on the CMU faculty from 1995 till 2003. Since 2003 he has<br />
been on the faculty of the Stanford Computer Science department. He is also a Google<br />
Fellow.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xxiv–
Workshops and Tutorials<br />
A total of 27 workshops and tutorials are scheduled on Sunday, Monday, and Friday of the<br />
conference week. They are listed here for your consideration. More detailed information<br />
and all workshops and tutorials proceedings can be found in the conference website<br />
www.iros<strong>2011</strong>.org/workshops-and-tutorials. All events take place in the Continental Ball<br />
and Parlor rooms 1–9, and in the Golden Gate Rooms 6 and 7.<br />
Sunday, September 25, <strong>2011</strong>: Full-day workshops and tutorials<br />
Session Room Title and Website Link Organizers<br />
ST1<br />
Tutorial<br />
ST2<br />
Tutorial<br />
ST3<br />
Tutorial<br />
SW4<br />
Workshop<br />
SW5<br />
Workshop<br />
SW6<br />
Workshop<br />
SW7<br />
Workshop<br />
SW8<br />
Workshop<br />
SW9<br />
Workshop<br />
Golden<br />
Gate<br />
Rooms 6-7<br />
Continental<br />
Parlor 1<br />
Continental<br />
Parlor 2<br />
Continental<br />
Ballroom 4<br />
Continental<br />
Ballroom 6<br />
Continental<br />
Parlor 3<br />
Continental<br />
Parlor 7<br />
Continental<br />
Parlor 8<br />
Continental<br />
Parlor 9<br />
Motion Planning for Real Robots<br />
3D Point Cloud Processing: PCL (Point<br />
Cloud Library)<br />
DARwIn-OP: An Open Platform, Miniature<br />
Humanoid Robot Platform for Research,<br />
Education and Outreach<br />
20 years of Microrobotics: progress,<br />
challenges, and future directions<br />
Cognitive Neuroscience Robotics<br />
Image-Guided Medical Robotic<br />
Interventions<br />
Knowledge Representation for<br />
Autonomous Robots<br />
Autonomous Underwater Robotics for<br />
Intervention<br />
Reconfigurable Modular Robotics:<br />
Challenges of Mechatronic and Bio-<br />
Chemo- Hybrid Systems<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xxv–<br />
Sachin Chitta<br />
Edward Gil Jones<br />
Ioan Alexandru Sucan<br />
Mark Moll*<br />
Lydia Kavraki<br />
Radu Bogdan Rusu*<br />
Michael Dixon<br />
Aitor Aldoma<br />
Suat Gedikli<br />
Dennis Hong*<br />
Daniel D. Lee<br />
Dan Popa*<br />
Fumihito Arai<br />
Kenichi Narioka*<br />
Yukie Nagai<br />
Minoru Asada<br />
Hiroshi Ishiguro<br />
Sarthak Misra*<br />
Jaydev P. Desai<br />
Michael Beetz<br />
Rachid Alami<br />
Joachim Hertzberg<br />
Alessandro Saffiotti<br />
Moritz Tenorth*<br />
Junku Yuh<br />
Giuseppe Casalino<br />
Alessio Turetta*<br />
Serge Kernbach*<br />
Robert Charles Fitch
Monday, September 26, <strong>2011</strong>: Half-day workshops and tutorials<br />
Session Room Title and Website Link Organizers<br />
MT1<br />
Tutorial<br />
MW2<br />
Workshop<br />
MW3<br />
Workshop<br />
MW4<br />
Workshop<br />
MW5<br />
Workshop<br />
MW6<br />
Workshop<br />
MW7<br />
Workshop<br />
MW8<br />
Workshop<br />
MW9<br />
Workshop<br />
Continental<br />
Ballroom 5<br />
Continental<br />
Parlor 2<br />
Continental<br />
Parlor 8<br />
Continental<br />
Parlor 1<br />
Continental<br />
Parlor 3<br />
Continental<br />
Parlor 7<br />
Continental<br />
Ballroom 4<br />
Continental<br />
Ballroom 6<br />
Continental<br />
Parlor 9<br />
Introduction to Rescue Robotics Robin Murphy*<br />
Visual Control of Mobile Robots<br />
(ViCoMoR)<br />
New and Emerging Technologies in<br />
Assistive Robotics<br />
Visual Tracking and Omni-directional<br />
Vision<br />
Space Robotics Simulation<br />
Current Directions in Marine Vehicle<br />
Autonomy Research<br />
Active Semantic Perception and Object<br />
Search in the Real World<br />
Sixth International Cognitive Vision<br />
Workshop (ICVW <strong>2011</strong>) Situated Vision vs.<br />
Internet Vision<br />
Redundancy in Robot Manipulators and<br />
Multi-Robot Systems<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xxvi–<br />
Youcef Mezouar<br />
G. Lopez-Nicolas*<br />
Kazuyoshi Wada*<br />
Machiel Van der Loos<br />
Loredana Zollo<br />
El Mustapha Mouaddib*<br />
Eric Marchand<br />
João P. Barreto<br />
Yasushi Yagi<br />
Rainer Krenn*<br />
Yves Gonthier<br />
Donald Eickstedt*<br />
Mae Seto<br />
Alper Aydemir<br />
Andrzej Pronobis<br />
Bhaskara Marthi<br />
Patric Jensfelt*<br />
Dirk Holz<br />
Barbara Caputo*<br />
Fiora Pirri<br />
Michael Zillich<br />
Dejan Milutinovic*<br />
Jacob Rosen
Friday, September 30, <strong>2011</strong>: Full-day workshops and tutorials<br />
Session Room Title and Website Link Organizers<br />
FW1<br />
Workshop<br />
FW2<br />
Workshop<br />
FW3<br />
Workshop<br />
FW4<br />
Workshop<br />
FW5<br />
Workshop<br />
SW6<br />
Workshop<br />
FW7<br />
Workshop<br />
FW8<br />
Workshop<br />
FW9<br />
Workshop<br />
Continental<br />
Ballroom 4<br />
Continental<br />
Ballroom 6<br />
Continental<br />
Parlor 1<br />
Continental<br />
Parlor 3<br />
Continental<br />
Parlor 7<br />
Continental<br />
Parlor 9<br />
Continental<br />
Parlor 2<br />
Continental<br />
Parlor 8<br />
Continental<br />
Ballroom 5<br />
* = Contact organizer<br />
European Efforts in Strengthening the<br />
Academia-Industry Collaboration<br />
Progress and Open Problems in Motion<br />
Planning<br />
Current and Future Related Technologies<br />
for Robotic Automation in Micro/Nano<br />
Scale<br />
Metrics and Methodologies for<br />
Autonomous Robot Teams in Logistics<br />
(MMART-LOG)<br />
Methods for Safer Surgical Robotics<br />
Procedures<br />
Robotics for Neurology and Rehabilitation<br />
Robotics for Environmental Monitoring<br />
Perception and Navigation for<br />
Autonomous Vehicles in Human<br />
Environment<br />
The PR2 Workshop: Results, Challenges<br />
and Lessons Learned in Advancing<br />
Robotics With a Common Platform<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xxvii–<br />
Reinhard Lafrenz*<br />
Alois Knoll<br />
Bruno Siciliano<br />
Rainer Bischoff<br />
Anne Wendel<br />
Timothy Bretl*<br />
Dan Halperin<br />
Kostas E. Bekris<br />
Masahiro Nakajima*<br />
Toshio Fukuda<br />
Alexander Kleiner*<br />
Rolf Lakaemper<br />
Raj Madhavan<br />
Rainer Konietschke*<br />
Stefan Joerg<br />
Paolo Fiorini<br />
Gentiane Venture*<br />
Philippe Fraisse<br />
Mitsuhiro Hayashibe<br />
Thierry Keller<br />
Lino Marques*<br />
Ryan Smith<br />
Volkan Isler<br />
Philippe Martinet*<br />
Christian Laugier<br />
Urbano Nunes<br />
William Smart*<br />
Sachin Chitta<br />
Caroline Pantofaru<br />
Radu Bogdan Rusu
Floorplan<br />
Demonstration Sessions<br />
Robotic demonstrations will be shown on the exhibition floor at the Hilton hotel (Golden Gate<br />
Room 8, Lobby level), next to the interactive sessions.<br />
Schedule<br />
Demonstrations run in parallel to the technical sessions from Tuesday, September 27 to<br />
Thursday morning, September 29, <strong>2011</strong>. Demonstrations marked with a star (*) have<br />
been selected for presentation in the special demonstration symposium (see next table).<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xxviii–
Monday, Sept 26 Tuesday, Sept 27 Wednesday, Sept 28 Thursday, Sept 29<br />
Demo Symposium Morning Afternoon Morning Afternoon Morning Afternoon<br />
Space A<br />
SP5<br />
"Teleoperation"<br />
(Care-o-bot)<br />
SP3*<br />
"Service<br />
robot"<br />
(Care-o-bot)<br />
SP4*<br />
"Active<br />
perception"<br />
(Care-o-bot)<br />
SP6*<br />
"Mobile<br />
manipulation"<br />
(Care-o-bot)<br />
OR4*<br />
"Pixhawk<br />
MAV"<br />
Space B<br />
SP7* SP9 SP10*<br />
"iTaSC "Manipulation "Haptic<br />
framework" tools" coupling"<br />
(PR2) (PR2) (PR2, youBot)<br />
OR9<br />
"Ballbot<br />
Rezero"<br />
OR8<br />
"The r-one"<br />
Space C<br />
OR6*<br />
"Bio-inspired<br />
humanoid"<br />
OR7<br />
"Needle<br />
steering"<br />
OR10<br />
"Perception<br />
for<br />
manipulation"<br />
OR5<br />
"Haptic<br />
rendering"<br />
SP12*<br />
"More<br />
cowbell"<br />
(Nao)<br />
Space D<br />
OR3<br />
"DARPA<br />
Arm"<br />
OR2*<br />
"Display-<br />
Swarm"<br />
OR1*<br />
"Kilobot"<br />
SP13<br />
"Visual<br />
servoing"<br />
(Nao)<br />
SP11*<br />
"Climbing<br />
stairs"<br />
(Nao)<br />
Space E<br />
iCub space<br />
SP1<br />
"Cooperative<br />
game"<br />
(iCub)<br />
List of Demonstrations<br />
SP2*<br />
"Aquila"<br />
(iCub)<br />
Standard Platform Demonstrations<br />
Other iCub<br />
demos<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xxix–<br />
SP8<br />
"Do not touch"<br />
(PR2)<br />
Other iCub<br />
demos<br />
Other iCub<br />
demos<br />
A number of standard platforms for mobility and manipulation in robotics research now exist,<br />
as both off-the-shelf commercial products, and research platforms with wide distribution.<br />
Several manufacturers have made available their platforms for demonstration. There are two<br />
benefits to this arrangement: demonstrators will not have to transport hardware, and the<br />
demonstrations will show the increasing use of standard hardware as a mean of attacking<br />
advanced applications. The accepted demonstrations for this session are:<br />
SP1 – iCub Learning a Cooperative Game through Interaction with his Human Partner<br />
Stephane Lallee, Ugo Pattacini, Lorenzo Natale, Giorgio Metta, Peter Ford Dominey<br />
Stem Cell and Brain Research Institute, Bron, France, Italian Institute of Technology, Genoa, Italy<br />
Platform: iCub<br />
Abstract: A human can interact with the iCub by pointing and speaking together in order to learn,<br />
name and play with unknown objects on a table. User can then ask for more complex actions and<br />
“program” small games by instructing the robot with a shared plan.<br />
SP2 – Aquila - The Cognitive Robotics Toolkit (*)<br />
Martin Peniak, Anthony F. Morse<br />
University of Plymouth, UK<br />
Platform: iCub<br />
Abstract: A live demonstration of Aquila, an open source cognitive robotics software toolkit for the<br />
iCub robot. This demonstration includes modeling sensorimotor learning in child development,<br />
action acquisition, and robot teleoperation.
SP3 – General Purpose Service Robot (*)<br />
Nico Hochgeschwender, Jan Paulus, Michael Reckhaus, Frederik Hegger, Christian A. Mueller,<br />
Sven Schneider, Paul G. Ploeger, Gerhard K. Kraetzschmar<br />
Bonn-Rhein-Sieg University of Applied Sciences, Bonn, Germany<br />
Platform: Care-O-Bot 3<br />
Abstract: In our demonstration we show a general purpose service robot which performs various<br />
tasks on demand.<br />
SP4 – Active Perception Planning on the Care-o-Bot platform (*)<br />
Robert Eidenberger, Michael Fiegert, Georg von Wichert, Gisbert Lawitzky<br />
Siemens, Munich, Germany<br />
Platform: Care-O-Bot 3<br />
Abstract: The Care-O-Bot platform localizes household objects in complex environments and<br />
grasps them. Environment perception and active perception planning is demonstrated and used for<br />
efficient scene modeling.<br />
SP5 – Semi-autonomous Tele-Operation Interface for Robotic Fetch and Carry Tasks<br />
Renxi Qiu 1 , Alexandre Noyvirt 1 , Nayden Chivarov 2 , Rafael Lopez 3 , Georg Arbeiter 4 , Dayou Li 5<br />
1 2 3<br />
Cardiff Univ., UK; Bulgarian Academy of Science, Bulgaria; Robotnik Automation, Spain;<br />
4 5<br />
Fraunhofer IPA, Germany; Univ. of Bedfordshire, UK<br />
Platform: Care-O-Bot 3<br />
Abstract: The demonstration focuses on remote user interface such as Apple IPAD to tele-operate<br />
robot semi-autonomously. It enables non-expert users taking charge of the robot around the home.<br />
SP6 – Using Mobile Manipulation to solve a Household Task (*)<br />
Florian Weisshardt, Alexander Bubeck, Jan Fischer, Ulrich Reiser<br />
Fraunhofer IPA, Stuttgart, Germany<br />
Platform: Care-O-Bot 3<br />
Abstract: The task of serving objects is significantly scalable and reaches from table top<br />
manipulation of simple objects to retrieving objects that require mobile manipulation of the<br />
environment first.<br />
SP7 – Demonstration of iTaSC as a unified framework for task specification, control, and<br />
coordination for mobile manipulation (*)<br />
Dominick Vanthienen, Tinne De Laet, Markus Klotzbuecher, Ruben Smits, Wilm Decré, Koen Buys,<br />
Steven Bellens, Herman Bruyninckx, and Joris De Schutter<br />
Katholieke Universiteit Leuven, Belgium<br />
Platform: PR2<br />
Abstract: Mobile co-manipulation task of a PR2 and a human, to illustrate the potential of the iTaSC<br />
method and its Orocos implementation to specify sensor-based, multi-frame, partially-specified<br />
robot tasks.<br />
SP8 – Please Do Not Touch the Robot<br />
Joseph M. Romano, Katherine J. Kuchenbecker<br />
University of Pennsylvania, Philadelphia, USA<br />
Platform: PR2<br />
Abstract: Natural human-robot interaction requires a tight coupling between sensing and control.<br />
Our PR2 demo use high-bandwidth tactile signals to showcase dynamic interactive behaviors such<br />
as high-fives.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
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SP9 – Interactive Manipulation Tools for the PR2 Robot<br />
Matei Ciocarlie, Kaijen Hsiao<br />
Willow Garage, Menlo Park, USA<br />
Platform: PR2<br />
Abstract: Interactive manipulation tools for the PR2: everything from low-level tools such as moving<br />
grippers around in Cartesian space, to high-level tools such as grasping segmented or recognized<br />
objects.<br />
SP10 – Haptic coupling with augmented feedback between the KUKA youBot and the PR2<br />
robot arms (*)<br />
Steven Bellens, Koen Buys, Nick Vanthienen, Tinne De Laet, Ruben Smits, Markus Klotzbuecher,<br />
Wilm Decré, Herman Bruyninckx, and Joris De Schutter<br />
Katholieke Universiteit Leuven, Belgium<br />
Platform: PR2 and youBot<br />
Abstract: We demonstrate haptic coupling between the KUKA youBot and the PR2 robot arms. A<br />
wearable display provides the operator with PR2 camera images, his head movement is coupled<br />
with the PR2's head.<br />
SP11 – Detecting and Climbing Stairs with a Laser-equipped Nao Humanoid (*)<br />
Stefan Osswald, Armin Hornung, Maren Bennewitz<br />
University of Freiburg, Germany<br />
Platform: Nao<br />
Abstract: We demonstrate autonomous stair climbing based on laser and vision data. After<br />
collecting a 3D laser scan, Nao detects steps of a staircase and climbs the staircase using visual<br />
information.<br />
SP12 – More cowbell! A musical ensemble with the NAO thereminist (*)<br />
Angelica Lim, Takeshi Mizumoto, Takuma Otsuka, Tatsuhiko Itohara, Kazuhiro Nakadai 1,2 , Tetsuya<br />
Ogata, Hiroshi G. Okuno<br />
Kyoto University; 1 Honda Research Institute, Wako, Japan; 2 Tokyo Institute of Technology, Japan<br />
Platform: Nao<br />
Abstract: We present the first live performance of our interactive music robot: Nao plays the<br />
theremin and listens to humans to stay in sync. Demo attendees are invited to join by playing<br />
cowbell or maracas.<br />
SP13 – Model-based Visual Servoing Tasks on Nao Robot<br />
A. Abou Moughlbay 1 , J. J. Sorribes 2 , E. Cervera 2 , P. Martinet 1<br />
1<br />
LASMEA, Blaise Pascal University, France<br />
2<br />
RobInLab, Jaume-I University, Spain<br />
Platform: Nao<br />
Abstract: Model-based visual servoing is implemented on the Nao robot. Based on geometric<br />
models, the robot performs localization, tracking and grasping of objects in a semi-structured<br />
environment.<br />
Open Research Demonstrations<br />
The goal of the open research demonstration session is to give participants an open forum<br />
to present their latest developments on robotic systems and software. The accepted<br />
demonstrations for this session are:<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
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OR1 – Kilobot: A Low Cost Scalable Robot System for Collective Behaviors (*)<br />
Michael Rubenstein, Radhika Nagpal<br />
Harvard University, Cambridge, USA<br />
Abstract: The Kilobot is a low cost, easy to use robot, which is designed for testing swarm<br />
algorithms on hundreds to thousands of robots. We will demo robot operations, and sample<br />
behaviors on 100 Kilobots.<br />
OR2 – DisplaySwarm: A robot swarm displaying images (*)<br />
Javier Alonso-Mora 1,2 , Andreas Breitenmoser 1 , Martin Rufli 1 , Stefan Haag, Gilles Caprari 3 , Roland<br />
Siegwart 1 , Paul Beardsley 2<br />
1 ETH Zurich; 2 Disney Research Zurich; 3 CGTronic Lugano, Switzerland<br />
Abstract: DisplaySwarm represents images in a novel way using small mobile robots with<br />
controllable colored illumination. The work addresses research questions in collision avoidance and<br />
pattern formation.<br />
OR3 – The DARPA ARM Robot: Available at Your Desk<br />
Gill Pratt 1 , Jim Pippine 2 , Andrew Mor 3 , Natalie Salaets 4<br />
1 DARPA DSO; 2 Golden Knight Technologies; 3 RE2, 4 System Planning Corporation, USA<br />
Abstract: The DARPA ARM demonstration features both the publicly available ARM robot simulator<br />
and a real remote robot. Visitors interact with the simulator to perform various tasks, then watch the<br />
real remote robot do the same tasks.<br />
OR4 – Flying the Pixhawk MAV - A Computer Vision Controlled Quadrotor (*)<br />
Lorenz Meier, Petri Tanskanen, Lionel Heng, Gim-Hee Lee, Friedrich Fraundorfer, Marc Pollefeys<br />
ETH Zurich, Switzerland<br />
Abstract: The Pixhawk micro air vehicle is an autonomous flying robot equipped with cameras and<br />
onboard computer. It will fly based on onboard visual localization and perform the task of object<br />
tracking and pattern recognition.<br />
OR5 – Proxy Method for Fast Haptic Rendering from Time Varying Point Clouds<br />
Fredrik Ryden, Sina Nia Kosari, Howard Jay Chizeck<br />
University of Washington, Seattle, USA<br />
Abstract: This demonstration will allow users to directly “feel” physical objects imaged by a Kinect.<br />
They will be able to directly experience haptic rendering of both fixed and moving objects in real-time.<br />
OR6 – Bio-inspired vertebral column, compliance and semi-passive dynamics in a<br />
lightweight humanoid robot (*)<br />
Olivier Ly, Matthieu Lapeyre, Pierre-Yves Oudeyer<br />
LABRI-INRIA, Talence, France<br />
Abstract: We demonstrate the use of compliance and vertebral column in humanoid locomotion.<br />
OR7 – A Robotic System for Needle Steering<br />
Ann Majewicz 1 , John Swensen 2 , Tom Wedlick 2 , Kyle Reed 3 , Ron Alterovitz 4 , Vinutha Kallem 5 ,<br />
Wooram Park 6 , Animesh Garg, Gregory Chirikjian 2 , Ken Goldberg 7 , Animesh Garg 7 , Noah Cowan 2 ,<br />
and Allison Okamura 1<br />
1 Stanford Univ.; 2 John Hopkins Univ.; 3 Univ. of South Florida; 4 University of North Carolina at<br />
Chapel Hill; 5 Univ. of Pennsylvania; 6 Univ. of Texas at Dallas, USA; 7 UC Berkeley;<br />
Abstract: A live demonstration of robotic needle steering in artificial tissue, as well as videos and<br />
posters about models and simulations, path planners, controllers, and integration with medical<br />
imaging.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
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OR8 – The r-one: A Low-Cost Robot for Research, Education, and Outreach<br />
A. Lynch, J. McLurkin<br />
Rice University, Houston, USA<br />
Abstract: We present the r-one: An advanced, low-cost robot for research and education. Come<br />
see autonomous multi-robot exploration, drive a mini-swarm, and write your program at<br />
programming stations.<br />
OR9 – Ballbot Rezero<br />
Simon Doessegger, Peter Fankhauser, Corsin Gwerder, Jonathan Huessy, Jerome Kaeser,<br />
Thomas Kammermann, Lukas Limacher, Michael Neunert, Francis Colas, Cedric Pradalier, Roland<br />
Siegwart<br />
ETH Zurich, Switzerland<br />
Abstract: Ballbots are able to balance and drive on a single sphere. Designed for high agility,<br />
organic motion and human interaction, Ballbot Rezero exploits its inherent, unstable dynamics.<br />
OR10 – Generic Perception for Manipulation<br />
Manuel Brucker 1 , Chavdar Papazov 2 , Simon Leonard 3 , Darius Burschka 2 , Gregory D. Hager 3 , Tim<br />
Bodenmueller 1<br />
1 DLR, Germany; 2 TU Munich, Germany; 3 John Hopkins University, Baltimore, USA<br />
Abstract: Our scene parsing algorithm leverages physical constraints to enhance data fitting over<br />
time and space. Our demonstration will show a robot manipulating complex scenes composed of<br />
various objects.<br />
Symposium<br />
A special symposium, Robot Demonstrations, will highlight twelve of the best demo<br />
proposals, with short descriptions of the demos by the authors.<br />
When: Monday, September 26, <strong>2011</strong>, 16:00<br />
Where: Continental Ballroom 4 (Session MoBT4)<br />
List of talks<br />
SP2 – Aquila - The Cognitive Robotics Toolkit<br />
SP3 – General Purpose Service Robot<br />
SP4 – Active Perception Planning on the Care-o-Bot platform<br />
SP6 – Using Mobile Manipulation to solve a Household Task<br />
SP7 – Demonstration of iTaSC as a unified framework for task specification, control, and<br />
coordination for mobile manipulation<br />
SP10 – Haptic coupling with augmented feedback between the KUKA youBot and the PR2 robot<br />
arms<br />
SP11 – Detecting and Climbing Stairs with a Laser-equipped Nao Humanoid<br />
SP12 – More cowbell! A musical ensemble with the NAO thereminist<br />
OR1 – Kilobot: A Low Cost Scalable Robot System for Collective Behaviors<br />
OR2 – DisplaySwarm: A robot swarm displaying images<br />
OR4 – Flying the Pixhawk MAV - A Computer Vision Controlled Quadrotor<br />
OR6 – Bio-inspired vertebral column, compliance and semi-passive dynamics in a lightweight<br />
humanoid robot<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
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Floorplan<br />
Exhibition<br />
The Exhibition venue is the Golden Gate room at the Lobby level of the Hilton hotel.<br />
Schedule<br />
Exhibitors will be active during the following days:<br />
Tuesday, September 27, <strong>2011</strong> 9:00 – 17:00<br />
Wednesday, September 28, <strong>2011</strong> 9:00 – 17:00<br />
Thursday, September 29, <strong>2011</strong> 9:00 – noon<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
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Exhibitors<br />
Platinum Corporate Sponsors exhibiting:<br />
Bosch Research and Technology Center www.boschresearch.com<br />
Honda Research Institute www.honda-ri.com/HRI_Us<br />
KUKA Robotics Corp. www.kuka.com<br />
SRI International www.sri.com<br />
Gold Corporate Sponsors exhibiting:<br />
Willow Garage, Inc. www.willowgarage.com<br />
Silver Corporate Sponsors exhibiting:<br />
Aldebaran Robotics www.aldebaran-robotics.com<br />
Google, Inc. research.google.com<br />
Intuitive Surgical, Inc. www.intuitivesurgical.com<br />
Shunk Intek, Inc. www.us.schunk.com<br />
Additional Exhibitors:<br />
Adept MobileRobots www.adept.com<br />
Ascending Technologies, Inc. www.asctec.de<br />
Barobo, Inc. www.barobo.com<br />
Barrett Technology, Inc. www.barrett.com<br />
BioRob GmbH www.biorob.de<br />
Butterfly Haptics, LLC www.butterflyhaptics.com<br />
Coroware, Inc. www.coroware.com<br />
DARPA ARM Outreach www.thearmrobot.com<br />
Festo Didactic www.festo.us<br />
Hokuyo Automatic Co., Ltd. www.hokuyo-aut.jp<br />
John Wiley & Sons www.wiley.com<br />
Kawada Industries, Inc. www.kawada.co.jp<br />
National Instruments Corporation www.ni.com<br />
RoadNarrows Robotics www.roadnarrows.com<br />
RobotCub Consortium (iCub) www.icub.org<br />
Robotis, Inc. www.robotis.com<br />
SimLab Co., Ltd www.simlab.co.kr<br />
Skybotix AG www.skybotix.com<br />
Springer Science+Business Media www.springer.com<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
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<strong>IROS</strong> <strong>2011</strong> Corporate Sponsors<br />
We acknowledge the support of the following Corporate Sponsors to the <strong>2011</strong> IEEE/RSJ<br />
International Conference on Intelligent Robots and Systems.<br />
Platinum<br />
www.boschresearch.com www.honda-ri.com/HRI_Us www.kuka.com www.sri.com<br />
Gold<br />
Silver<br />
www.abb.com/robots www.willowgarage.com<br />
www.aldebaran-robotics.com research.google.com www.intuitivesurgical.com www.us.schunk.com<br />
At the end of this Digest, you will find ads from the above Corporate Sponsors.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
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<strong>IROS</strong> <strong>2011</strong> <strong>Awards</strong><br />
<strong>IROS</strong> Harashima Award<br />
To honor Professor Fumio Harashima, the Honorary Founding Chair of <strong>IROS</strong> conferences,<br />
by recognizing outstanding contributions of an individual of the <strong>IROS</strong> community, who has<br />
pioneered activities in robotics and intelligent systems.<br />
NTF Award for Entertainment Robots and Systems<br />
Started in 2007, this award is to encourage research and development of "entertainment<br />
robots and systems", and new technologies for future entertainment. Sponsored by the<br />
New Technology Foundation; Certificate + USD 1,000.<br />
JTCF Novel Technology Paper Award for Amusement Culture<br />
This award recognizes practical technology contributing to toys, toy models, and amusement<br />
culture. Sponsored by the Japan Toy Culture Foundation; Certificate + JPY 100,000.<br />
RoboCup Best Paper Award<br />
Sponsored by The RoboCup Federation; Certificate + USD 500.<br />
<strong>IROS</strong> CoTeSys Cognitive Robotics Best Paper Award<br />
Started in 2010, this award is to promote interdisciplinary research on cognition for<br />
technical systems (CoTeSys) and advancements of cognitive robotics in industry, home<br />
applications, and daily life. Sponsored by the German Cluster of Excellence, CoTeSys;<br />
Certificate + USD 1,000.<br />
Best Application Paper Award<br />
Sponsored by the Institute of Control, Robotics, and Systems (ICROS); Certificate + USD<br />
1,000.<br />
<strong>IROS</strong> <strong>2011</strong> Best Paper Award<br />
To recognize the most outstanding paper presented at the Conference, based on the<br />
quality of the contribution, of the written paper, and of the oral presentation. Sponsored by<br />
RSJ and SICE; Certificate + USD 2,000.<br />
<strong>IROS</strong> <strong>2011</strong> Best Student Paper Award<br />
To recognize the most outstanding paper authored primarily and presented by a student at<br />
the Conference, based on the quality of the contribution, of the written paper and of the oral<br />
presentation. Sponsored by the <strong>IROS</strong> <strong>2011</strong> Conference; Certificate + USD 1,000.<br />
All award winners will be announced during the <strong>Awards</strong> Lunch on Thursday, September 29.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xxxvii–
Welcome Reception<br />
Social Events<br />
A welcome reception will be offered to all <strong>IROS</strong> <strong>2011</strong> participants on Monday,<br />
September 26, 18:30-20:00. After the end of the sessions, we will meet in the Continental<br />
Ballroom.<br />
Dinner Cruise on the Bay<br />
San Francisco's skyline is most spectacular when seen from the water. On Tuesday,<br />
September 27, we will take you on an afternoon and dinner cruise on the San Francisco<br />
Belle, a beautiful three-leveled 292-foot sternwheeler with a unique Art Nouveau style.<br />
During the cruise, you will have ample opportunities to enjoy the views of the City and<br />
many of its most famous sights, such as Alcatraz, Angel Island, the Embarcadero,<br />
Fisherman's Wharf, Coit Tower, the skyscrapers of the Financial District, and – of course –<br />
the Golden Gate Bridge. Transfer busses will leave at the Hilton hotel at 16:00, and we<br />
expect to be back at the hotel at 22:00.<br />
Gold Lunch<br />
On Wednesday, September 28, at noon, a lunch for all Graduates of the Last Decade<br />
(GOLD) will be organized at the Continental Ballroom level. This luncheon was initiated<br />
within the RAS Technical Activities Board as a mean to let graduates be aware of what the<br />
society has to offer and to network with each other. The opportunity is also used to present<br />
the structure of the society and introduce the various Technical Committees forming TAB.<br />
RAS members will have first priority. If you are not yet a member, you can sign up now at<br />
www.ieee-ras.org.<br />
Lunch with Leaders<br />
This event is organized by the IEEE Robotics and Automation Society (RAS) and will take<br />
place in parallel to the GOLD lunch on Wednesday, September 28, at noon. Lunch with<br />
Leaders (LwL) was initiated by the Student Activities Committee with the aim to provide<br />
students with an opportunity to get in contact with leaders and get advice and mentoring on<br />
their career and research. RAS members will have first priority.<br />
Evening at the Museum with Picasso<br />
This year, we have discovered a very special venue for the conference banquet. It will be<br />
held on the evening of Wednesday, September 28, in the de Young Museum. The<br />
museum is situated in Golden Gate Park, which extends all the way to Ocean Beach, and<br />
which features many other attractions such as the Japanese Tea Garden, the Botanical<br />
Garden, and the California Academy of Sciences, to name but a few. As a special highlight,<br />
we will have exclusive access to the Picasso Exhibition. Transfer busses will leave at the<br />
Hilton hotel at 18:00, and we expect to be back at the hotel at 22:00.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
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<strong>Awards</strong> Ceremony<br />
All <strong>IROS</strong> <strong>2011</strong> participants are invited to attend the <strong>Awards</strong> Ceremony in the Continental<br />
Ballroom on Thursday, September 29, at 12:30. All conference participants are welcome<br />
to join while the eight <strong>IROS</strong> <strong>2011</strong> Conference <strong>Awards</strong> will be presented.<br />
Farewell Reception<br />
At the end of the conference, a reception will gather all <strong>IROS</strong> <strong>2011</strong> participants in the<br />
Continental Ballroom on Thursday, September 29, 18:30-20:00.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
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Map of Conference Venue<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
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Conference Overview<br />
<strong>IROS</strong> <strong>2011</strong> features three types of sessions. In addition to the customary technical tracks<br />
of regular sessions, there are specially organized symposia, novel interactive sessions,<br />
and five forums.<br />
Regular Sessions<br />
These consist of eight papers. Five papers are presented in regular (15 minute) talk format.<br />
Three papers are allotted short (5 minute) presentations. Each of these “teaser” talks<br />
previews the corresponding paper and invites the audience to the full presentation of the<br />
paper at the subsequent (90 minute) interactive session in the special presentation area<br />
equipped with large screens.<br />
Symposia<br />
Symposia are invited sessions that are focused on a specific subfield. Organized by<br />
renowned researchers, they are a celebration of 50 years of robotics. All symposia consist<br />
of papers that have undergone the regular review process. In addition, a longer invited talk<br />
(semi-plenary) is included in some symposia.<br />
Interactive sessions<br />
These sessions allow a carefully selected number of papers to be presented in an<br />
interactive manner on large flat screens. Up to 30 papers are presented in parallel to small<br />
groups of interested attendees during each session, in order to foster a lively exchange of<br />
ideas between participants. Before being presented in an interactive session, every such<br />
paper is previewed in the form of a short (5 minute) “teaser” talk in a regular session. It is<br />
important to note that papers presented during interactive sessions were reviewed and are<br />
published exactly as regular papers. The Senior Program Committee selected those<br />
papers for interactive sessions that are most likely to benefit from the special presentation<br />
format.<br />
Industrial Forums<br />
<strong>IROS</strong> <strong>2011</strong> features three industrial forums, each one session long. Each forum includes<br />
representatives from robotics companies making short presentations and engaging in a<br />
moderated panel discussion among themselves and with the audience.<br />
Robots: The New Commercial Platforms<br />
This forum focuses on recent and emerging robotic platforms. Participants from industry<br />
discuss recent and projected advances in robotic technology with a commentary on<br />
emerging applications. The forum is moderated by Professor Rüdiger Dillmann.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
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Robots: The Next Generation<br />
This forum focuses on next generation robotic platforms, new business models for robotics,<br />
and the role of open software. Participants from industry discuss advances in robotic<br />
technology with an emphasis on software and applications. The forum is moderated by<br />
Steve Cousins.<br />
Medical Robotics<br />
This forum focuses on the rapidly growing field of medical robotics. In synergy with the<br />
Symposia on medical robotics at <strong>IROS</strong> <strong>2011</strong>, this forum features industry experts<br />
discussing and presenting cutting edge commercial advances in the field. The forum is<br />
moderated by Professor Paolo Dario.<br />
Forums<br />
Robotics: Beyond the Horizon<br />
In addition to the industrial forums, <strong>IROS</strong> <strong>2011</strong> features a special ‘Blue Sky’ forum on the<br />
future of robotics. Participants from academia, government, and industry present their<br />
visions for the future of the field. The forum is moderated by Professor Hirochika Inoue.<br />
On Robotics Conferences: A Town Hall Meeting<br />
<strong>IROS</strong> <strong>2011</strong> features a town hall meeting forum. The forum is an opportunity for conference<br />
attendees to provide feedback about the conference, and for future conference organizers<br />
to briefly present their vision of the meetings they are planning. The forum is moderated by<br />
Professor Peter Corke.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
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<strong>IROS</strong> <strong>2011</strong> Technical Program at a Glance: Monday, September 26, <strong>2011</strong><br />
Track T1 Track T2 Track T3 Track T4 Track T5 Track T6 Track T7 Track T8 Track T9 Track T10<br />
14:00-15:30<br />
MoAT1<br />
Continental<br />
Parlor 1<br />
Microsensing<br />
16:00-17:30<br />
MoBT1<br />
Continental<br />
Parlor 1<br />
Micromanipulation<br />
14:00-15:30<br />
MoAT2<br />
Continental<br />
Parlor 2<br />
Localization<br />
16:00-17:30<br />
MoBT2<br />
Continental<br />
Parlor 2<br />
Localization with<br />
Constraints<br />
14:00-15:30<br />
MoAT3<br />
Continental<br />
Parlor 3<br />
Symposium:<br />
Robot Audition:<br />
Active Audition<br />
16:00-17:30<br />
MoBT3<br />
Continental<br />
Parlor 3<br />
Symposium:<br />
Robot Audition:<br />
From Sound<br />
Source<br />
Localization to<br />
Automatic Speech<br />
Recognition<br />
14:00-15:30<br />
MoAT4<br />
Continental<br />
Ballroom 4<br />
Symposium:<br />
Telerobotics<br />
16:00-17:30<br />
MoBT4<br />
Continental<br />
Ballroom 4<br />
Symposium:<br />
Robot<br />
Demonstrations<br />
14:00-15:30<br />
MoAT5<br />
Continental<br />
Ballroom 5<br />
Symposium:<br />
Bio-Inspired<br />
Robotics I<br />
16:00-17:30<br />
MoBT5<br />
Continental<br />
Ballroom 5<br />
Symposium:<br />
Bio-Inspired<br />
Robotics II<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xliii–<br />
14:00-15:30<br />
MoAT6<br />
Continental<br />
Ballroom 6<br />
Symposium:<br />
Field Robotics I<br />
16:00-17:30<br />
MoBT6<br />
Continental<br />
Ballroom 6<br />
Symposium:<br />
Field Robotics II<br />
14:00-15:30<br />
MoAT7<br />
Continental<br />
Parlor 7<br />
Wheeled Robots<br />
16:00-17:30<br />
MoBT7<br />
Continental<br />
Parlor 7<br />
Teleoperation<br />
14:00-15:30<br />
MoAT8<br />
Continental<br />
Parlor 8<br />
Learning<br />
Parameterized<br />
Policies<br />
16:00-17:30<br />
MoBT8<br />
Continental<br />
Parlor 8<br />
Learning for<br />
Control<br />
14:00-15:30<br />
MoAT9<br />
Continental<br />
Parlor 9<br />
Novel Actuators I<br />
16:00-17:30<br />
MoBT9<br />
Continental<br />
Parlor 9<br />
Novel Actuators II<br />
16:00-17:30<br />
MoBPT10<br />
Golden Gate<br />
Room<br />
Interactive I
<strong>IROS</strong> <strong>2011</strong> Technical Program at a Glance: Tuesday, September 27, <strong>2011</strong><br />
Track T1 Track T2 Track T3 Track T4 Track T5 Track T6 Track T7 Track T8 Track T9 Track T10<br />
08:00-09:30<br />
TuAT1<br />
Continental<br />
Parlor 1<br />
Object<br />
Recognition,<br />
Segmentation,<br />
and Detection<br />
10:00-11:30<br />
TuBT1<br />
Continental<br />
Parlor 1<br />
Perception,<br />
Saliency and<br />
Novelty<br />
14:00-15:30<br />
TuCT1<br />
Continental<br />
Parlor 1<br />
Object Detection<br />
& Collision<br />
Avoidance<br />
08:00-09:30<br />
TuAT2<br />
Continental<br />
Parlor 2<br />
Simultaneous<br />
Localization and<br />
Mapping<br />
10:00-11:30<br />
TuBT2<br />
Continental<br />
Parlor 2<br />
Semantic SLAM &<br />
Loop Closure<br />
14:00-15:30<br />
TuCT2<br />
Continental<br />
Parlor 2<br />
Motion Estimation,<br />
Mapping & SLAM<br />
08:00-09:30<br />
TuAT3<br />
Continental<br />
Parlor 3<br />
Symposium:<br />
Microrobotics<br />
I<br />
10:00-11:30<br />
TuBT3<br />
Continental<br />
Parlor 3<br />
Symposium:<br />
Microrobotics<br />
II<br />
14:00-15:30<br />
TuCT3<br />
Continental<br />
Parlor 3<br />
Symposium:<br />
Microrobotics<br />
III<br />
08:00-09:30<br />
TuAT4<br />
Continental<br />
Ballroom 4<br />
Industrial Forum:<br />
Robots: The New<br />
Commercial<br />
Platforms<br />
10:00-11:30<br />
TuBT4<br />
Continental<br />
Ballroom 4<br />
Industrial Forum:<br />
Robots: The Next<br />
Generation<br />
14:00-15:30<br />
TuCT4<br />
Continental<br />
Ballroom 4<br />
Forum:<br />
Robotics: Beyond<br />
the Horizon<br />
08:00-09:30<br />
TuAT5<br />
Continental<br />
Ballroom 5<br />
Symposium:<br />
Medical Robotics<br />
I<br />
10:00-11:30<br />
TuBT5<br />
Continental<br />
Ballroom 5<br />
Symposium:<br />
Medical Robotics<br />
II<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xliv–<br />
08:00-09:30<br />
TuAT6<br />
Continental<br />
Ballroom 6<br />
Symposium:<br />
Grasping and<br />
Manipulation:<br />
Control and<br />
Learning<br />
10:00-11:30<br />
TuBT6<br />
Continental<br />
Ballroom 6<br />
Symposium:<br />
Grasping and<br />
Manipulation:<br />
Mechanics and<br />
Design<br />
12:30-13:45 TuPL<br />
Continental Ballroom<br />
Plenary I: Design<br />
14:00-15:30<br />
TuCT5<br />
Continental<br />
Ballroom 5<br />
Symposium:<br />
Medical Robotics<br />
III<br />
14:00-15:30<br />
TuCT6<br />
Continental<br />
Ballroom 6<br />
Symposium:<br />
Grasping and<br />
Manipulation:<br />
Grasp Planning<br />
and Quality<br />
08:00-09:30<br />
TuAT7<br />
Continental<br />
Parlor 7<br />
Software<br />
Architectures &<br />
Frameworks<br />
10:00-11:30<br />
TuBT7<br />
Continental<br />
Parlor 7<br />
Contact and<br />
Deformation<br />
14:00-15:30<br />
TuCT7<br />
Continental<br />
Parlor 7<br />
Mechanisms:<br />
Actuator Design<br />
08:00-09:30<br />
TuAT8<br />
Continental<br />
Parlor 8<br />
Bio-Inspired &<br />
Biomimetic Robots<br />
10:00-11:30<br />
TuBT8<br />
Continental<br />
Parlor 8<br />
Biomimetic<br />
Limbed Robots<br />
14:00-15:30<br />
TuCT8<br />
Continental<br />
Parlor 8<br />
Reptile & Fish-<br />
Inspired Robots<br />
08:00-09:30<br />
TuAT9<br />
Continental<br />
Parlor 9<br />
Probabilistic<br />
Exploration and<br />
Coverage<br />
10:00-11:30<br />
TuBT9<br />
Continental<br />
Parlor 9<br />
Perceptual<br />
Learning<br />
14:00-15:30<br />
TuCT9<br />
Continental<br />
Parlor 9<br />
Novel Sensors<br />
08:00-09:30<br />
TuAPT10<br />
Golden Gate<br />
Room<br />
Interactive II<br />
10:00-11:30<br />
TuBPT10<br />
Golden Gate<br />
Room<br />
Interactive III<br />
14:00-15:30<br />
TuCPT10<br />
Golden Gate<br />
Room<br />
Interactive IV
<strong>IROS</strong> <strong>2011</strong> Technical Program at a Glance: Wednesday, September 28, <strong>2011</strong><br />
Track T1 Track T2 Track T3 Track T4 Track T5 Track T6 Track T7 Track T8 Track T9 Track T10<br />
08:00-09:30<br />
WeAT1<br />
Continental<br />
Parlor 1<br />
HRI: Modeling<br />
Human Behavior<br />
10:00-11:30<br />
WeBT1<br />
Continental<br />
Parlor 1<br />
Socially Assistive<br />
Robots<br />
14:00-15:30<br />
WeCT1<br />
Continental<br />
Parlor 1<br />
Human-Robot<br />
Interaction and<br />
Cooperation<br />
16:00-17:30<br />
WeDT1<br />
Continental<br />
Parlor 1<br />
Human-Robot<br />
Collaboration<br />
08:00-09:30<br />
WeAT2<br />
Continental<br />
Parlor 2<br />
Models and<br />
Representation<br />
10:00-11:30<br />
WeBT2<br />
Continental<br />
Parlor 2<br />
Estimation &<br />
Sensor Fusion<br />
14:00-15:30<br />
WeCT2<br />
Continental<br />
Parlor 2<br />
Pose Estimation<br />
& Visual Tracking<br />
16:00-17:30<br />
WeDT2<br />
Continental<br />
Parlor 2<br />
Recognition &<br />
Prediction of<br />
Motion<br />
08:00-09:30<br />
WeAT3<br />
Continental<br />
Parlor 3<br />
Medical Robotics:<br />
Tracking &<br />
Detection<br />
10:00-11:30<br />
WeBT3<br />
Continental<br />
Parlor 3<br />
Surgical Robotics<br />
14:00-15:30<br />
WeCT3<br />
Continental<br />
Parlor 3<br />
Robot Safety<br />
16:00-17:30<br />
WeDT3<br />
Continental<br />
Parlor 3<br />
Haptic Rendering<br />
& Object<br />
Recognition<br />
08:00-09:30<br />
WeAT4<br />
Continental<br />
Ballroom 4<br />
08:00-09:30<br />
WeAT5<br />
Continental<br />
Ballroom 5<br />
Symposium: Symposium:<br />
Haptics Interfaces Robot Motion<br />
for the Fingertip, Planning:<br />
Hand, and Arm Achievements and<br />
Emerging<br />
Approaches<br />
10:00-11:30<br />
WeBT4<br />
Continental<br />
Ballroom 4<br />
Symposium:<br />
Hardware and<br />
Software Design<br />
for Haptic Systems<br />
14:00-15:30<br />
WeCT4<br />
Continental<br />
Ballroom 4<br />
Symposium:<br />
Haptic Feedback<br />
and System<br />
Evaluation<br />
16:00-17:30<br />
WeDT4<br />
Continental<br />
Ballroom 4<br />
Industrial Forum:<br />
Medical Robotics<br />
10:00-11:30<br />
WeBT5<br />
Continental<br />
Ballroom 5<br />
Symposium:<br />
Foundations and<br />
Future Prospects<br />
of Sampling-Based<br />
Motion Planning<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xlv–<br />
08:00-09:30<br />
WeAT6<br />
Continental<br />
Ballroom 6<br />
Symposium:<br />
Aerial Robotics:<br />
Estimation,<br />
Perception and<br />
Control<br />
10:00-11:30<br />
WeBT6<br />
Continental<br />
Ballroom 6<br />
Symposium:<br />
Aerial Robotics:<br />
Control and<br />
Planning<br />
12:30-13:45 WePL<br />
Continental Ballroom<br />
Plenary II: BioRobotics<br />
14:00-15:30<br />
WeCT5<br />
Continental<br />
Ballroom 5<br />
Symposium:<br />
Symbolic<br />
Approaches to<br />
Motion Planning<br />
and Control<br />
16:00-17:30<br />
WeDT5<br />
Continental<br />
Ballroom 5<br />
Symposium:<br />
Robot Motion<br />
Planning: New<br />
Frameworks and<br />
High Performance<br />
14:00-15:30<br />
WeCT6<br />
Continental<br />
Ballroom 6<br />
Symposium:<br />
Marine Robotics:<br />
Platforms and<br />
Applications<br />
16:00-17:30<br />
WeDT6<br />
Continental<br />
Ballroom 6<br />
Symposium:<br />
Marine Robotics:<br />
Control and<br />
Planning<br />
08:00-09:30<br />
WeAT7<br />
Continental<br />
Parlor 7<br />
Robot Walking<br />
10:00-11:30<br />
WeBT7<br />
Continental<br />
Parlor 7<br />
Passive Walking<br />
& Leg-Wheeled<br />
Robots<br />
14:00-15:30<br />
WeCT7<br />
Continental<br />
Parlor 7<br />
Humanoid<br />
Control<br />
16:00-17:30<br />
WeDT7<br />
Continental<br />
Parlor 7<br />
Tracking & Gait<br />
Analysis<br />
08:00-09:30<br />
WeAT8<br />
Continental<br />
Parlor 8<br />
Networked Robots<br />
10:00-11:30<br />
WeBT8<br />
Continental<br />
Parlor 8<br />
Multirobot<br />
Systems:<br />
Rendezvous &<br />
Task Switching<br />
14:00-15:30<br />
WeCT8<br />
Continental<br />
Parlor 8<br />
Multirobot<br />
Planning<br />
16:00-17:30<br />
WeDT8<br />
Continental<br />
Parlor 8<br />
Multirobot<br />
Coordination &<br />
Modular Robots<br />
08:00-09:30<br />
WeAT9<br />
Continental<br />
Parlor 9<br />
Vision: From<br />
Features to<br />
Applications<br />
10:00-11:30<br />
WeBT9<br />
Continental<br />
Parlor 9<br />
Visual Servoing<br />
14:00-15:30<br />
WeCT9<br />
Continental<br />
Parlor 9<br />
Visual & Multi-<br />
Sensor Calibration<br />
16:00-17:30<br />
WeDT9<br />
Continental<br />
Parlor 9<br />
Calibration &<br />
Identification<br />
08:00-09:30<br />
WeAPT10<br />
Golden Gate<br />
Room<br />
Interactive V<br />
10:00-11:30<br />
WeBPT10<br />
Golden Gate<br />
Room<br />
Interactive VI<br />
14:00-15:30<br />
WeCPT10<br />
Golden Gate<br />
Room<br />
Interactive VII<br />
16:00-17:30<br />
WeDPT10<br />
Golden Gate<br />
Room<br />
Interactive VIII
<strong>IROS</strong> <strong>2011</strong> Technical Program at a Glance: Thursday, September 29, <strong>2011</strong><br />
Track T1 Track T2 Track T3 Track T4 Track T5 Track T6 Track T7 Track T8 Track T9 Track T10<br />
08:00-09:30<br />
ThAT1<br />
Continental<br />
Parlor 1<br />
Force and<br />
Stiffness Control<br />
10:00-11:30<br />
ThBT1<br />
Continental<br />
Parlor 1<br />
Control for<br />
Manipulation<br />
& Grasping<br />
14:45-16:15<br />
ThCT1<br />
Continental<br />
Parlor 1<br />
Manipulation<br />
08:00-09:30<br />
ThAT2<br />
Continental<br />
Parlor 2<br />
Range and<br />
RGB-D Sensing<br />
10:00-11:30<br />
ThBT2<br />
Continental<br />
Parlor 2<br />
Mapping<br />
14:45-16:15<br />
ThCT2<br />
Continental<br />
Parlor 2<br />
Industrial Robots<br />
16:45-17:45<br />
ThDT1<br />
Continental Balllroom<br />
Forum:<br />
On Robotics Conferences:<br />
A Town Hall Meeting<br />
08:00-09:30<br />
ThAT3<br />
Continental<br />
Parlor 3<br />
Discrete &<br />
Kinodynamic<br />
Planning<br />
10:00-11:30<br />
ThBT3<br />
Continental<br />
Parlor 3<br />
08:00-09:30<br />
ThAT4<br />
Continental<br />
Ballroom 4<br />
08:00-09:30<br />
ThAT5<br />
Continental<br />
Ballroom 5<br />
Symposium: Symposium:<br />
Stochasticity in Humanoid<br />
Robotics and Robotics and<br />
Biological Systems Biped Locomotion<br />
I<br />
Randomized Symposium:<br />
Planning & Stochasticity in<br />
Kinematic Control Robotics and<br />
Biological Systems<br />
II<br />
14:45-16:15<br />
ThCT3<br />
Continental<br />
Parlor 3<br />
Marine Systems<br />
I<br />
16:45-17:45<br />
ThDT3<br />
Continental<br />
Parlor 3<br />
Marine Systems<br />
II<br />
10:00-11:30<br />
ThBT4<br />
Continental<br />
Ballroom 4<br />
14:45-16:15<br />
ThCT4<br />
Continental<br />
Ballroom 4<br />
Symposium:<br />
(Self-)assembly<br />
from the Nano to<br />
the Macro Scale:<br />
State of the Art<br />
and Future<br />
Directions<br />
16:45-17:45<br />
ThDT4<br />
Continental<br />
Parlor 2<br />
Novel System<br />
Designs:<br />
Locomotion<br />
10:00-11:30<br />
ThBT5<br />
Continental<br />
Ballroom 5<br />
Symposium:<br />
Humanoid<br />
Technologies<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–xlvi–<br />
08:00-09:30<br />
ThAT6<br />
Continental<br />
Ballroom 6<br />
Symposium:<br />
Robots that<br />
Can See I<br />
10:00-11:30<br />
ThBT6<br />
Continental<br />
Ballroom 6<br />
Symposium:<br />
Robots that<br />
Can See II<br />
13:30-14:30 ThPT11<br />
Continental Ballroom<br />
Plenary III: Self-Driving Cars<br />
14:45-16:15<br />
ThCT5<br />
Continental<br />
Ballroom 5<br />
Symposium:<br />
Humanoid<br />
Applications<br />
16:45-17:45<br />
ThDT5<br />
Continental<br />
Parlor 1<br />
Climbing &<br />
Brachiation<br />
14:45-16:15<br />
ThCT6<br />
Continental<br />
Ballroom 6<br />
Symposium:<br />
Computer Vision<br />
for Robotics<br />
16:45-17:45<br />
ThDT6<br />
Continental<br />
Parlor 9<br />
Novel System<br />
Designs: Sensing<br />
and Manipulation<br />
08:00-09:30<br />
ThAT7<br />
Continental<br />
Parlor 7<br />
Mechanisms: Joint<br />
Design<br />
10:00-11:30<br />
ThBT7<br />
Continental<br />
Parlor 7<br />
Medical Robots<br />
& Systems<br />
14:45-16:15<br />
ThCT7<br />
Continental<br />
Parlor 7<br />
Exoskeleton<br />
Robots & Gait<br />
Rehabilitation<br />
16:45-17:45<br />
ThDT7<br />
Continental<br />
Parlor 7<br />
Medical Robotics:<br />
Motion Planning &<br />
State Estimation<br />
08:00-09:30<br />
ThAT8<br />
Continental<br />
Parlor 8<br />
Autonomous<br />
Vehicles<br />
10:00-11:30<br />
ThBT8<br />
Continental<br />
Parlor 8<br />
Search & Rescue<br />
Robots<br />
14:45-16:15<br />
ThCT8<br />
Continental<br />
Parlor 8<br />
Aerial Robots:<br />
Navigation,<br />
Tracking &<br />
Landing<br />
16:45-17:45<br />
ThDT8<br />
Continental<br />
Parlor 8<br />
Aerial Robots<br />
08:00-09:30<br />
ThAT9<br />
Continental<br />
Parlor 9<br />
Towards<br />
Anthropomimetic<br />
Robots<br />
10:00-11:30<br />
ThBT9<br />
Continental<br />
Parlor 9<br />
Intelligent<br />
Transportation<br />
Systems<br />
(Automotive)<br />
14:45-16:15<br />
ThCT9<br />
Continental<br />
Parlor 9<br />
Swarms and<br />
Flocks<br />
08:00-09:30<br />
ThAPT10<br />
Golden Gate<br />
Room<br />
Interactive IX<br />
10:00-11:30<br />
ThBPT10<br />
Golden Gate<br />
Room<br />
Interactive X<br />
14:45-16:15<br />
ThCPT10<br />
Golden Gate<br />
Room<br />
Interactive XI<br />
16:45-17:45<br />
ThDPT10<br />
Golden Gate<br />
Room<br />
Interactive XII
14:00-15:30<br />
Technical Program Digest<br />
Session MoAT1 ⎯⎯ Microsensing<br />
Session MoAT2 ⎯⎯ Localization<br />
Session MoAT3 ⎯⎯ Symposium: Robot Audition: Active Audition<br />
Session MoAT4 ⎯⎯ Symposium: Telerobotics<br />
Session MoAT5 ⎯⎯ Symposium: Bio-inspired Robotics I<br />
Session MoAT6 ⎯⎯ Symposium: Field Robotics I<br />
Session MoAT7 ⎯⎯ Wheeled Robots<br />
Session MoAT8 ⎯⎯ Learning Parameterized Policies<br />
Session MoAT9 ⎯⎯ Novel Actuators I<br />
16:00-17:30<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–1–<br />
Monday<br />
September 26, <strong>2011</strong><br />
Session MoBT1 ⎯⎯ Micromanipulation<br />
Session MoBT2 ⎯⎯ Localization with Constraints<br />
Session MoBT3 ⎯⎯ Symposium: Robot Audition: From Sound Source Localization to<br />
Automatic Speech Recognition<br />
Session MoBT4 ⎯⎯ Symposium: Robot Demonstrations<br />
Session MoBT5 ⎯⎯ Symposium: Bio-inspired Robotics II<br />
Session MoBT6 ⎯⎯ Symposium: Field Robotics II<br />
Session MoBT7 ⎯⎯ Teleoperation<br />
Session MoBT8 ⎯⎯ Learning for Control<br />
Session MoBT9 ⎯⎯ Novel Actuators II
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–2–<br />
Monday<br />
September 26, <strong>2011</strong>
Session MoAT1 Continental Parlor 1 Monday, September 26, <strong>2011</strong>, 14:00–15:30<br />
Microsensing<br />
Chair Koichi Hashimoto, Tohoku Univ.<br />
Co-Chair Fumihito Arai, Nagoya Univ.<br />
14:00–14:15 MoAT1.1<br />
Evaluation of Biological Clock Activity<br />
Capsulated by Lipid-mono-layer<br />
Masaru Kojima 1 , Masahiro Nakajima 2 , Kingo Takiguchi 3 ,<br />
Michio Homma 3 , Takao Kondo 3 and Toshio Fukuda 1,2<br />
1.Dept. of Micro-Nano Systems Eng., Nagoya Univ., Japan<br />
2.Center for Micro-Nano Mechatronics, Nagoya Univ., Japan<br />
3.Div. of biological science, Nagoya Univ., Japan<br />
• Toward to application, the components of<br />
the biological clock reconstituted into<br />
phospholipid-coated microdroplets.<br />
• The clock activity in phospholipid-coated<br />
microdroplets was detected as long<br />
period.<br />
• Localization of KaiB and KaiC proteins in<br />
droplets was observed by using<br />
fluorescent microscopy<br />
• Different distribution between KaiB and<br />
KaiC suggest that biased local<br />
concentration make long period.<br />
phosphorylation rate<br />
50 μ m<br />
1<br />
0.9<br />
0.8<br />
0.7<br />
0.6<br />
0.5<br />
0.4<br />
0.3<br />
0.2<br />
Control<br />
Droplet<br />
0.1<br />
0<br />
35 hr 35 hr<br />
0 10 20 30 40 50 60<br />
70 80 90 100<br />
time (hr)<br />
Clock activity in phospholipidcoated<br />
microdroplets<br />
14:30–14:45 MoAT1.3<br />
Temperature Measurement by Color Analysis of<br />
Fluorescent Spectrum Using Cell Investigation Tool<br />
Impregnated with Quantum Dot for<br />
Cell Measurement on a Microfluidic Chip<br />
Hisataka MARUYAMA, Kyohei TOMITA, Taisuke MASUDA<br />
Department of Micro-Nano Systems Engineering, Nagoya University, Japan<br />
Fumihito Arai<br />
Department of Micro-Nano Systems Engineering, Nagoya University, Japan<br />
Seoul National University, Korea.<br />
• Local temperature measurement using<br />
hydrogel containing temperature sensitive<br />
quantum dot was achieved.<br />
• Temperature was measured by color<br />
information instead of fluorescent intensity<br />
for long-lifetime temperature<br />
measurement.<br />
• Sensitivity of temperature measurement<br />
was -1.3%/K and accuracy of temperature<br />
was �0.3 K, respectively.<br />
• Lifetime of temperature measurement<br />
using color is much longer than<br />
temperature measurement using<br />
fluorescent intensity.<br />
Fluorescent image of gel-tool<br />
containing Q-dots<br />
14:15–14:30 MoAT1.2<br />
Fast and adaptive auto-focusing algorithm<br />
for microscopic cell observation<br />
Takeshi Obara and Koichi Hashimoto<br />
Graduate School of Information Sciences, Tohoku University, Japan<br />
Yasunobu Igarashi<br />
Olympus Software Technology Corp., Japan<br />
• Auto-focusing algorithm to observe a<br />
freely motile cell, combining 2 methods<br />
below<br />
• Fast auto-focus: Depth from Diffraction<br />
(DFDi)<br />
• Adaptive auto-focus: wide scan using 2D<br />
Laplacian to correct DFDi parameter<br />
• A freely moving paramecium was autofocused<br />
and tracked during 45s (see fig.)<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–3–<br />
Time lapse images of the freely<br />
moving paramecium<br />
14:45–14:50 MoAT1.4<br />
Tracking of Objects in Motion-Distorted Scanning<br />
Electron Microscope Images<br />
Christian Dahmen and Sergej Fatikow<br />
AMiR, University of Oldenburg, Germany<br />
• Motion of objects induces object<br />
appearance distortion due to scanning<br />
nature of scanning electron microscope<br />
• Adjacent lines have closest temporal<br />
relation<br />
• For tracking, a template is decomposited<br />
into line templates, and the line templates<br />
flexibly matched<br />
• This allows tracking of objects where<br />
standard algorithms may fail due to<br />
distortions.
Session MoAT1 Continental Parlor 1 Monday, September 26, <strong>2011</strong>, 14:00–15:30<br />
Microsensing<br />
Chair Koichi Hashimoto, Tohoku Univ.<br />
Co-Chair Fumihito Arai, Nagoya Univ.<br />
14:50–14:55 MoAT1.5<br />
Cell Hardness Measurement by Using Twofingered<br />
Microhand with Micro Force Sensor<br />
Daiki KAWAKAMI*, Kenichi OHARA*,<br />
Tomohito TAKUBO*, Yasushi MAE*, Akihiko ICHIKAWA**,<br />
Tamio TANIKAWA***, Tatsuo ARAI*<br />
* Graduate School of System Engineering,Osaka Univ., Japan<br />
** Dept. of Micro-Nano System Engineering, Nagoya Univ.,Japan<br />
***Intelligent Systems Research Institute, AIST, Japan<br />
• Two-fingered microhand with micro force<br />
sensor was developed.<br />
• The microhand can realize cell operation<br />
and cell condition measurement.<br />
• Meyer hardness is focused as a validation<br />
factor.<br />
• Calibration strategy obtaining the<br />
relationships between measured sensor<br />
data to SI units is shown.<br />
• The algorithm to obtain Meyer hardness<br />
by using the microhand is proposed.<br />
• Validation results are shown through the<br />
experiments using fibroblast cell<br />
End-effector with micro force sensor<br />
Loading and unloading process<br />
for fibroblast cell<br />
15:00–15:15 MoAT1.7<br />
Nanoforce estimation with Kalman filtering<br />
applied to a force sensor based on diamagnetic<br />
levitation<br />
Emmanuel Piat and Joël Abadie and Stéphane Oster<br />
Femto-st Institute, CNRS – UFC – ENSMM – UTBM, France<br />
• Nanoforce sensor based on diamagnetic<br />
levitation<br />
• Force/displacement transducer based on<br />
macroscopic seismic mass<br />
• Deconvolution of displacement using<br />
Kalman filtering to estimate the force<br />
• Uncertain a priori modelling of the<br />
unknown force<br />
• Trade-off between force resolution and<br />
sensor response time<br />
• The size of the image may be adjusted<br />
14:55–15:00 MoAT1.6<br />
An ultra-high precision, high bandwidth torque<br />
sensor for microrobotics applications<br />
Ben Finio and Robert Wood<br />
School of Engineering and Applied Sciences, Harvard University, USA<br />
Kevin Galloway<br />
Wyss Institute, Harvard University, USA<br />
• We present, analysis, design, fabrication, calibration and use of a highresolution,<br />
single-axis torque sensor<br />
• Sensor fulfills need for torque measurements in micro-Newton-meter<br />
range, not possible with commercially available sensors<br />
• As an example application, we measure open-loop pitch torques<br />
generated by wing motions of a robotic fly<br />
15:15–15:30 MoAT1.8<br />
Optimal Design of Non Intuitive Compliant<br />
Microgripper With High Resolution<br />
Aymen Grira and Christine Rotinat-Libersa<br />
CEA –LIST, Interactive Robotics Laboratory France<br />
Aymen Grira, Bernard Legrand, Estelle Mairiaux, Lionel Buchaillot<br />
IEMN, Nano and micro systems group, Lille, France<br />
• New high force-resolution microgripper with<br />
large jaw displacement, electrostatically<br />
actuated by a comb-drive and instrumented<br />
with an integrated differential capacitive<br />
displacement sensor.<br />
• This microgripper has been non-intuitively<br />
designed using a multi-objective optimization<br />
method, to reach the best compromise<br />
between chosen performance criteria.<br />
• It has been modeled and fabricated using<br />
silicon micro-technology.<br />
• The theoretical gripping force resolution<br />
obtained is 78 pN along the direction of the<br />
mobile tip displacement.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–4–<br />
Differential<br />
capacitive<br />
sensor<br />
(a)<br />
Fixed<br />
tip<br />
Mobile<br />
tip<br />
Combdrive<br />
Fixed<br />
electrodes<br />
(b)<br />
actuators<br />
Sensing<br />
20 μm<br />
electrodes<br />
Mobile<br />
electrodes<br />
Fixed<br />
electrodes<br />
500 μm 100 μm (c)<br />
SEM pictures of the fabricated microgripper.<br />
(a) Overview of the optimized microgripper<br />
combined with the comb-drive actuator and<br />
the differential capacitive displacement sensor.<br />
(b) A closed view of the differential capacitive<br />
displacement sensor made of 30 fingers. Each<br />
one is 485 µm long and 4 µm wide. (c) A<br />
closed view of the electrostatic comb-drive<br />
actuator made of 68 fingers. Each finger is 20<br />
µm long and 2 µm wide. The fingers are<br />
spaced by 2 µm and the overlapping distance<br />
between fixed and mobile fingers at rest is 10<br />
µm.
Session MoAT2 Continental Parlor 2 Monday, September 26, <strong>2011</strong>, 14:00–15:30<br />
Localization<br />
Chair Juan D. Tardos, Univ. de Zaragoza<br />
Co-Chair Antonio Franchi, Max Planck Inst. for Biological Cybernetics<br />
14:00–14:15 MoAT2.1<br />
Real-Time Loop Detection<br />
with Bags of Binary Words<br />
Dorian Gálvez-López and Juan D. Tardós<br />
I3A, University of Zaragoza, Spain<br />
• Real-time loop closure detection applying<br />
the bag-of-words + geometrical checking<br />
approach on a binary descriptor space.<br />
• The feature extraction time is reduced by<br />
using FAST points with Binary Robust<br />
Independent Elementary Features<br />
(BRIEF).<br />
• Geometrical checking is speeded up by<br />
pre-filtering correspondence points with a<br />
direct index.<br />
• Extracting features, detecting loops in a<br />
database with 19K images and performing<br />
geometrical checking take less than 50<br />
milliseconds.<br />
Execution time with 2723 images.<br />
Precision: 100%, Recall: 57.86%<br />
14:30–14:45 MoAT2.3<br />
An Observability-Constrained<br />
Sliding Window Filter for SLAM<br />
Guoquan Huang, and Stergios Roumeliotis<br />
Dept. of Computer Science and Engineering, Univ. of Minnesota, USA<br />
Anastasios Mourikis<br />
Dept. of Electrical Engineering, Univ. of California, Riverside, USA<br />
• Sliding-Window Filter (SWF):<br />
A resource-adaptive smoother,<br />
better than filtering at dealing with<br />
nonlinearity<br />
• Inconsistency of standard SWF:<br />
rank of the Hessian is erroneously<br />
increased<br />
• Observability-Constrained (OC)-<br />
SWF selects linearization points to<br />
ensure correct rank of Hessian<br />
while minimizing linearization errors<br />
OC-SWF: better consistency and<br />
accuracy than competing approaches,<br />
performs closer to batch Maximum-A-<br />
Posterior (MAP) estimator in tests<br />
14:15–14:30 MoAT2.2<br />
Best-First Branch and Bound Search Method for<br />
Map Based Localization<br />
Jari Saarinen, Janne Paanajärvi and Pekka Forsman<br />
Automation and Systems technology, Aalto University, Finland<br />
• An efficient algorithm that calculates<br />
globally optimal pose estimate<br />
• The algorithm is based on best-first<br />
branch and bound search in the<br />
relative pose space between the data<br />
sets<br />
• Provides also an estimation of the<br />
accuracy<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–5–<br />
The measurement is evaluated in<br />
the center of the cell, for which<br />
the score and upper bound<br />
are estimated<br />
14:45–14:50 MoAT2.4<br />
Corrective Gradient Refinement for<br />
Mobile Robot Localization<br />
Joydeep Biswas, Brian Coltin, Manuela Veloso<br />
School of Computer Science, Carnegie Mellon University, USA<br />
• Corrective Gradient Refinement uses<br />
state space derivatives of observation<br />
likelihood to refine proposal distributions<br />
• Efficient, Analytic computation using<br />
Vector Maps and 2D (Laser rangefinder)<br />
and 3D (Kinect Depth Camera) point cloud<br />
observation models<br />
• Experimentally shown to be more<br />
accurate, and require far fewer particles<br />
than Sampling / Importance Resampling<br />
Monte Carlo Localization<br />
• Long term trials (> 13km traversed indoors<br />
over 19 days) demonstrate reliability<br />
CGR<br />
SIR - MCL<br />
CGR efficiently samples<br />
directions of uncertainty based on<br />
observations
Session MoAT2 Continental Parlor 2 Monday, September 26, <strong>2011</strong>, 14:00–15:30<br />
Localization<br />
Chair Juan D. Tardos, Univ. de Zaragoza<br />
Co-Chair Antonio Franchi, Max Planck Inst. for Biological Cybernetics<br />
14:50–14:55 MoAT2.5<br />
A Monocular Vision-based System<br />
for 6D Relative Robot Localization<br />
Andreas Breitenmoser, Laurent Kneip and Roland Siegwart<br />
Autonomous Systems Laboratory, ETH Zurich, Switzerland<br />
• Novel relative localization system with<br />
two complementary modules:<br />
• Camera module: monocular vision<br />
• Target module: 4 active or passive<br />
markers<br />
• 6D relative localization in 3D space,<br />
based on target design, P3P-localization,<br />
blob extraction & pose prediction<br />
• Experimental validation on a quadrotor<br />
helicopter and on a team of e-puck robots<br />
• Accuracies of a few centimeters in<br />
position & a few degrees in orientation<br />
Relative localization modules<br />
installed on three e-puck robots<br />
15:00–15:15 MoAT2.7<br />
Loop-closure candidates selection by exploiting<br />
structure in vehicle trajectory<br />
J. Nieto, G. Agamennoni and T. Vidal-Calleja<br />
Australian Centre for Field Robotics, The University of Sydney, Australia<br />
• Detecting loops is a key problem in robot<br />
localisation.<br />
• Most of the current techniques use<br />
observations of the environment as main<br />
features to produce loop-hypotheses.<br />
• In this paper we investigate the feasibility<br />
of producing loop candidates from<br />
features of the robot trajectory.<br />
• We present an alignment likelihood<br />
function to measure similarity between<br />
trajectory sequences.<br />
• Experimental results with data collected in<br />
Sydney CBD are used to validate our<br />
approach.<br />
Estimated trajectory with loop<br />
candidates selected<br />
14:55–15:00 MoAT2.6<br />
Accurate Human Motion Capture in Large Areas<br />
by Combining IMU- and Laser-Based<br />
People Tracking<br />
Jakob Ziegler, Henrik Kretzschmar,<br />
Cyrill Stachniss, and Wolfram Burgard<br />
Dept. of Computer Science, University of Freiburg, Germany<br />
Giorgio Grisetti<br />
Dept. of Systems and Computer Science, Sapienza University of Rome, Italy<br />
• Estimate the globally aligned full body<br />
posture of a person<br />
• Combine an inertial motion capture suit<br />
with laser-based people tracking<br />
• Formulate a joint least squares<br />
optimization problem to compute a<br />
maximum likelihood trajectory estimate<br />
given all measurements<br />
• Experimental evaluation in large outdoor<br />
scenes<br />
15:15–15:30 MoAT2.8<br />
Optimized Motion Strategies for Localization in<br />
Leader-Follower Formations<br />
Xun S. Zhou*, Ke X. Zhou†, and Stergios I. Roumelitotis*<br />
*Dept. of Computer Science & Engineering, Univ. of Minnesota, USA<br />
†Dept. of Eletrical & Computer Engineering, Univ. of Minnesota, USA<br />
• Main issue: Robot-to-robot pose is<br />
unobservable using distance- or bearing-only<br />
measurements, when moving in formation.<br />
• Key idea: Allow follower to deviate from the<br />
desired formation within a predefined region<br />
to minimize the localization uncertainty.<br />
• Formulated as non-convex optimization<br />
problems<br />
• Key contribution:<br />
(a) Analyze the trade-off between the<br />
formation and the estimation accuracy;<br />
(b) Analytically compute the global optimal<br />
motion strategy (by solving a system of<br />
multivariate polynomials).<br />
• Performance: Indistinguishable from gridbased<br />
exhaustive search, and significantly<br />
better than state of the art.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–6–<br />
{F}<br />
{L}<br />
The follower (F) seeks the next<br />
best pose within a disk centered<br />
at the desired position p o with<br />
radius r, such that the localization<br />
uncertainty is minimized.
Session MoAT3 Continental Parlor 3 Monday, September 26, <strong>2011</strong>, 14:00–15:30<br />
Symposium: Robot Audition: Active Audition<br />
Chair Patrick Danès, Univ. de Toulouse ; LAAS-CNRS ; UPS ; F-31077<br />
Co-Chair Ian Lane, Carnegie Mellon Univ. Silicon Valley<br />
14:00–14:15 MoAT3.1*<br />
Semi-Plenary Invited Talk: An Overview of the<br />
BINAAHR Project<br />
Patrick Danès, Université de Toulouse ; LAAS-CNRS ; UPS ;<br />
F-31077<br />
Hiroshi G. Okuno, Kyoto University<br />
14:30–14:45 MoAT3.3<br />
Active Soft Pinnae for Robots<br />
Makoto Kumon and Yoshitaka Noda<br />
Graduate School of Science and Technology, Kumamoto University, Japan<br />
• A novel pinna system that can deform its<br />
shape actively is proposed.<br />
• The pinna has a soft deformable skin<br />
made of silicone rubber, and it is covered<br />
with fur.<br />
• Kinematic model of the pinna is<br />
investigated, and it is validated by<br />
experiments.<br />
• Measured acoustic characteristics shows<br />
that the pinna can focus the sound coming<br />
from the front, and that it can encode the<br />
elevation of the sound source as a<br />
frequency cue.<br />
14:15–14:30 MoAT3.2<br />
Assessment of Single-channel<br />
Ego Noise Estimation Methods<br />
Gökhan Ince 1,2 , Kazuhiro Nakadai 1,2 , Tobias Rodemann 3 ,<br />
Jun-ichi Imura 2 , Keisuke Nakamura 1 and Hirofumi Nakajima 1<br />
1- Honda Research Institute Japan Co., Ltd. 2- Tokyo Institute of Tech., Japan<br />
3- Honda Research Institute Europe GmbH<br />
• Problem: Ego noise (Fan noise + motor<br />
noise generated during robot motions)<br />
• Method: Instantaneous estimation of ego<br />
noise using parameterized templates<br />
• Evaluation: Performance comparison<br />
with other state-of-the-art single-channel<br />
noise estimation methods<br />
• Results:<br />
1) Reduced estimation error<br />
2) Reduced log spectral distortion<br />
3) Improved signal-to-noise ratio<br />
4) Improved word correct rates<br />
during the arm and head motion of a<br />
humanoid robot in the presence of<br />
changing background noise<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–7–<br />
Noisy signal<br />
Enhanced signal<br />
Yes sir,<br />
we are cool!<br />
Robots<br />
are cool!<br />
14:45–14:50 MoAT3.4<br />
Particle-filter Based Audio-visual Beat-tracking<br />
for Music Robot Ensemble with Human Guitarist<br />
Tatsuhiko Itohara, Takuma Otsuka ,Takeshi Mizumoto<br />
Tetsuya Ogata, and Hiroshi G. Okuno<br />
Graduate School of Informatics, Kyoto University, Japan<br />
• Beat-tracking, estimation of tempo<br />
and beat-times, is critical to the high<br />
quality of musical ensemble.<br />
• Most conventional methods lack the<br />
adaptation of some feature of guitar<br />
performances, such as back-beats.<br />
• Our method integrates the audio and<br />
visual information with a particle filter<br />
method.<br />
• Experimental results reveal the<br />
robustness against the guitar features<br />
and real-time performance.<br />
A ensemble by a thereminist<br />
robot and a human guitarist
Session MoAT3 Continental Parlor 3 Monday, September 26, <strong>2011</strong>, 14:00–15:30<br />
Symposium: Robot Audition: Active Audition<br />
Chair Patrick Danès, Univ. de Toulouse ; LAAS-CNRS ; UPS ; F-31077<br />
Co-Chair Ian Lane, Carnegie Mellon Univ. Silicon Valley<br />
14:50–14:55 MoAT3.5<br />
Optimizing a Reconfigurable Robotic<br />
Microphone Array<br />
Eric Martinson, Thomas Apker, Magda Bugajska<br />
Navy Center for Applied Research in Artificial Intelligence<br />
Naval Research Laboratory, USA<br />
Omnidirectional<br />
Camera<br />
Microphones,<br />
with Fiducials<br />
• A distributed, robotic microphone array is deployed to localize sound<br />
sources in predetermined regions.<br />
• The array shape, (relative microphone locations), impact sound<br />
localization accuracy.<br />
• Random array shapes often have poor performance.<br />
• Optimizing robotic positions for the region of interest improves sound<br />
localization accuracy > 70%.<br />
15:00–15:15 MoAT3.7<br />
Acoustic Models and Kalman Filtering Strategies<br />
for Active Binaural Sound Localization<br />
Alban Portello † , Patrick Danès † , Sylvain Argentieri ‡<br />
† CNRS-LAAS, Université de Toulouse, France<br />
‡ ISIR-CNRS, UPMC, France<br />
• A generic framework to the localization of<br />
a moving sound source from a moving<br />
binaural sensor is presented.<br />
• An accurate model of binaural cues in the<br />
presence of motion is set up.<br />
• A multi-hypothesis estimation scheme is<br />
proposed, which can ensure a correct<br />
(non-overconfident) localization with no<br />
prior knowledge.<br />
• The strategy is shown to resolve frontback<br />
ambiguities and to provide range<br />
information about the source.<br />
• The approach is validated on simulated<br />
and experimental results.<br />
(a) Sensor motion and and emitter<br />
position estimate in the world<br />
frame f (b) Range R and d azimuth i th<br />
estimates in the sensor frame along<br />
time<br />
14:55–15:00 MoAT3.6<br />
Incremental Learning for<br />
Ego Noise Estimation of a Robot<br />
Gökhan Ince 1,2 , Kazuhiro Nakadai 1,2 , Tobias Rodemann 3 ,<br />
Jun-ichi Imura 2 , Keisuke Nakamura 1 and Hirofumi Nakajima 1<br />
1- Honda Research Institute Japan Co., Ltd. 2- Tokyo Institute of Tech., Japan<br />
3- Honda Research Institute Europe GmbH<br />
• Problem: Template estimation suffers<br />
from constantly growing database size<br />
• Method: Incremental learning of the<br />
templates with an update mechanism<br />
• Results:<br />
1) Reduced database size<br />
2) Reduced log spectral distortion<br />
3) Reduced noise estimation error<br />
4) Improved signal-to-noise ratio<br />
5) Improved word correct rates<br />
during the arm and head motion of a<br />
humanoid robot in the presence of<br />
changing background noise<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–8–<br />
Number of templates in the<br />
database<br />
2000<br />
Standard template<br />
1800<br />
learning<br />
1600<br />
Incremental learning<br />
1400 of templates<br />
1200<br />
1000<br />
800<br />
600<br />
400<br />
200<br />
0<br />
1 2 3 4 5<br />
Number of motion repetitions<br />
Yes sir, we are<br />
totally cool!<br />
Robots are<br />
cool!<br />
15:15–15:30 MoAT3.8<br />
Intelligent Sound Source Localization and Its<br />
Application to Multimodal Human Tracking<br />
Keisuke Nakamura, Kazuhiro Nakadai<br />
Futoshi Asano, and Gökhan Ince<br />
Honda Research Institute Japan Co., Ltd., Japan<br />
• The proposed human tracking system in a<br />
real environment by intelligent sound<br />
source localization (SSL) achieves:<br />
1. Intelligent sound source tracking based<br />
on the sort of sound source such as<br />
speech, music, and so on by integrating<br />
SSL with sound source identification<br />
2. Highly-robust human tracking by<br />
multimodal integration of a microphone<br />
array, ToF camera, and thermo camera<br />
even when sensor information is<br />
missing or ambiguous due to occlusion<br />
or acoustic noise.<br />
Intelligent Human<br />
Tracking System
Session MoAT4 Continental Ballroom 4 Monday, September 26, <strong>2011</strong>, 14:00–15:30<br />
Symposium: Telerobotics<br />
Chair Angelika Peer, Tech. Univ. München<br />
Co-Chair M. Cenk Cavusoglu, Case Western Res. Univ.<br />
14:00–14:15 MoAT4.1*<br />
Semi-Plenary Invited Talk: Development of VGO<br />
Robotic Telepresence System<br />
Thomas Ryden, VGO Communications<br />
14:30–14:45 MoAT4.3<br />
Hybrid Virtual-Proxy Based Control Framework<br />
for Passive Bilateral Teleoperation over Internet<br />
Ke Huang* and Dongjun Lee +<br />
* MABE Department, University of Tennessee - Knoxville, USA<br />
+ School of MAE, Seoul National University, South Korea<br />
• Novel hybrid control framework for bilateral<br />
teleoperation over packet-switched<br />
network with varying-delay, packet loss,<br />
data swapping, etc.<br />
• Key innovation is to exploit oftenoverlooked<br />
hybrid nature of teleoperation<br />
(i.e., continuous robot, discrete comm.,<br />
and sampled-date control)<br />
• Closed-loop passivity guaranteed by a<br />
combination of discrete/adjustable virtual<br />
proxy (VP) damping and continuous/unadjustable<br />
device damping<br />
• Large device damping requirement of VPless<br />
direct PD-coupling (IFAC11) for<br />
severe comm. imperfectness is removed<br />
System structure of VP based<br />
bilateral teleoperation<br />
14:15–14:30 MoAT4.2<br />
Semi-Plenary Invited Talk: Development of VGO<br />
Robotic Telepresence System<br />
Thomas Ryden, VGO Communications<br />
14:45–14:50 MoAT4.4<br />
Mutual Telexistence Surrogate System: TELESAR4<br />
- telexistence in real environments using<br />
autostereoscopic immersive display -<br />
Susumu Tachi, Kouichi Watanabe, Keisuke Takeshita,<br />
Kouta Minamizawa, Takumi Yoshida, and Katsunari Sato<br />
Graduate school of Media Design, Keio University, Japan<br />
• TELESAR4 affords a remote person the<br />
opportunity to virtually participate in some<br />
event, such as a party or a meeting.<br />
• The system provides the remote<br />
participant with a life size 360˚ wide-range<br />
stereo view of the event venue with local<br />
participants in real time.<br />
• The local participants at the event are able<br />
to see the face and expressions of the<br />
remote participant in real time.<br />
• The system also allows the remote<br />
participant to move freely about the venue<br />
and to perform some manipulatory tasks<br />
such as handshake and gestures.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–9–<br />
a<br />
b<br />
(a) Remote participant virtually<br />
attending an event.<br />
(b) Local participants and<br />
surrogate robot in the venue.
Session MoAT4 Continental Ballroom 4 Monday, September 26, <strong>2011</strong>, 14:00–15:30<br />
Symposium: Telerobotics<br />
Chair Angelika Peer, Tech. Univ. München<br />
Co-Chair M. Cenk Cavusoglu, Case Western Res. Univ.<br />
14:50–14:55 MoAT4.5<br />
Experiments of Passivity-Based Bilateral Aerial<br />
Teleoperation of a Group of UAVs with<br />
Decentralized Velocity Synchronization<br />
Paolo Robuffo Giordano * , Antonio Franchi * , Cristian Secchi ** , and<br />
Heinrich H. Bülthoff * ***<br />
* Max Planck Institute for Biological Cybernetics, Germany<br />
** DISMI, University of Modena and Reggio Emilia, Italy<br />
*** DBCE, Korea University, Korea<br />
• Decentralized Bilateral Teleoperation<br />
of a group of UAVs<br />
• Time-varying topology (arbitrary<br />
split/join) is handled in a stable (passive)<br />
way<br />
• Formal proof of steady-state velocity<br />
synchronization via a variable damping<br />
action<br />
• Experiments with 4 Quadcopters (slaveside)<br />
and a Omega.3 force-feedback<br />
device (master-side)<br />
Experiments of 4 Quadcopters<br />
teleoperated in a cluttered<br />
environment<br />
15:00–15:15 MoAT4.7<br />
Network Representation and Passivity of<br />
Delayed Teleoperation Systems<br />
J.Artigas, C. Preusche, G. Hirzinger<br />
Institute of Robotics and Mechatronic, German Aerospace Center, Germany<br />
J-H. Ryu<br />
Biorobotics Laboratory, Korean University of Technology, South Korea<br />
• General network based<br />
representation, analysis and<br />
design.<br />
• A solution to the ambiguity of the<br />
channel causality (with lack of effort<br />
or flow transmitted!).<br />
• A framework to design<br />
teleoperation architectures:<br />
• Time delay independency<br />
• For any channel architecture<br />
• Stability through Time Domain<br />
Passivity.<br />
14:55–15:00 MoAT4.6<br />
Design of Single-Operator-Multi-Robot<br />
Teleoperation Systems with<br />
Random Communication Delays<br />
Yunyi Jia, Ning Xi and John Buether<br />
Electritical and Computer Engineering Department,<br />
Michigan State University, USA<br />
• Propose a system structure for<br />
designing Single-Operator-Multi-<br />
Robot Teleoperation Systems<br />
• Non-time based control is applied<br />
to cope with random<br />
communication delays<br />
• Convenient to change the multirobot<br />
structure, like adding or<br />
removing robots<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–10–<br />
Single-Operator-Multi-Robot<br />
Teleoperation System<br />
15:15–15:30 MoAT4.8<br />
Controlling Telerobotic Operations Adaptive to<br />
Quality of Teleoperator and Task Dexterity<br />
Yunyi Jia, Ning Xi, Fei Wang, Yunxia Wang and Xin Li<br />
Electritical and Computer Engineering Department,<br />
Michigan State University, USA<br />
• Propose a concept named Quality<br />
of Teleoperator (QoT)<br />
• Integrate the quality of<br />
teleoperator into teleoperation<br />
system<br />
• Adjust controller parameters<br />
adaptive to QoT and Task<br />
dexterity<br />
• Teleoperation efficiency and<br />
safety are enhanced<br />
Telerobotic systems adaptive to QoT and TDI
Session MoAT5 Continental Ballroom 5 Monday, September 26, <strong>2011</strong>, 14:00–15:30<br />
Symposium: Bio-inspired Robotics I<br />
Chair Justin Seipel, Purdue<br />
Co-Chair Xinyan Deng, Purdue Univ.<br />
14:00–14:15 MoAT5.1*<br />
Semi-Plenary Invited Talk: Robustness in Animals as<br />
Inspiration for the Next Generation Robot<br />
Robert Full, University of California at Berkeley<br />
14:30–14:45 MoAT5.3<br />
A MATLAB Framework for Efficient Gait Creation<br />
C. David Remy and Roland Siegwart, Fellow, IEEE<br />
Autonomous Systems Lab, ETH Zurich, Switzerland<br />
Keith Buffinton<br />
Dept. of Mech Engr., Bucknell University, Lewisburg PA, USA<br />
• The paper introduces a framework for the<br />
creation of efficient gaits that can exploit<br />
natural dynamics.<br />
• The presented tools and methods are<br />
illustrated with the following three<br />
examples:<br />
• The stability analysis of a passive<br />
dynamic walker<br />
• An analysis of the efficiency (COT) of<br />
an actuated monopod hopper<br />
• The control of a bounding quadruped<br />
14:15–14:30 MoAT5.2*<br />
Semi-Plenary Invited Talk: Snake-inspired Robots<br />
Shigeo Hirose, Tokyo Institute of Technology<br />
14:45–14:50 MoAT5.4<br />
A Controller for Continuous Wave<br />
Peristaltic Locomotion<br />
Alexander S. Boxerbaum, Andrew D. Horchler, Roger D. Quinn<br />
Mechanical Engineering, Case Western Reserve University, USA<br />
Kendrick M. Shaw, Hillel J. Chiel<br />
Biology, Case Western Reserve University, USA<br />
• Recent successes with a continuously deformable wormlike robot have<br />
informed the investigation of novel soft-bodied control strategies<br />
• A dynamic analysis of peristalsis suggests ways to minimize slippage<br />
• A Wilson-Cowan model of<br />
neuronal populations is explored<br />
as a means to generate many<br />
different waveforms<br />
• Unlike CPG networks, this<br />
model can readily change its<br />
spatial and temporal<br />
frequencies, even coming to a<br />
complete stop<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–11–<br />
A wormlike robot with a continuously<br />
deformable outer mesh
Session MoAT5 Continental Ballroom 5 Monday, September 26, <strong>2011</strong>, 14:00–15:30<br />
Symposium: Bio-inspired Robotics I<br />
Chair Justin Seipel, Purdue<br />
Co-Chair Xinyan Deng, Purdue Univ.<br />
14:50–14:55 MoAT5.5<br />
Energetics of Bio-Inspired Legged Robot<br />
Locomotion with Elastically-Suspended Loads<br />
Jeffrey Ackerman and Justin Seipel<br />
School of Mechanical Engineering, Purdue University, USA<br />
• Loads such as batteries, electronics, and<br />
fuel can be elastically-suspended from a<br />
robot<br />
• Elastically-suspended loads increase the<br />
energy efficiency of legged locomotion<br />
• Thus, a robot’s speed, operating time, or<br />
load carrying capacity could be increased<br />
Prototype robot with an<br />
elastically-suspended load<br />
15:00–15:15 MoAT5.7<br />
Descending Commands to an Insect Leg Controller<br />
Network Cause Smooth Behavioral Transitions<br />
Brandon Rutter, Brian Taylor, John Bender, Roy Ritzmann and<br />
Roger Quinn<br />
Mech. & Aero Eng., and Biology, Case Western Reserve Univ., USA<br />
William Lewinger<br />
Inst. Of Perception, Action & Behavior, U. of Edinburgh, UK<br />
Marcus Blümel<br />
Dept. of Animal Physiology, Inst. For Zoology, U. of Cologne, Germany<br />
• Leg Controller Network (LegConNet)<br />
controls forward stepping of a robotic<br />
model cockroach leg.<br />
• Hypothesized additional pathways allow<br />
the model to smoothly transition to insectlike<br />
turning behaviors.<br />
• The modification of these pathways and<br />
their strengths is equivalent to descending<br />
commands in the animal<br />
• Several experiments were run showing<br />
the operation of the system under differing<br />
timing of descending commands.<br />
14:55–15:00 MoAT5.6<br />
A Rapidly Reconfigurable Robot for Assistance<br />
in Urban Search and Rescue<br />
Alexander Hunt, Richard Bachmann and Roger Quinn<br />
Department of Mechanical and Aerospace Engineering<br />
Case Western Reserve University, USA<br />
Robin Murphy<br />
Department of Computer Science and Engineering, Texas A&M, USA<br />
• USAR Whegs TM successfully implements<br />
features desirable for search and rescue<br />
on a mobile robot<br />
• Relatively lightweight and compact<br />
• Four wheel-legs and differential steering<br />
• Carbon fiber wheel-legs for strength and<br />
reduction of rotary inertia by factor of eight<br />
• Quick change devices allow the robot to<br />
be changed to/from tracks and wheel-legs<br />
• Geosystems Zippermast for elevating<br />
camera field of view up to 8 feet (xx<br />
meters)<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–12–<br />
Robot with mast raised next to a<br />
74 inch (188 cm) tall man<br />
15:15–15:30 MoAT5.8<br />
Virtual Chassis for Snake Robots<br />
David Rollinson and Howie Choset<br />
The Robotics Institute, Carnegie Mellon University, USA<br />
• A new method for determining<br />
intuitive body coordinate frames for<br />
snake robots.<br />
• Separates internal motion due to the<br />
robot’s shape changes from external<br />
motion in the world.<br />
• A general method is presented for<br />
any shape configuration.<br />
• Additional refinements can be made<br />
in certain cases that exploit the<br />
symmetry of the robot’s shape.
Session MoAT6 Continental Ballroom 6 Monday, September 26, <strong>2011</strong>, 14:00–15:30<br />
Symposium: Field Robotics I<br />
Chair Satoshi Tadokoro, Tohoku Univ.<br />
Co-Chair David A. Anisi, ABB<br />
14:00–14:15 MoAT6.1*<br />
Semi-Plenary Invited Talk: Rescue mobile robot<br />
Quince --Toward emergency response to the nuclear<br />
accident at Fukushima Daiichi Nuclear Power Plant on<br />
March <strong>2011</strong><br />
Keiji Nagatani, Tohoku University<br />
Tomoaki Yoshida, Chiba Institute of Technology<br />
14:30–14:45 MoAT6.3<br />
Perception for a River Mapping Robot<br />
Andrew Chambers, Supreeth Achar, Stephen Nuske, Jörn Rehder,<br />
Lyle Chamberlain, Justin Haines, Sebastian Scherer, Sanjiv Singh<br />
Robotics Institute, Carnegie Mellon University, USA<br />
Bernd Kitt<br />
The Institute of Measurement and Control Systems,<br />
Karlsruhe Institute of Technology, Germany<br />
• Perception system for mapping rivers with<br />
thick canopy<br />
• 360� obstacle detection with off-axis<br />
spinning laser<br />
• Adaptive appearance model for river<br />
detection and map generation<br />
• Graph-based global state estimation from<br />
visual odometry, inertial measurements,<br />
and intermittent GPS<br />
3D Point Cloud overlaid on 2km<br />
river traverse<br />
14:15–14:30 MoAT6.2<br />
Semi-Plenary Invited Talk: Rescue mobile robot<br />
Quince --Toward emergency response to the nuclear<br />
accident at Fukushima Daiichi Nuclear Power Plant on<br />
March <strong>2011</strong><br />
Keiji Nagatani, Tohoku University<br />
Tomoaki Yoshida, Chiba Institute of Technology<br />
14:45–14:50 MoAT6.4<br />
Real-world demonstration of sensor-based<br />
robotic automation in oil & gas facilities<br />
David A. Anisi, Erik Persson and Clint Heyer<br />
Department for Strategic R&D for Oil, Gas<br />
and Petrochemicals, ABB, Norway<br />
• On-site demonstration of autonomous<br />
valve manipulation and thermal inspection<br />
• Both operations were requested by the<br />
current site operators<br />
• Run amidst live hydrocarbon processes<br />
• The first system performing sensor-based,<br />
close-contact operations in a real<br />
operational environment (ATEX)<br />
• Using torque overload detection method to<br />
avoid over-tightening/loosening the valve<br />
• Rely on four independent layers of safety<br />
to meet the level of robustness, accuracy<br />
and reliability required by the industry<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–13–<br />
Industrial robot certified for running<br />
in explosive atmospheres (ATEX)
Session MoAT6 Continental Ballroom 6 Monday, September 26, <strong>2011</strong>, 14:00–15:30<br />
Symposium: Field Robotics I<br />
Chair Satoshi Tadokoro, Tohoku Univ.<br />
Co-Chair David A. Anisi, ABB<br />
14:50–14:55 MoAT6.5<br />
Offshore Robotics –<br />
Survey, Implementation, Outlook<br />
Kai Pfeiffer, Alexander Bubeck and Matthias Bengel<br />
Robot Systems Department, Fraunhofer Institute for<br />
Manufacturing Engineering and Automation (IPA), Germany<br />
• Survey of production operation tasks on<br />
offshore installations shows potential for<br />
robotic automation<br />
• MIMROex: World wide first ATEX<br />
certifiable mobile inspection robot tested<br />
on an offshore installation<br />
• Extension of robot control system for<br />
assisted tele-manipulation for<br />
interaction with process equipment<br />
Mobile inspection robot MIMROex<br />
on an offshore installation in the<br />
south Chinese sea<br />
15:00–15:15 MoAT6.7<br />
Combining Radar and Vision for Self-Supervised<br />
Ground Segmentation in Outdoor Environments<br />
Annalisa Milella<br />
Institute of Intelligent Systems for Automation, CNR, Italy<br />
Giulio Reina<br />
Department of Engineering for Innovation, University of Salento, Italy<br />
James Underwood, and Bertrand Douillard<br />
Australian Centre for Field Robotics, University of Sydney, Australia<br />
• Self-supervised radar-vision classification<br />
system that allows an autonomous vehicle<br />
to automatically construct online a visual<br />
model of the ground and perform accurate<br />
ground segmentation.<br />
• Automatic online labeling based on a<br />
radar ground segmentation approach prior<br />
to image analysis to avoid time consuming<br />
manual labeling to construct the training<br />
set.<br />
• Adaptive ground model learning by<br />
retraining the classifier using the most<br />
recent radar scans.<br />
14:55–15:00 MoAT6.6<br />
Active camera control with obstacle avoidance for<br />
remote operations with industrial manipulators:<br />
Implementation and experimental results<br />
Magnus Bjerkeng and Kristin Pettersen<br />
Department of Engineering Cybernetics, NTNU, Norway<br />
Aksel Transeth, Erik Kyrkebø and Sigurd Fjerdingen<br />
SINTEF ICT Applied Cybernetics, Norway<br />
• A controller for dynamic surveillance of<br />
remote operations for enhanced<br />
remote awareness is presented.<br />
• A pseudoinverse controller based on<br />
the stereographic projection achieves<br />
real-time camera tracking and tunable<br />
camera distance.<br />
• Obstacle avoidance is achieved using<br />
artificial potential fields respecting joint<br />
limitations without oscillations.<br />
• Experiments using industrial<br />
manipulators are presented.<br />
15:15–15:30 MoAT6.8<br />
Assessing the Deepwater Horizon Oil Spill with<br />
the Sentry Autonomous Underwater Vehicle<br />
James C. Kinsey 1 , Dana R. Yoerger 1 , Michael V. Jakuba 2 ,<br />
Rich Camilli 1 , Charles R. Fisher 3 , and Christopher R. German 1<br />
1 Woods Hole Oceanographic Institution, USA<br />
2 Australian Centre for Field Robotics, Australia<br />
3 Pennsylvania State University, USA<br />
• The Sentry AUV is an autonomous<br />
underwater robot capable of operations to<br />
4500m<br />
• In 2010, Sentry was deployed on two<br />
cruises in response to the Deepwater<br />
Horizon Oil Spill<br />
• In June 2010, Sentry was equipped with<br />
the TETHYS mass spectrometer [Camilli<br />
and Duryea, 2009] and tracked an<br />
underwater oil plume up to 35km from the<br />
spill site<br />
• Another cruise in December 2010 did<br />
bathymetric and photo surveys to assess<br />
the impact of the spill on coral<br />
communities<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–14–<br />
Top: Sentry AUV at the DWH Oil Spill.<br />
Bottom: Methane and water velocity<br />
measurements of the plume<br />
[Camilli et al., 2010]
Session MoAT7 Continental Parlor 7 Monday, September 26, <strong>2011</strong>, 14:00–15:30<br />
Wheeled Robots<br />
Chair Yasuhisa Hirata, Tohoku Univ.<br />
Co-Chair Matthew Spenko, Illinois Inst. of Tech.<br />
14:00–14:15 MoAT7.1<br />
SO(2) and SO(3), Omni-Directional Personal Mobility<br />
with Link-Driven Spherical Wheels<br />
Sungsuk Ok, Atsushi Kodama, Yuma Matsumura<br />
and Yoshihiko Nakamura<br />
Mechano-Informatics, The University of Tokyo, Japan<br />
• This paper proposed Link-Driven<br />
Spherical Wheel composed of three serial<br />
links jointed to the spherical wheel.<br />
• It is realized that LDSW move to any<br />
direction by designed controller.<br />
• The SO(3) installing 3LDSW is developed<br />
for a hospital bed or a personal mobility<br />
and realized to move to omni-direction by<br />
designed controller.<br />
• The SO(2) installing 2LDSW is developed<br />
for a personal mobility and experimented<br />
by designed controller based on the<br />
dynamics and kinematics of mobility.<br />
The overview of Link-Driven<br />
Spherical Wheel & Mobilities<br />
( LDSW, SO(2) and SO(3) )<br />
14:30–14:45 MoAT7.3<br />
Wheel-Soil Interaction Model for Rover<br />
Simulation Based on Plasticity Theory<br />
Ali Azimi, Jozsef Kovecses, and Jorge Angeles<br />
Department of Mechanical Engineering<br />
and Centre for Intelligent Machines<br />
McGill University, Canada<br />
• A novel approach based on plasticity<br />
theory is introduced here, which is<br />
computationally efficient<br />
• The simulation results are verified with<br />
experimentally validated semiempirical<br />
models in steady-state<br />
operations<br />
• This approach can address some of<br />
the shortcomings of the semi-empirical<br />
models, like slip-sinkage phenomenon<br />
• Our approach is well suited for<br />
implementation in a multibody<br />
dynamics simulation environment like<br />
Vortex<br />
Sojourner Simulation in Vortex<br />
14:15–14:30 MoAT7.2<br />
Design and control of an active anti-roll system<br />
for a fast rover<br />
Mohamed Krid and Faiz Ben Amar<br />
Institut des Systèmes Intelligents et de Robotiques<br />
Université Pierre et Marie Curie UPMC Paris 6, CNRS UMR7222, France<br />
• Fast off-road rover with high clearance<br />
and large suspension displacement<br />
• Design of an innovative anti-roll active<br />
device and its kinematic modeling<br />
• Roll dynamics modeling<br />
• Stability metrics defined by Lateral Load<br />
Transfer<br />
• Design of a model predictive control MPC<br />
for roll control and optimal stability<br />
considering the path following control<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–15–<br />
Figure caption is optional,<br />
use Arial Narrow 20pt<br />
14:45–14:50 MoAT7.4<br />
Using Unmanned Ground Vehicle Performance<br />
Measurements as a Unique Method of Terrain<br />
Classification<br />
Siddharth Odedra<br />
Autonomous Systems Lab, Middlesex University, UK<br />
• An Unmanned Ground Vehicles’ biggest<br />
obstacle is terrain; which can be<br />
unpredictable, unstructured and<br />
untraversable.<br />
• Systems with more knowledge about<br />
terrain will have greater success when<br />
operating in unforgiving conditions.<br />
• It is proposed that terrain can be classified<br />
by measuring its effect on vehicle<br />
performance.<br />
Vehicle platform used to<br />
measure the vehicle-terrain<br />
interaction.
Session MoAT7 Continental Parlor 7 Monday, September 26, <strong>2011</strong>, 14:00–15:30<br />
Wheeled Robots<br />
Chair Yasuhisa Hirata, Tohoku Univ.<br />
Co-Chair Matthew Spenko, Illinois Inst. of Tech.<br />
14:50–14:55 MoAT7.5<br />
A Multi-tiered Robust Steering Controller Based<br />
on Yaw Rate and Side Slip Estimation<br />
Ming Xin and Mark Minor<br />
Mechanical Engineering Department, University of Utah, USA<br />
• Model considers curvature based vehicle<br />
kinematics, and slip angle and yaw rate<br />
dynamics.<br />
• Multi-tiered sliding mode control well<br />
matched to vehicle sensors, actuators,<br />
and dynamics.<br />
• High gain observer estimates slip angle<br />
and yaw rate for output feedback control<br />
• Simulations and experiment verify<br />
performance at high and low speeds<br />
The Experimental Platform –<br />
“Red Rover”<br />
15:00–15:15 MoAT7.7<br />
A Diameter-Dependent Terramechanics Model<br />
for Small-Wheeled UGVs on Deformable Terrain<br />
Gareth Meirion-Griffith and Matthew Spenko<br />
Mechanical, Materials and Aerospace Engineering Department, Illinois Institute<br />
of Technology, U.S.A.<br />
• In this paper a diameter-dependent<br />
pressure-sinkage model is introduced.<br />
• This model is used to derive a modified<br />
terramechanics framework for smallwheeled<br />
UGVs.<br />
• This framework is used to simulate vehicle<br />
performance on deformable terrains.<br />
• Field tests using an experimental UGV are<br />
used to validate this model.<br />
Elliptical distribution of normal<br />
stress at the wheel-soil interface<br />
14:55–15:00 MoAT7.6<br />
Differential flatness of a front-steered vehicle<br />
with tire-force control<br />
Steven Peters and Karl Iagnemma<br />
Dept. of Mechanical Engineering, MIT, USA<br />
Emilio Frazzoli<br />
Lab for Information and Decision Systems (LIDS), MIT, USA<br />
• The position of the front center of<br />
oscillation of a bicycle is a flat output<br />
• Applying tire force control allows this<br />
point to track desired trajectories,<br />
even at slip angles of 10-20 degrees<br />
• Structure in yaw dynamics resembling<br />
Liénard system enables nonlinear<br />
stability analysis of 3 rd degree of<br />
freedom, yaw dynamics<br />
15:15–15:30 MoAT7.8<br />
Development of Passive type Double<br />
Wheel Caster Unit based on Analysis of<br />
Feasible Braking Force and Moment Set<br />
Masao Saida, Yasuhisa Hirata and Kazuhiro Kosuge<br />
Engineering, Tohoku University, Japan<br />
• We introduce passive mobile robots<br />
consisting of various wheels which are<br />
controlled by servo brakes.<br />
• We analyze a feasible braking<br />
force/moment necessary for controlling<br />
the robot with servo brakes.<br />
• We reveal the advantages and<br />
disadvantages of the feasible braking<br />
force/moment of each robot.<br />
• We propose a new passive type double<br />
wheel caster unit, called PDC.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–16–<br />
F yr<br />
x r<br />
Izz mxr c<br />
Fxf<br />
Fyf<br />
Feasible Braking Force and Moment<br />
Passive type Double Wheel Caster Unit<br />
δ
Session MoAT8 Continental Parlor 8 Monday, September 26, <strong>2011</strong>, 14:00–15:30<br />
Learning Parameterized Policies<br />
Chair Jan Peters, Darmstadt Univ. of Tech.<br />
Co-Chair Petar Kormushev, Istituto Italiano di Tecnologia<br />
14:00–14:15 MoAT8.1<br />
Bipedal Walking Energy Minimization<br />
by Reinforcement Learning with<br />
Evolving Policy Parameterization<br />
Petar Kormushev, S. Calinon, N. G. Tsagarakis, D. G. Caldwell<br />
Advanced Robotics, Italian Institute of Technology (IIT), Italy<br />
Barkan Ugurlu<br />
Toyota Technological Institute (TTI), Japan<br />
• New reinforcement learning approach<br />
evolves the policy parameterization<br />
dynamically during the learning process<br />
• Advantages:<br />
• Faster convergence<br />
• Avoids local optima<br />
• Decreased computation time<br />
• Applications:<br />
• Function approximation task<br />
• Bipedal robot walking energy<br />
consumption minimization<br />
• Variable-CoM-height ZMP-based walk<br />
generator uses efficiently the passive<br />
compliance of the robot<br />
Passively-compliant robot COMAN<br />
walking with varying CoM-height<br />
14:30–14:45 MoAT8.3<br />
Learning Anticipation Policies<br />
for Robot Table Tennis<br />
Zhikun Wang 1,2 , Christoph H. Lampert 3 , Katharina Mülling 1,2 ,<br />
Bernhard Schölkopf 1 , Jan Peters 1,2<br />
1 MPI for Intelligent Systems 2 TU Darmstadt 3 IST Austria<br />
• The robot learns to play table tennis against human players.<br />
• We develop an anticipation system that allows the robot to<br />
• perceive the opponent’s moves;<br />
• predict his intention;<br />
• react accordingly with optimized hitting plans.<br />
• The anticipation policies are learned using reinforcement learning.<br />
• Figure. The optimal hitting plan is chosen by the learned anticipation policies.<br />
14:15–14:30 MoAT8.2<br />
Learning Motion Primitive Goals<br />
For Robust Manipulation<br />
Freek Stulp, Evangelos Theodorou, Mrinal Kalakrishnan,<br />
Peter Pastor, Ludovic Righetti, Stefan Schaal<br />
Computational Learning and Motor Control Lab,<br />
University of Southern California, USA<br />
• Contributions:<br />
• Simplify the PI2 model-free<br />
reinforcement learning<br />
algorithm<br />
• Enable PI2 to simultaneously<br />
learn the shape and goal (i.e.<br />
end-point) of a motion primitive<br />
• Apply to learning motion<br />
primitives for manipulation that<br />
are robust to uncertainty in<br />
object position<br />
• Evaluated on a 11DOF<br />
manipulation platform<br />
14:45–14:50 MoAT8.4<br />
Learning Elementary Movements<br />
Jointly with a Higher Level Task<br />
Jens Kober and Jan Peters<br />
Department of Empirical Inference, MPI for Intelligent Systems, Germany<br />
Intelligent Autonomous Systems Group, TU Darmstadt, Germany<br />
• Learning primitive movements and higher<br />
level strategies at the same time.<br />
• Lower level learned by Cost-regularized<br />
Kernel Regression.<br />
• Transition probabilities of the higher level<br />
estimated from the lower level.<br />
• Approach evaluated on a side-stall-style<br />
throwing game both in simulation and with<br />
a real BioRob<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–17–
Session MoAT8 Continental Parlor 8 Monday, September 26, <strong>2011</strong>, 14:00–15:30<br />
Learning Parameterized Policies<br />
Chair Jan Peters, Darmstadt Univ. of Tech.<br />
Co-Chair Petar Kormushev, Istituto Italiano di Tecnologia<br />
14:50–14:55 MoAT8.5<br />
Learning Interaction Control Policies<br />
by Demonstration<br />
Vasiliki Koropouli, Dongheui Lee and Sandra Hirche<br />
Institute of Automatic Control Engineering,<br />
Technical University of Munich, Germany<br />
• Learning interaction forces to interact with compliant environments.<br />
• Modeling of forces by an autonomous control policy based on parallel<br />
force/position control.<br />
• Parameterization and learning of the policy by Locally Weighted<br />
Regression.<br />
• Implemented in virtual manipulation tasks using 2DOF haptic device.<br />
15:00–15:15 MoAT8.7<br />
Adapting Control Policies for Expensive Systems to<br />
Changing Environments<br />
Matthew Tesch, Jeff Schneider and Howie Choset<br />
Robotics Institute, Carnegie Mellon University, United States of America<br />
• Optimal control parameters can vary<br />
significantly from environment to<br />
environment<br />
• Optimizing expensive systems is<br />
restrictive in even a single environment<br />
• Control policies define a mapping from<br />
environment parameters to controller<br />
parameters<br />
• Learning good policies for expensive<br />
systems requires reasoning about related<br />
environments and careful experiment<br />
selection<br />
A predicted model of an objective<br />
function, with the corresponding<br />
optimal policy.<br />
14:55–15:00 MoAT8.6<br />
Motion Generation by Reference-Point-Dependent<br />
Trajectory HMMs<br />
Komei Sugiura, Naoto Iwahashi, and Hideki Kashioka<br />
NICT, Japan<br />
• Imitation learning method for object-manipulating motions such as<br />
“place X on Y” and “raise Z”<br />
• EM algorithm and cross-validation are used for estimating the optimal<br />
coordinate system and state number<br />
• Reference-point-dependent trajectory HMM is used for generating<br />
smooth trajectory<br />
Imitation learning of motion “throw-away”<br />
15:15–15:30 MoAT8.8<br />
Online movement adaptation<br />
based on previous sensor experiences<br />
Peter Pastor1 , Ludovic Righetti1 ,<br />
Mrinal Kalakrishnan1 , and Stefan Schaal1,2 1CLMC Lab, University of Southern California, USA<br />
2Max-Planck-Institute for Intelligent Systems, Tübingen, Germany<br />
• Stereotypical movements allow to<br />
accumulate previous sensor<br />
experiences and generate predictive<br />
models for subsequent task executions<br />
• These sensor traces are used to adapt<br />
the movement plan online to react to<br />
unexpected perturbations<br />
• Tight feedback integration is achieved<br />
using Dynamic Movement Primitives<br />
and a compliant control architecture<br />
• Our approach combines reactive<br />
behaviors with stereotypical<br />
movements to create a rich set of<br />
possible motions that account for<br />
external perturbations and perception<br />
uncertainty to generate truly robust<br />
behaviors<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–18–
Session MoAT9 Continental Parlor 9 Monday, September 26, <strong>2011</strong>, 14:00–15:30<br />
Novel Actuators I<br />
Chair Han-Pang Huang, National Taiwan Univ.<br />
Co-Chair Stephen Mascaro, Univ. of Utah<br />
14:00–14:15 MoAT9.1<br />
Design of a New Variable Stiffness Actuator and<br />
Application for Assistive Exercise Control<br />
Tzu-Hao Huang, Jiun-Yih Kuan, and Han-Pang Huang*<br />
Department of Mechanical Engineering, National Taiwan University, Taiwan<br />
• Design concept of CCEA: antagonistically<br />
identical four-bar linkages with two<br />
extensional linear springs<br />
• A human-robot interaction model is<br />
proposed to investigate the properties of<br />
the system itself and its stability during<br />
physical human-robot interaction<br />
• Two control models are proposed to<br />
investigate the performance of assistive<br />
exercise control<br />
• CCEA exoskeleton is built for elbow<br />
rehabilitation. Both simulations and<br />
experiments are conducted to show some<br />
desired properties of the proposed CCEA<br />
system<br />
Fac<br />
M ac<br />
R1<br />
y ac<br />
xac<br />
R 2<br />
yca<br />
xca<br />
R1 2 R<br />
M ca<br />
14:30–14:45 MoAT9.3<br />
Optimal energy density piezoelectric twisting<br />
actuators<br />
Ben Finio and Robert Wood<br />
School of Engineering and Applied Sciences, Harvard University, USA<br />
• Piezoelectric actuators are frequently<br />
useful in microrobotics applications due to<br />
their compact size<br />
• We present a torsional piezoelectric<br />
actuator<br />
• Modeling using laminate-plate theory is<br />
verified with experimental actuator<br />
performance testing<br />
X ac<br />
X<br />
β<br />
X ca<br />
Fl<br />
Y<br />
14:15–14:30 MoAT9.2<br />
A New Variable Stiffness Actuator (CompAct-<br />
VSA): Design and Modelling<br />
Nikos G. Tsagarakis, Irene Sardellitti, Darwin G. Caldwell<br />
Department of Advanced Robotics, Istituto Italiano di Tecnologia, Italy<br />
• This work introduces the mechanics, the<br />
principle of operation and the model of new<br />
variable stiffness actuator .<br />
• The actuator regulates the compliance<br />
using an amplification mechanism based<br />
on the lever arm principle with a<br />
continuously regulated pivot point.<br />
• The proposed principle permits the<br />
realization of an actuation unit with a wide<br />
range of stiffness and a fast stiffness<br />
regulation response.<br />
• Preliminary results are presented to<br />
demonstrate the fast stiffness regulation<br />
response and the wide range of stiffness<br />
achieved by the proposed design.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–19–<br />
The CompAct-VSA unit<br />
14:45–14:50 MoAT9.4<br />
A Nonlinear Series Elastic Actuator for Highly<br />
Dynamic Motions<br />
• Called the HypoSEA<br />
• Designed specifically for force<br />
control of jumping robots<br />
• Nonlinear torque-angle<br />
relationship and 40J energy<br />
storage capability are optimized<br />
for hopping motions<br />
• Very robust against shock<br />
Ivar Thorson and Darwin Caldwell<br />
Department of Advanced Robotics,<br />
Istituto Italiano di Tecnologia, Italy<br />
The HypoSEA Actuator
Session MoAT9 Continental Parlor 9 Monday, September 26, <strong>2011</strong>, 14:00–15:30<br />
Novel Actuators I<br />
Chair Han-Pang Huang, National Taiwan Univ.<br />
Co-Chair Stephen Mascaro, Univ. of Utah<br />
14:50–14:55 MoAT9.5<br />
Development of a 3-DOF Inchworm Mechanism<br />
Organized by a Pair of Y-shaped Electromagnets<br />
and 6 Piezoelectric Actuators<br />
-Design, Principle, and Experiments of Translational Motions-<br />
O. Fuchiwaki, M. Yatsurugi, S. Omura, and K. Arafuka<br />
Yokohama National University<br />
Hodogaya, Yokohama, Kanagawa, Japan<br />
• Newly developed 3-DOF inchworm<br />
mechanism with 6 contact points<br />
on a surface<br />
• 3-DOF simple harmonic vibration<br />
model to get the input signals<br />
• Improvement of a positioning<br />
repeatability, especially when it<br />
carries a payload<br />
• VTR demonstration of interesting<br />
omnidirectional motion is also<br />
available to check how it works<br />
Y-shaped<br />
electromagnet<br />
Piezoelectric<br />
actuator<br />
Newly-developed mechanism with<br />
better positioning repeatability<br />
15:00–15:15 MoAT9.7<br />
Dielectric Elastomer Bender Actuator Applied to<br />
Modular Robotics<br />
Paul White, Stella Latscha,<br />
Steve Schlaefer, and Mark Yim<br />
Mechanical Engineering and Applied Mechanics<br />
University of Pennsylvania, USA<br />
• Comparison of several actuation<br />
technologies with respect to important<br />
module metrics indicates dielectric<br />
elastomer actuation is a promising<br />
technology<br />
• Design, fabrication, and experimental<br />
characterization of a 30mm length scale<br />
dielectric elastomer actuated module<br />
• Six different modular robot configurations<br />
comprising two or three modules<br />
• Verified parallel actuation with two<br />
modules lifting twice the load a single<br />
module can<br />
0V<br />
3500V<br />
Single module (top); two module<br />
chain unactuated (middle) and<br />
actuated (bottom)<br />
14:55–15:00 MoAT9.6<br />
Wet Shape Memory Alloy Actuated Robotic<br />
Heart with Thermofluidic Feedback<br />
Matt Pierce and Stephen Mascaro<br />
Mechanical Engineering, University of Utah, USA<br />
• First ever successful implementation of a<br />
self-sustaining thermofluidically powered<br />
SMA pump<br />
• Distributes thermofluidic energy to arrays<br />
of SMA actuators that function as robotic<br />
muscles<br />
• Designed and optimized by varying key<br />
parameters through modeling, simulation,<br />
and experimentation<br />
• Capable of a net output of 66 mL/min<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–20–<br />
Wet SMA Robotic Heart Prototype<br />
15:15–15:30 MoAT9.8<br />
Stretchable Circuits and Sensors<br />
For Robotic Origami<br />
Jamie K Paik, Rebecca K Kramer and Robert J Wood<br />
Microrobotics Laboratory,<br />
School of Engineering and Applied Sciences, Harvard University, USA<br />
• We present two types of stretchable<br />
circuits that are directly applicable to low<br />
profile and high strain environment of<br />
robotic origami: copper-mesh circuit and<br />
eGaIn-filled PDMS micro channel circuit.<br />
• Both circuits are directly connected to the<br />
SMA actuator module that produces<br />
independent folding with 0� -180� range of<br />
motion and 3.5mNm torque.<br />
• Copper-mesh circuit has strategic slits that<br />
are embedded within polymer to provide<br />
high conductivity during actuator motions.<br />
eGaIn circuit can produce strain up to<br />
160% before failure.<br />
Two flexible circuits integrated<br />
with robotic origami tile modules.<br />
The left shows the copper-mesh<br />
circuit and the right is the eGaIn<br />
circuit. Both circuits have trace<br />
widths of 1mm. For the eGaIn<br />
circuit, the curvature sensor is<br />
embedded to the right of the<br />
actuator.
Session MoBT1 Continental Parlor 1 Monday, September 26, <strong>2011</strong>, 16:00–17:30<br />
Micromanipulation<br />
Chair Tatsuo Arai, Osaka Univ.<br />
Co-Chair Jake Abbott, Univ. of Utah<br />
16:00–16:15 MoBT1.1<br />
Image-Based Magnetic Control of<br />
Paramagnetic Microparticles in Water<br />
Jasper D. Keuning, Jeroen de Vries, Leon Abelmann<br />
and Sarthak Misra<br />
University of Twente, The Netherlands<br />
• Implementation of a system for controlling<br />
the position of a 100μm paramagnetic<br />
microparticle using electromagnets.<br />
• The position of the particle is determined<br />
by processing microscopic images.<br />
• The particle is positioned with an accuracy<br />
of 8.4μm, achieving speeds of 235μm/s.<br />
• The particle can follow a circular or a<br />
figure-eight path.<br />
Image of the setup and results of the<br />
particle tracking a figure-eight path<br />
16:30–16:45 MoBT1.3<br />
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16:15–16:30 MoBT1.2<br />
Automated Micromanipulation of a Microhand<br />
with All-In-Focus Imaging System<br />
Chanh-Nghiem Nguyen, Kenichi Ohara, Ebubekir AVCI,<br />
Tomohito Takubo, Yasushi Mae and Tatsuo Arai<br />
Department of Systems Innovation, Graduate School of Engineering Science,<br />
Osaka University, Japan<br />
• In this paper, automated<br />
micromanipulation using a twofingered<br />
microhand was<br />
performed<br />
• 3D positions of both end-effectors<br />
and target objects were calculated<br />
using All-In-Focus imaging system<br />
• Line-Type Pattern Matching is<br />
proposed to calculate 3D positions<br />
of end-effectors<br />
• Automated micromanipulation was<br />
demonstrated with single pickand-place<br />
task of microspheres<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–21–<br />
Target microsphere<br />
Static microhand<br />
50µm<br />
Target is<br />
released<br />
Automated pick-and-place<br />
(clockwise from Top-Left)<br />
Target is<br />
picked up<br />
16:45–16:50 MoBT1.4<br />
Modeling and Design of Magnetic Sugar<br />
Particles Manipulation System for Fabrication of<br />
Vascular Scaffold<br />
Chengzhi Hu, Carlos Tercero, Seiichi Ikeda, Toshio Fukuda<br />
Department of Micro-nano Systems Engineering, Nagoya University, Japan<br />
Fumihito Arai<br />
Department of Mechanical Science and Engineering, Nagoya University, Japan<br />
Makoto Negoro<br />
Department of Neurosurgery, Fujita Health University, Japan<br />
• Proposing a method for developing<br />
magnetically controllable templates for<br />
particulate leaching by encapsulating<br />
magnetic microparticles inside sugar<br />
microspheres to realize 3D manipulation<br />
of magnetic particle used for structured<br />
porogen-based fabrication of vascular<br />
scaffolds.<br />
• Control methodology based on Maxwell<br />
coils and Helmholtz coils was illustrated<br />
and we theoretically analyzed the<br />
movement properties of MSP<br />
• A prototype magnetic motion control<br />
system for MSP/MSP cluster is presented
Session MoBT1 Continental Parlor 1 Monday, September 26, <strong>2011</strong>, 16:00–17:30<br />
Micromanipulation<br />
Chair Tatsuo Arai, Osaka Univ.<br />
Co-Chair Jake Abbott, Univ. of Utah<br />
16:50–16:55 MoBT1.5<br />
Toward Intuitive Teleoperation of Micro/Nano-<br />
Manipulators with Piezoelectric Stick-Slip Actuators<br />
Manikantan Nambi, Aayush Damani, and Jake J. Abbott<br />
Mechanical Engineering, University of Utah, USA<br />
• Our goal is to enable intuitive openloop<br />
teleoperated rate control of<br />
micro/nano-manipulators without<br />
any closed-loop feedback from the<br />
vision system.<br />
• Two algorithms are developed for<br />
rate control of stick-slip actuator<br />
based micro/nano- manipulators.<br />
• A method for incorporating openloop<br />
models of the manipulator is<br />
developed.<br />
• Experimental results for a<br />
positioning task in the horizontal<br />
plane are presented.<br />
(top) Experimental setup.<br />
(bottom) Simulation results moving from initial<br />
to target position. Algorithm 1 shows<br />
undesirable drift at very low velocities.<br />
17:00–17:15 MoBT1.7<br />
Evaluation and Application of Thermoresponsive<br />
Gel Handling towards Manipulation of Single Cells<br />
Masaru Takeuchi, Masaru Kojima and Toshio Fukuda<br />
Department of Micro-Nano Systems Engineering, Nagoya University, Japan<br />
Masahiro Nakajima<br />
Center for Micro-nano Mechatronics, Nagoya University, Japan<br />
• Evaluation of the Thermoresponsive Gel<br />
probe (GeT probe) was conducted<br />
• The temperature at the probe tip was<br />
analyzed using Finite Element Method<br />
(FEM) analysis<br />
• Fixation force was measured using AFM<br />
cantilever<br />
• Two methods were proposed to handle<br />
microbeads in the pure water by the GeT<br />
probe<br />
GeT probe<br />
Microbead<br />
AFM cantilever<br />
Thermoresponsive gel 50 �m<br />
50 �m<br />
Fixation force measurement<br />
using AFM cantilever<br />
16:55–17:00 MoBT1.6<br />
Pairing and Moving Swarm of Micro Particles into<br />
Array with a Robot-tweezer Manipulation System<br />
Haoyao Chen<br />
Mechanical Engineering and Automation, HITSZ, P.R. China<br />
Dong Sun<br />
MEEM, City University of Hong Kong, Hong Kong<br />
• Present the batch manipulation of<br />
particles by using integrated robotics and<br />
holographic optical tweezers technologies<br />
• A controller is proposed to drive pairs of<br />
particles to the assigned regions which<br />
are centered at array points.<br />
• The potential field method is utilized to<br />
avoid collisions between particles.<br />
• Experiments on colloidal particles are<br />
performed to demonstrate the<br />
effectiveness of the proposed approach.<br />
17:15–17:30 MoBT1.8<br />
Comparison on Experimental and Numerical<br />
Results for Helical Swimmers inside Channels<br />
Ahmet Fatih Tabak, Fatma Zeynep Temel,<br />
Serhat Yesilyurt<br />
Mechatronics Engineering, Sabanci University, Turkey<br />
• Magnetic swimmers with ~200 micron<br />
diameter Nd 2Fe 14B particles (body) and ~<br />
2mm long helix-shaped rigid wires (tail).<br />
• 1-mm diameter glass tube filled with<br />
glycerol at room temperature placed<br />
inside Helmoltz coil arrangement.<br />
• Hydrodynamic model of the swimmer with<br />
external magnetic torque and wall effects.<br />
• Experimental data and simulation results<br />
agree well on the forward swimming<br />
motion inside the channel.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–22–
Session MoBT2 Continental Parlor 2 Monday, September 26, <strong>2011</strong>, 16:00–17:30<br />
Localization with Constraints<br />
Chair Giuseppe Oriolo, Univ. di Roma<br />
Co-Chair Ethan Stump, US Army Res. Lab.<br />
16:00–16:15 MoBT2.1<br />
Mutual Localization using<br />
Anonymous Bearing Measurements<br />
Paolo Stegagno*, Marco Cognetti*<br />
Antonio Franchi**, Giuseppe Oriolo*<br />
*Università di Roma “La Sapienza”<br />
**Max Plank Institute for Biological Cybernetics, Germany<br />
• Decentralized mutual localization<br />
problem for multi-robot systems<br />
• Only anonymous relative bearing<br />
measurements are used (no global<br />
localization, no distances, no identity)<br />
• Novel algorithm for probabilistic<br />
multiple registration of these<br />
measurements<br />
• Extensive experimental validation<br />
and comparison with the case of<br />
bearing-plus-distance measurements<br />
experiments and simulations with<br />
4 ground robots<br />
16:30–16:45 MoBT2.3<br />
Monte Carlo Localization using 3D Texture Maps<br />
Yu Fu<br />
National Taiwan University of Science and Technology, Taiwan<br />
Stephen Tully, George Kantor, and Howie Choset<br />
Robotics Institute, Carnegie Mellon University,USA<br />
• Monte Carlo Localization is<br />
adopted to localize a robot in a<br />
simplified 3D texture map<br />
• A simplified 3D texture map<br />
consists of vertical planes and<br />
their appearance<br />
• Particles converge faster using<br />
our method than with range-only<br />
MCL due to observing texture<br />
• Particles are more concentrated<br />
because the observed texture for<br />
a particle is generated from a<br />
texture map<br />
Localization in 3D texture map<br />
16:15–16:30 MoBT2.2<br />
Robust Local Localization for Indoor<br />
Environments with Uneven Floors and<br />
Inaccurate Maps<br />
Peter Abeles<br />
Intelligent Automation Inc., USA<br />
• Fault-tolerant robotic localization for indoor<br />
environments using Laser Range Finder<br />
(LRF) .<br />
• Unlike past work, specifically designed to<br />
handle inaccurate maps and uneven<br />
floors.<br />
• Novel motion estimation equations without<br />
singularities in long hallways.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–23–<br />
Terrain at MOUT site<br />
16:45–16:50 MoBT2.4<br />
Active Target Localization<br />
for Bearing Based Robotic Telemetry<br />
Pratap Tokekar, Joshua Vander Hook and Volkan Isler<br />
Department of Computer Science,<br />
Univ. of Minnesota, USA<br />
• A novel robotic telemetry system<br />
for localizing radio-tagged invasive<br />
fish in frozen lakes<br />
• We present a technique for<br />
bearing-estimation from sparse<br />
measurements obtained from a<br />
directional radio antenna<br />
• Next, we study 3 active localization<br />
strategies to select sensing<br />
locations to minimize uncertainty in<br />
the location of a target<br />
• Our system can operate on frozen<br />
lakes and localize the target to<br />
within values as low as one meter<br />
The Husky robot with radio tracking<br />
equipment (left top of chassis)<br />
and antenna during field testing<br />
on Lake Casey, MN
Session MoBT2 Continental Parlor 2 Monday, September 26, <strong>2011</strong>, 16:00–17:30<br />
Localization with Constraints<br />
Chair Giuseppe Oriolo, Univ. di Roma<br />
Co-Chair Ethan Stump, US Army Res. Lab.<br />
16:50–16:55 MoBT2.5<br />
Orientation Descriptors<br />
for Localization in Urban Environments<br />
Philip David and Sean Ho<br />
Army Research Laboratory, USA<br />
• Determine the 2D position and 3D<br />
orientation of a ground-level camera in<br />
urban environments<br />
• The urban model is a 2D building map<br />
generated from aerial imagery<br />
• Register building Footprint Orientation<br />
(FPO) descriptors calculated from the<br />
2D map and from omnidirectional<br />
ground imagery<br />
• Accuracy: 1m in position and 1� in<br />
orientation<br />
17:00–17:15 MoBT2.7<br />
Monte Carlo Localization and Registration to<br />
Prior Data for Outdoor Navigation<br />
David Silver and Anthony Stentz<br />
Robotics Institute, Carnegie Mellon University, USA<br />
• GPS provides positioning for outdoor<br />
navigation, but can be intermittent<br />
• GPS may not be properly aligned with<br />
prior sources of environmental data<br />
• A non-parametric sensor model can be<br />
learned between generic onboard and<br />
prior data<br />
• The learned model can be used to localize<br />
in the absence of GPS, or register GPS<br />
and prior data<br />
Comparison of GPS, odometry,<br />
and MCL localized paths<br />
16:55–17:00 MoBT2.6<br />
A Hybrid Estimation Framework for Cooperative<br />
Localization under Communication Constraints<br />
Esha D. Nerurkar + , Ke X. Zhou*, and Stergios I. Roumeliotis +<br />
+ Dept. of Comp. Sci. and Engineering, Univ. of Minnesota, USA<br />
*Dept. of Electrical and Comp. Engineering, Univ. of Minnesota, USA<br />
• Develop a novel hybrid estimation scheme for CL under severe bandwidth<br />
constraints (robot only allowed to communicate single bit per analog msr.)<br />
• Proposed scheme enables each robot to process quantized msrs. from<br />
other robots, as well as its own analog msrs.<br />
• Present an improved quantization rule by using more accurate estimates<br />
from the hybrid estimation framework<br />
• Hybrid scheme achieves higher estimation accuracy than existing<br />
estimators that process only quantized msrs.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–24–
Session MoBT3 Continental Parlor 3 Monday, September 26, <strong>2011</strong>, 16:00–17:30<br />
Symposium: Robot Audition: From Sound Source Localization to Automatic Speech Recognition<br />
Chair Kazuhiro Nakadai, Honda Res. Inst. Japan Co., Ltd.<br />
Co-Chair Hiroshi G. Okuno, Kyoto Univ.<br />
16:00–16:15 MoBT3.1*<br />
Semi-Plenary Invited Talk: Real-Time Large Vocabulary<br />
Conversational Speech Recognition for Robust<br />
Human-Robot Interaction<br />
Ian Lane, Carnegie Mellon University Silicon Valley<br />
16:30–16:45 MoBT3.3<br />
Variable Frame Rate Hierarchical Analysis for<br />
Robust Speech Recognition<br />
Jean ROUAT and Stéphane LOISELLE<br />
NECOTIS, GEGI, Univ. de Sherbrooke, Canada<br />
Stéphane MOLOTCHNIKOFF<br />
Dépt. biologie, Univ. de Montréal, Canada<br />
• Extraction of acoustical events<br />
corresponding to ONSETS, stable,<br />
ascending or descending 2 nd level<br />
complex features<br />
• Events are time markers on which a<br />
subsequent signal frame can be placed<br />
for speech recognition<br />
• The VFR yields more than 50% increase<br />
in rates on TI46-words corrupted with<br />
Aurora-2 noises with SNR between 0<br />
and 20dB<br />
• Robustness to reverberation is shown<br />
16:15–16:30 MoBT3.2*<br />
Semi-Plenary Invited Talk: Recent Advances on Noise<br />
Reduction and Source Separation Technology for<br />
Robot Audition<br />
Hiroshi Saruwatari, Nara Institute of Science and Technology<br />
16:45–16:50 MoBT3.4<br />
SLAM-based Online Calibration of<br />
Asynchronous Microphone Array for<br />
Robot Audition<br />
1 1<br />
Hiroki Miura, Takami Yoshida,<br />
2<br />
1,2<br />
Keisuke Nakamura, Kazuhiro Nakadai<br />
1. Dept. of Mechanical and Environmental Informatics,<br />
Tokyo Institute of Techonology, Japan<br />
2. Honda Research Institute Japan Co., Ltd, Japan<br />
• We present online calibration of an<br />
asynchronous microphone array in<br />
which a location and a clock delay of each<br />
microphone are unknown.<br />
• The calibration is done by handclaps of a<br />
person walking around the microphone<br />
array with EKF-SLAM which<br />
simultaneously estimates a location and a<br />
clock delay of each microphone and a<br />
location of a sound source (handclapping).<br />
• We showed that at most 14 hand claps<br />
are necessary for calibration and then a<br />
sound source is successfully localized<br />
with the calibrated microphone array.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–25–<br />
Mic locations and<br />
delay are unknown.<br />
Before calibration<br />
Calibration<br />
(EKF-SLAM)<br />
Sound<br />
location<br />
After calibration<br />
Spatial Spectrum of Beamforming
Session MoBT3 Continental Parlor 3 Monday, September 26, <strong>2011</strong>, 16:00–17:30<br />
Symposium: Robot Audition: From Sound Source Localization to Automatic Speech Recognition<br />
Chair Kazuhiro Nakadai, Honda Res. Inst. Japan Co., Ltd.<br />
Co-Chair Hiroshi G. Okuno, Kyoto Univ.<br />
16:50–16:55 MoBT3.5<br />
HARK based Real-time Single Pane 3D Auditory<br />
Scene Visualizer Empowered by Speech Arrow<br />
Zheng Gong and Ichiro Hagiwara<br />
Tokyo Institute of Technology, Japan<br />
Kazuhiro Nakadai<br />
Honda Research Institute Japan Co., Ltd., Japan<br />
Tokyo Institute of Technology, Japan<br />
Hirofumi Nakajima<br />
Honda Research Institute Japan Co., Ltd., Japan<br />
• How to visually present information<br />
effectively to assist people in perception is<br />
a key issue for robot audition.<br />
• Speech Arrow is proposed to display<br />
recognized speeches in animated arrow<br />
shaped frames indicating sound source<br />
orientations.<br />
• A single pane auditory scene visualizer is<br />
implemented by combining visualization of<br />
several types of data in one window to<br />
increase robot auditory scene awareness.<br />
• The 3D vision technology is applied to<br />
enhance visual experiences.<br />
• The 3D sound is also employed to restore<br />
the original sound field.<br />
17:00–17:15 MoBT3.7<br />
A Scene-Associated Training Method for Mobile<br />
Robot Speech Recognition in Multisource<br />
Reverberated Environments<br />
Jindong Liu, Edward Johns and Guang-Zhong Yang<br />
The Hamlyn Centre, Imperial College London, United Kingdom<br />
• Aim: Train a speech recognition model<br />
working in varying reverberation<br />
environments<br />
• Measure room impulse<br />
response and group<br />
together similar auditory<br />
scenes to train a scenedependent<br />
speech<br />
recognition model<br />
• Utilise vision to<br />
recognise scenes and<br />
call the corresponding<br />
recognition model<br />
16:55–17:00 MoBT3.6<br />
Multi-modal Front-end for Speaker Activity<br />
Detection in Small Meetings<br />
Jani Even, Panikos Heracleous, Carlos Ishi and Norihiro Hagita<br />
ATR Intelligent Robotics and Communication Laboratories, Kyoto, Japan<br />
• Use LRF based human tracker for<br />
monitoring the movement of the<br />
participants<br />
• Assign one audio stream to each of the<br />
participants using the position from the<br />
human tracker<br />
• Beamforming technique is used to reduce<br />
background noise and interference<br />
• Use GMM trained before hand to detect<br />
speaker activity and identify the active<br />
speakers<br />
17:15–17:30 MoBT3.8<br />
The effects of microphone array processing on<br />
pitch extraction in real noisy environment<br />
Carlos T. Ishi, Dong Liang, Hiroshi Ishiguro, Norihiro Hagita<br />
ATR Intelligent Robotics and Communication Labs, Kyoto, Japan<br />
• Pitch extraction is important for humanrobot<br />
interaction, since pitch may carry<br />
information about intention, attitude or<br />
emotion expression<br />
• We propose a pitch extraction method<br />
by combining microphone array and<br />
auditory scene analysis technologies,<br />
and evaluate pitch extraction of multiple<br />
speakers in robot’s real noisy<br />
environment<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–26–<br />
Correlogram of baseline system<br />
Correlogram of proposed method<br />
Pitch of the target speech
Session MoBT4 Continental Ballroom 4 Monday, September 26, <strong>2011</strong>, 16:00–17:30<br />
Symposium: Robot Demonstrations<br />
Chair Oliver Brock, Tech. Univ. Berlin<br />
Co-Chair Kurt Konolige, Willow Garage<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–27–
Session MoBT5 Continental Ballroom 5 Monday, September 26, <strong>2011</strong>, 16:00–17:30<br />
Symposium: Bio-inspired Robotics II<br />
Chair K. H. Low, Nanyang Tech. Univ.<br />
Co-Chair Ravi Vaidyanathan, Imperial Coll. London<br />
16:00–16:15 MoBT5.1*<br />
Semi-Plenary Invited Talk: Robotics at Boston<br />
Dynamics<br />
Gabriel Nelson, Boston Dynamics<br />
16:30–16:45 MoBT5.3<br />
Design of a Miniature Integrated Multi-Modal<br />
Jumping and Gliding Robot<br />
Matthew A. Woodward and Metin Sitti<br />
Dept. of Mechanical Engineering, Carnegie Mellon University, USA<br />
• Multiple locomotion modes can<br />
enhance a system’s performance<br />
in unstructured terrain.<br />
• A integrated strategy for<br />
developing systems which exhibit<br />
multiple locomotion modes is<br />
proposed.<br />
• A highly integrated robot is<br />
developed which utilizes<br />
approximately 80% of the system<br />
mass for the jumping mode and<br />
67% for the gliding mode.<br />
• This robot is capable of jumping<br />
heights of 6 meters with a mass of<br />
under 100 grams.<br />
16:15–16:30 MoBT5.2*<br />
Semi-Plenary Invited Talk: Progress on 100 Milligram<br />
Robotic Insects<br />
Robert Wood, Harvard University<br />
16:45–16:50 MoBT5.4<br />
ScarlETH: Design and Control of a Planar<br />
Running Robot<br />
Marco Hutter, C. David Remy, Mark A. Höpflinger,<br />
Roland Siegwart<br />
Autonomous Systems Lab, ETH Zurich, Switzerland<br />
• Design of an articulated robotic leg<br />
based on high compliant series<br />
elastic actuation<br />
• Combination of versatility and<br />
efficiency:<br />
• precise torque control<br />
• fast position control<br />
• efficient energy storage<br />
• maximal range of motion<br />
• Virtual model control is applied for<br />
hopping in 2D:<br />
• virtual spring in vertical direction<br />
• angle of attack and force control<br />
in horizontal direction<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–28–
Session MoBT5 Continental Ballroom 5 Monday, September 26, <strong>2011</strong>, 16:00–17:30<br />
Symposium: Bio-inspired Robotics II<br />
Chair K. H. Low, Nanyang Tech. Univ.<br />
Co-Chair Ravi Vaidyanathan, Imperial Coll. London<br />
16:50–16:55 MoBT5.5<br />
Modeling and Control on Hysteresis Nonlinearity<br />
in Biomimetic Undulating Fins<br />
Tianjiang Hu, Huayong Zhu, Han Zhou, Lincheng Shen<br />
College of Mechatronics and Automation, Natioanl University of Defense Technology (NUDT), China<br />
K. H. Low<br />
School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore<br />
• Biomimetic undulating fins are considered<br />
with focus on their hysteresis nonlinearity,<br />
which causes effect on biomimetic<br />
investigation of fish swimming.<br />
• Hysteresis is confirmed with experimental<br />
data on RoboGnilos, and qualitative<br />
modeling on this nonlinear action is then<br />
achieved by using Preisach equations.<br />
• The developed iterative learning control is<br />
applied to eliminate hysteresis nonlinearity<br />
in biorobotic undulating fins.<br />
• Simulation and experiments show that the<br />
proposed control method is effective and<br />
feasible to improve the tracking<br />
performance of biorobotic fins.<br />
17:00–17:15 MoBT5.7<br />
Biologically Derived Models of the Sunfish for<br />
Investigations of Multi-Fin Swimming<br />
James Tangorra, Anthony Mignano, Gabe Carryon, and Jeff Kahn<br />
Department of Mechanical Engineering, Drexel University, USA<br />
• Biologically derived models of the<br />
sunfish, fin central pattern<br />
generators, and pectoral fin<br />
sensory systems were developed.<br />
• The biorobotic fish enables<br />
interdependencies of sub-systems<br />
to be investigated during multi-fin<br />
swimming.<br />
• Neural oscillator networks<br />
generate gaits for steady<br />
swimming and maneuvers, and<br />
makes stable transitions.<br />
• Relationships were determined<br />
between distributed sensory<br />
information and fin forces.<br />
Biorobotic sunfish, instrumented pectoral<br />
fins, and neural oscillator network.<br />
16:55–17:00 MoBT5.6<br />
Translational Damping on Flapping Cicada<br />
Wings<br />
Perry Parks, Bo Cheng, Zheng Hu, and Xinyan Deng<br />
School of Mechanical Engineering, Purdue University<br />
• Robotic insect thorax mechanism<br />
• Flaps up to 65Hz,and weigh 2.86g<br />
including the motor and wing<br />
• Transitional damping when body moves<br />
• Indirect measurement using a pendulum<br />
• Compared to analytical estimation from<br />
Blade-Element Analysis<br />
• Good agreement with estimation in<br />
forward-backward and left-right translation<br />
• Asymmetry in up-down translation<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–29–<br />
Man-made (top) and cicada<br />
(bottom) wings mounted on the<br />
high frequency robotic thorax<br />
mechanism.<br />
17:15–17:30 MoBT5.8<br />
Dynamic Modeling of Robotic Fish and<br />
Its Experimental Validation<br />
Jianxun Wang, Freddie Alequin-Ramos and Xiaobo Tan<br />
Department of Electrical and Computer Engineering,<br />
Michigan State University, MI, USA<br />
• Model carangiform robotic fish<br />
motion by merging rigid-body<br />
dynamics with Lighthill’s largeamplitude<br />
elongated-body theory<br />
• Introduce an effective approach to<br />
the evaluation of time-varying drag<br />
force and moment coefficients<br />
• Show that the pressure force acting<br />
at the tail tip plays a significant role<br />
• Validate the proposed approach<br />
with experiments on a freeswimming<br />
robotic fish<br />
A robotic fish prototype with servo-driven<br />
rigid tail, used for model validation
Session MoBT6 Continental Ballroom 6 Monday, September 26, <strong>2011</strong>, 16:00–17:30<br />
Symposium: Field Robotics II<br />
Chair David A. Anisi, ABB<br />
Co-Chair Satoshi Tadokoro, Tohoku Univ.<br />
16:00–16:15 MoBT6.1*<br />
Semi-Plenary Invited Talk: Recent Developments in<br />
Robotics Technology and Flight Implementation at JPL<br />
Richard Volpe, Jet Propulsion Laboratory, Caltech<br />
16:30–16:45 MoBT6.3<br />
Path Planning and Evaluation for Planetary<br />
Rovers Based on Dynamic Mobility Index<br />
Genya Ishigami<br />
Japan Aerospace Exploration Agency, Japan<br />
Keiji Nagatani and Kazuya Yoshida<br />
Tohoku University, Japan<br />
• Three-step path planning and<br />
evaluation method is proposed.<br />
• Step 1: Path generation on a given<br />
terrain map with a basic planning<br />
algorithm.<br />
• Step 2: Dynamic simulation<br />
providing metrics for robotic<br />
mobility.<br />
• Step 3: Path evaluation based on<br />
dynamic mobility index.<br />
• Demonstrations for the path planning<br />
and evaluation are presented.<br />
Dynamic simulation profile for<br />
each candidate path<br />
16:15–16:30 MoBT6.2<br />
On On-Orbit Passive Object Handling by<br />
Cooperating Space Robotic Servicers<br />
Georgios Rekleitis and Evangelos Papadopoulos<br />
Department of Mechanical Engineering<br />
National Technical University of Athens, Athens, Greece<br />
• A technique for 3D on-orbit manipulation of a passive object by<br />
free-flying servicers is presented<br />
• On-off thrusters and proportional action<br />
manipulators used<br />
• Passive object trajectory<br />
tracking motion strategy and<br />
optimal end-effector contact point selection via a two-layer optimization<br />
• Novel, model based controller, adapted to the special requirements is<br />
presented. Performance and robustness are studied via simulations<br />
• Comparison with standard on-off control shows reduced fuel consumption<br />
16:45–16:50 MoBT6.4<br />
Control of a Passively Steered Rover<br />
using 3-D Kinematics<br />
Neal Seegmiller and David Wettergreen<br />
Robotics Institute, Carnegie Mellon University, USA<br />
• 3-D kinematic control for<br />
passively-steered rovers<br />
• The passive-steering design is<br />
reliable and efficient, but<br />
challenging to control<br />
• The 3-D controller uses inertial &<br />
proprioceptive sensing for<br />
accurate steering on rough<br />
terrain<br />
• The controller was validated in<br />
both simulation and physical<br />
experiments<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–30–<br />
Experiment, simulation, and diagram of the Zoë<br />
rover traversing an obstacle with 3-D control
Session MoBT6 Continental Ballroom 6 Monday, September 26, <strong>2011</strong>, 16:00–17:30<br />
Symposium: Field Robotics II<br />
Chair David A. Anisi, ABB<br />
Co-Chair Satoshi Tadokoro, Tohoku Univ.<br />
16:50–16:55 MoBT6.5<br />
Optical Flow Odometry with<br />
Robustness to Self-shadowing<br />
Neal Seegmiller and David Wettergreen<br />
Robotics Institute, Carnegie Mellon University, USA<br />
• Efficient optical flow odometry for mobile<br />
robots using a single downward looking<br />
camera.<br />
• Robustness to self-shadowing errors,<br />
even in extreme cases when the<br />
shadow dominates the image.<br />
• Method: (1) prevent feature selection<br />
near shadow edges (2) outlier rejection<br />
based on rigidity constraint<br />
• Experimental results: the presented<br />
algorithm accurately estimates velocity<br />
when outlier rejection alone fails<br />
• Tested at 2 m/s (algorithm design permits<br />
higher speeds). Odometry error 2-3% of<br />
distance traveled.<br />
Tracked features in image<br />
dominated by the LATUV shadow<br />
17:00–17:15 MoBT6.7<br />
Time-Optimal Detumbling Maneuver<br />
along an Arbitrary Arm Motion<br />
during the Capture of a Target Satellite<br />
Tomohisa Oki Satoko Abiko<br />
MDA Space Missions, Canada Tohoku University, Japan<br />
Hiroki Nakanishi Kazuya Yoshida<br />
Japan Aerospace Exploration Agency, Japan Tohoku University, Japan<br />
• Time-Optimal control method to<br />
stabilize a detumbling satellite is<br />
proposed<br />
• Combined system dynamics between<br />
a free-floating robot system and a<br />
target satellite is presented<br />
• Detumbling operation along an<br />
arbitrary arm motion is parameterized<br />
by a single parameter<br />
• The concept of detumbling speed is<br />
proposed and advantageous when<br />
handling parameter uncertainty in the<br />
target dynamics<br />
• Numerical simulation result verifies<br />
the proposed control scheme<br />
16:55–17:00 MoBT6.6<br />
Vision-based Space Autonomous Rendezvous :<br />
A Case Study<br />
Antoine Petit<br />
IRISA/INRIA Rennes, France<br />
Eric Marchand<br />
IRISA, Université de Rennes 1, France<br />
Keyvan Kanani<br />
Astrium, France<br />
• 3D model-based tracking for the<br />
navigation of the final phase of a space<br />
rendezvous.<br />
• Tests on a mock-up of a<br />
satellite with a 6-DOF<br />
robotic arm, in open loop.<br />
• Precision, real-time. Robustness to<br />
motions, orientation, illumination.<br />
• 2 1/2 D visual servoing,<br />
using pose estimation,<br />
with satisfactory results.<br />
17:15–17:30 MoBT6.8<br />
3D SLAM for Planetary Worksite Mapping<br />
Chi Hay Tong and Timothy D. Barfoot<br />
Institute for Aerospace Studies, University of Toronto, Canada<br />
Erick Dupuis<br />
Space Exploration, Canadian Space Agency, Canada<br />
• 3D SLAM framework designed for the planetary worksite mapping task<br />
using a laser rangefinder mounted on a mobile rover platform<br />
• Utilizes a combination of sparse features and dense data for data<br />
association<br />
• Post-alignment automatic verification check to ensure map quality<br />
• Experimental validation at two planetary analogue test facilities<br />
Map of our planetary analogue test facility created by the 3D SLAM framework.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–31–
Session MoBT7 Continental Parlor 7 Monday, September 26, <strong>2011</strong>, 16:00–17:30<br />
Teleoperation<br />
Chair Nikhil Chopra, Univ. of Maryland, Coll. Park<br />
Co-Chair Patrick van der Smagt, DLR<br />
16:00–16:15 MoBT7.1<br />
A Constrained Optimization Approach to Virtual<br />
Fixtures for Multi-Robot Collaborative Tele-op<br />
Tian Xia and Peter Kazanzides and Russell Taylor<br />
Computer Science, Johns Hopkins University, USA<br />
Ankur Kapoor<br />
National Institute of Health, USA<br />
• Developed a system for roboticassisted<br />
manipulation using the<br />
da Vinci ® surgical robot<br />
• Used constrained optimization<br />
to implement multi-robot virtual<br />
fixtures for surgical knot<br />
positioning<br />
• Used virtual fixtures to maintain<br />
spatial and temporal<br />
relationships between robots to<br />
complete task<br />
• Used virtual fixtures provide<br />
haptic feedback to user<br />
Master<br />
Controller<br />
Slave<br />
Controller<br />
F c m<br />
Transformations<br />
- + F<br />
Fs<br />
d s<br />
∆X d∗<br />
sl<br />
∆X d sl<br />
Collaborative<br />
Controller<br />
Master/Slave Pair<br />
F c m<br />
Transformations<br />
F d s<br />
+<br />
- Fs<br />
∆X d sr<br />
∆X d∗<br />
sr<br />
Control algorithm overview<br />
Master<br />
Controller<br />
Slave<br />
Controller<br />
16:30–16:45 MoBT7.3<br />
Small Gain Design of Cooperative Teleoperator<br />
System with Projection-Based Force Reflection<br />
Ilia Polushin and Amir Takhmar and Rajni V. Patel<br />
Department of Electrical and Computer Engineering,<br />
University of Western Ontario, Canada<br />
• Developments to the small-gain approach<br />
to the design of networked cooperative<br />
force-reflecting teleoperators are<br />
presented<br />
• Explicit assumptions on the human<br />
dynamics are incorporated into the<br />
stability analysis<br />
• The conservatism of the small-gain design<br />
is eliminated using the projeciton-based<br />
force reflection principle<br />
• Samples of the experimental results are<br />
presented<br />
Cooperative teleoperator system<br />
16:15–16:30 MoBT7.2<br />
A Task-space Weighting Matrix Approach to<br />
Semi-autonomous Teleoperation Control<br />
Pawel Malysz and Shahin Sirouspour<br />
Department of Electrical and Computer Engineering<br />
McMaster University, Canada<br />
• A new general framework for shared teleoperation/autonomous control<br />
of mobile base and/or twin-armed manipulator robots<br />
• Hierarchical controller based on<br />
task-level coordinating velocity<br />
commands and low-level joint<br />
velocity control<br />
• A task-space weighting matrix<br />
adjusts relative priority between<br />
autonomous and human<br />
teleoperation control<br />
• Rigorous performance and<br />
stability analysis<br />
• Experimental evaluation in<br />
teleoperation of a twin-armed<br />
Twin-armed Mobile Robot<br />
mobile slave robot<br />
16:45–16:50 MoBT7.4<br />
An Enhanced Sliding-Mode Control for a<br />
Pneumatic-Actuated Teleoperation System<br />
M.Q. Le1 , M.T. Pham1 , M. Tavakoli2 , R. Moreau1 1 Laboratoire Ampère, UMR CNRS 5005, Université de Lyon, France<br />
2 Department of Electrical and Computer Engineering, University of Alberta, Canada<br />
• This paper presents an enhanced sliding mode control for<br />
pneumatic master-slave teleoperation systems with low-cost<br />
solenoid valves<br />
• By using 2 additional modes, the system dynamic performance<br />
is improved and the valve’s switching activities is reduced<br />
• The proposed control design is experimentally verified on a<br />
two-channel bilateral teleoperation architecture involving<br />
position-position, force-force, or force-position schemes<br />
• Good transparency and stability were achieved<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–32–<br />
Pressure sensors<br />
Solenoid valves<br />
Force sensor<br />
Mass<br />
Position sensor Cylinder<br />
Environement
Session MoBT7 Continental Parlor 7 Monday, September 26, <strong>2011</strong>, 16:00–17:30<br />
Teleoperation<br />
Chair Nikhil Chopra, Univ. of Maryland, Coll. Park<br />
Co-Chair Patrick van der Smagt, DLR<br />
16:50–16:55 MoBT7.5<br />
Subspace-oriented Energy Distribution for the<br />
Time Domain Passivity Approach<br />
Christian Ott, Jordi Artigas, and Carsten Preusche<br />
Institute of Robotics and Mechatronics,<br />
German Aerospace Center (DLR), Germany<br />
• Time Domain Passivity Approach applies<br />
a dissipative control action in order to<br />
preserve passivity.<br />
• Requires a criterion for the dissipation<br />
among multiple degrees of freedom.<br />
• We propose an energy based formulation<br />
for distribution of damping to task<br />
space and nullspace of a redundant<br />
manipulator.<br />
• The solution avoids disturbance of the<br />
task space, by prioritizing dissipation in<br />
the nullspace.<br />
• The method is based on an optimization<br />
formulation and allows to consider<br />
constraints in the maximum torques and<br />
damping values.<br />
passivity observer<br />
task space<br />
damping<br />
subspace<br />
damping<br />
distribution<br />
nullspace<br />
damping<br />
17:00–17:15 MoBT7.7<br />
Semi-Autonomous Teleoperation in Task Space<br />
with Redundant Slave Robot<br />
Yen-Chen Liu and Nikhil Chopra<br />
Department of Mechanical Engineering, University of Maryland,<br />
College Park, USA<br />
• Difficult to achieve efficient task space<br />
teleoperation of complex robots operating<br />
in cluttered environments.<br />
• Redundancy in the slave robot can be<br />
utilized to autonomously achieve several<br />
sub-tasks.<br />
• Control algorithms developed to<br />
guarantee task space tracking in the<br />
presence of uncertainties and delays.<br />
• Algorithm is validated with sub-tasks such<br />
as singularity avoidance, joint limits, and<br />
collision avoidance.<br />
Configurations of the slave<br />
robot with sub-task control<br />
16:55–17:00 MoBT7.6<br />
EMG-Based Teleoperation and Manipulation<br />
with the DLR LWR-III<br />
Jörn Vogel, Claudio Castellini and Patrick van der Smagt<br />
Institute of Robotics and Mechatronics, German Aerospace Center, Germany<br />
• Decoding wrist position, orientation<br />
and grasp force from surface<br />
electromyography<br />
• Control of a lightweight robotic arm<br />
and hand using the decoded signals<br />
• No kinematic model of the users arm<br />
or precise positioning of sEMGelectrodes<br />
needed<br />
• Possible applications in the field of<br />
variable stiffness robotics and<br />
rehabilitation<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–33–<br />
Schematic overview<br />
of the system<br />
17:15–17:30 MoBT7.8<br />
Noninvasive Brain-Computer Interface-based<br />
Control of Humanoid Navigation<br />
Yongwook Chae1 , Jaeseung Jeong2 , and Sungho Jo1 1Department of Computer Science, KAIST, Korea<br />
2Department of Bio and Brain Engineering, KAIST, Korea<br />
Key Features<br />
1) Natural & Direct Brain-actuated<br />
Navigation<br />
Mapped with three natural motor<br />
intentions into the five low level<br />
motion commands<br />
2) Direct-Control System<br />
Free for the external cues and<br />
environmental restrictions<br />
3) Asynchronous BCI System<br />
User can command the humanoid at<br />
any time<br />
4) Postural Dependent Control<br />
Not based on environmental<br />
condition (applicable to any<br />
environments)
Session MoBT8 Continental Parlor 8 Monday, September 26, <strong>2011</strong>, 16:00–17:30<br />
Learning for Control<br />
Chair Jun Nakanishi, Univ. of Edinburgh<br />
Co-Chair Jorg Conradt, Tech. Univ. München<br />
16:00–16:15 MoBT8.1<br />
Adding a Receding Horizon to Locally Weighted<br />
Regression for Learning Robot Control<br />
Christopher Lehnert and Gordon Wyeth<br />
Queensland University of Technology, Australia<br />
• Locally Weighted Regression (LWR)<br />
fails to learn a useful controller when<br />
the system is temporally dependent.<br />
• We define the concept of a horizon of<br />
temporal dependence, and illustrate<br />
why LWR fails to learn.<br />
• By introducing a receding horizon of<br />
future output states of the system, we<br />
show that sufficient constraint is<br />
applied to learn good solutions.<br />
• Our new method, Receding Horizon<br />
Locally Weighted Regression, is<br />
demonstrated through one-shot<br />
learning on a real Series Elastic<br />
Actuator controlling a pendulum.<br />
Control response of Series Elastic<br />
Actuator after 20 seconds of training<br />
using Receding Horizon Locally<br />
Weighted Regression.<br />
16:30–16:45 MoBT8.3<br />
Learning Task-Space Control with Kernels<br />
D. Nguyen-Tuong1 and J. Peters1,2 1Max Planck Institute for Intelligent Systems, Germany<br />
2Universität Darmstadt, Intelligent Autonomous Systems, Germany<br />
• Learning models for task-space control<br />
is an ill-posed problem, i.e., learning<br />
one-to-many mappings.<br />
• We propose a local learning approach<br />
appropriate for learning one-to-many<br />
mappings.<br />
• The proposed approach is based on the insight that despite being a<br />
globally ill-posed problem, such task-space control mapping is locally<br />
well-defined. We show in simulation the ability of the method for online<br />
model learning for task-space control of redundant robots.<br />
16:15–16:30 MoBT8.2<br />
Learning Inverse Kinematics<br />
with Structured Prediction<br />
1 Botond Bócsi, 2 Duy Nguyen-Tuong, 1 Lehel Csató,<br />
2 Bernhard Schölkopf, 2 Jan Peters<br />
1 Faculty of Mathematics and Informatics, Babes-Bolyai University, Romania<br />
2 Dep. of Emp. Inf., Max Planck Inst. for Intelligent Sys., Tubingen, Germany<br />
Technische Universitaet Darmstadt, Intelligent Autonomous Systems Group,<br />
Germany<br />
• Inverse kinematics mappings are<br />
multivalued functions, thus, they are<br />
ill-defined<br />
• We model the joint input-output (task<br />
space position - joint space<br />
configuration) mapping and<br />
maximize over the output space<br />
• The maximization is possible using<br />
gradient search started from the<br />
current arm position<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–34–<br />
Inverse kinematics<br />
prediction scheme<br />
16:45–16:50 MoBT8.4<br />
Learning to Control Planar Hitting Motions in a<br />
Minigolf-like Task<br />
K.Kronander, S.M Khansari-Zadeh and A.Billard<br />
Learning Algorithms and Systems Laboratory (LASA),<br />
Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland<br />
• We present a robotic system for<br />
learning to play minigolf from<br />
human demonstrations.<br />
• First, the robot is taught a basic<br />
hitting motion using kinesthetic<br />
demonstrations.<br />
• Then, the robot learns a hitting<br />
motion adaptation model from<br />
examples provided by the<br />
teacher.<br />
• Neither physical model of the<br />
field or reward-function need to<br />
be specified.<br />
The 7-dof Barrett WAM playing minigolf
Session MoBT8 Continental Parlor 8 Monday, September 26, <strong>2011</strong>, 16:00–17:30<br />
Learning for Control<br />
Chair Jun Nakanishi, Univ. of Edinburgh<br />
Co-Chair Jorg Conradt, Tech. Univ. München<br />
16:50–16:55 MoBT8.5<br />
Stiffness and Temporal Optimization in Periodic<br />
Movements: An Optimal Control Approach<br />
Jun Nakanishi, Konrad Rawlik and Sethu Vijayakumar<br />
IPAB, School of Informatics, University of Edinburgh, UK<br />
• Present a novel framework for stiffness<br />
and temporal optimization of periodic<br />
movements<br />
• Exploit the intrinsic passive dynamics to<br />
realize efficient actuation and control<br />
• Dynamical systems based representation<br />
of rhythmic movements<br />
• Propose a systematic methodology to<br />
optimize for control commands, temporal<br />
aspect of movements and time-varying<br />
stiffness profiles from first principles of<br />
optimality<br />
• Evaluations on a single pendulum and an<br />
underactuated two-link brachiating robot<br />
simulation<br />
Temporally optimized movement<br />
of the swing locomotion task<br />
17:00–17:15 MoBT8.7<br />
Behavioural Cloning for Driving Robots<br />
over Rough Terrain<br />
Raymond Sheh, Bernhard Hengst and Claude Sammut<br />
ARC Centre of Excellence for Autonomous Systems<br />
School of Computer Science and Engineering<br />
The University of New South Wales, Sydney, Australia<br />
• Controllers for driving robots over<br />
rough terrain need to consider robotterrain<br />
interactions that are traditionally<br />
difficult to model (eg. body contact).<br />
• We use Behavioural Cloning in<br />
simulation and reality to directly learn<br />
the control task without explicit system<br />
modeling.<br />
• We also develop an autonomous<br />
demonstrator to generate training data.<br />
• Generated policies found to transfer<br />
directly from simulation to reality.<br />
• On-task performance exceeds basic<br />
controllers and is comparable to the<br />
human expert.<br />
16:55–17:00 MoBT8.6<br />
Improving Operational Space Control of Heavy<br />
Manipulators via Open-Loop Compensation<br />
Guilherme Maeda, Surya Singh, David Rye<br />
Australian Centre for Field Robotics,<br />
The University of Sydney, Australia<br />
• Hydraulic manipulators present<br />
high friction and dynamic coupling<br />
making it difficult to obtain a global<br />
model with acceptable estimation<br />
error<br />
• Model-based compensation based<br />
on such models have negative<br />
impact on performance and may<br />
lead to instability<br />
• Open-loop commands generate<br />
stable partial compensation<br />
• In open-loop, the model can be<br />
local, requiring only dynamic<br />
knowledge in the neighborhood of<br />
the desired trajectory<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–35–
Session MoBT9 Continental Parlor 9 Monday, September 26, <strong>2011</strong>, 16:00–17:30<br />
Novel Actuators II<br />
Chair Cagdas Denizel Onal, Massachusetts Inst. of Tech.<br />
Co-Chair Ohmi Fuchiwaki, Yokohama National Univ. (YNU)<br />
16:00–16:15 MoBT9.1<br />
Sliding-Mode Control of<br />
Nonlinear Discrete-Input Pneumatic Actuators<br />
Sean Hodgson 1 , Minh Quyen Le 2 ,<br />
Mahdi Tavakoli 1 , Minh Tu Pham 2<br />
1 Electrical and Computer Engineering, University of Alberta, Canada<br />
2 Laboratoire Ampère, Université de Lyon, France<br />
• This paper proposes a sliding mode law<br />
for precise position control with minimal<br />
switching activity.<br />
• This control law is applied to 1 DOF<br />
pneumatic actuator utilizing on/off<br />
solenoid valves.<br />
• Utilizes 4 new modes of operation that use<br />
necessary and sufficient amounts of drive<br />
energy.<br />
• Simulated and experimental results<br />
demonstrate improved system<br />
performance!<br />
Pneumatic Actuator<br />
16:30–16:45 MoBT9.3<br />
Development of<br />
a Miniature Foil Type Ultrasonic Motor<br />
Jun Okamoto<br />
Institute of Advanced Biomedical Engineering & Science,<br />
Tokyo Women's Medical University, Japan<br />
Shigeki Toyama<br />
Department of Mechanical Systems Engineering,<br />
Tokyo University of Agriculture and Technology, Japan<br />
• A traveling wave type miniature<br />
ultrasonic motor using a foil type stator<br />
is proposed<br />
• It is found that the main driving force<br />
was the generated symmetric lamb<br />
wave which rotates the coil surface<br />
forward direction<br />
• Rotational speed was found to be 6,380<br />
rpm and starting torque was 0.698 Nm<br />
when the ideal gap between rotor and<br />
stator was realized<br />
• The performance of the developed<br />
prototype was found quite good<br />
compared to the earlier miniature motors<br />
0.8mm<br />
FEM Analysis<br />
Endurance test<br />
Foil type stator<br />
Wave<br />
direction<br />
16:15–16:30 MoBT9.2<br />
Trajectory Planning and Current Control Optimization<br />
of Three Degree-of-Freedom Spherical Actuator<br />
Liang Zhang, Weihai Chen, Liang Yan, and Jingmeng Liu<br />
School of Automation Science and Electrical Engineering,<br />
Beihang University, Beijing, China<br />
• An orientation representation<br />
method is proposed to<br />
facilitate the trajectory<br />
planning of rotor<br />
• A trajectory planning method<br />
which can generate smooth<br />
spline in two-step is utilized<br />
Working<br />
principle<br />
Trajectory<br />
planning<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–36–<br />
Desired torque<br />
Torque<br />
modeling<br />
Spherical actuator<br />
Current control<br />
optimization<br />
• Optimization algorithm for current is developed to improve the power<br />
efficiency and fault tolerance capability<br />
• Simulations are carried out to validate the proposed method and algorithm<br />
16:45–16:50 MoBT9.4<br />
Soft Robot Actuators using Energy-Efficient<br />
Valves Controlled by<br />
Electropermanent Magnets<br />
Andrew D. Marchese, Cagdas D. Onal, and Daniela Rus<br />
Electrical Engineering and Computer Science, MIT, USA<br />
•Authors employ novel<br />
electropermanent magnet (EPM)<br />
valves to drive actuation of a soft,<br />
multi-segment robot.<br />
•When used in driving soft<br />
actuators, EPM valves can be<br />
compact, light weight, and energy<br />
efficient.<br />
•The forward locomotion of a soft<br />
robot consisting of six compliant<br />
fluidic actuators was controlled by<br />
EPM valves.<br />
EPM valves were used to<br />
drive a six segment soft<br />
rolling robot. Segments<br />
were placed around the<br />
perimeter of a cylinder.
Session MoBT9 Continental Parlor 9 Monday, September 26, <strong>2011</strong>, 16:00–17:30<br />
Novel Actuators II<br />
Chair Cagdas Denizel Onal, Massachusetts Inst. of Tech.<br />
Co-Chair Ohmi Fuchiwaki, Yokohama National Univ. (YNU)<br />
16:50–16:55 MoBT9.5<br />
Synthesis of a Non-Circular Cable Spool<br />
to Realize a Nonlinear Rotational Spring<br />
Nicolas Schmit and Masafumi Okada<br />
Dept. of Mech. Sciences and Eng., Tokyo Institute of Technology, Japan<br />
• Closed-form solution of the non-circular<br />
spool which synthesizes a nonlinear<br />
torque profile.<br />
• Realization of a constant force spring, an<br />
exponential softening spring and a cubic<br />
polynomial spring.<br />
• Average experimental error: 1.5%<br />
17:00–17:15 MoBT9.7<br />
Variable Impedance due to Electromechanical<br />
Coupling in Electroactive Polymer Actuators<br />
Sanjay Dastoor and Mark Cutkosky<br />
Department of Mechanical Engineering, Stanford University, USA<br />
• Electroactive polymers (EAPs) have<br />
inherent compliance and damping, making<br />
them attractive for bio-inspired systems<br />
• A novel control circuit can be used to<br />
change the electrical boundary conditions<br />
on the EAP<br />
• A quasi-viscoelastic model can be used to<br />
to simply model the dynamics of EAPs<br />
• Through dynamics modeling and electrical<br />
control, open-loop passive impedance can<br />
be varied, increasing the performance of<br />
bio-inspired robots.<br />
EAP actuator, variable<br />
impedance trajectory, and<br />
perching application<br />
16:55–17:00 MoBT9.6<br />
Combining IBVS and PBVS to ensure the<br />
visibility constraint<br />
Olivier Kermorgant and François Chaumette<br />
Lagadic, INRIA Rennes-Bretagne Atlantique, France<br />
• Hybrid visual servoing based on a generic fusion approach<br />
• Pure PBVS is performed inside a safe image area<br />
• 2D information is added to keep the object in the image<br />
• Validation with simulation and experiments<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–37–<br />
Visual servo with a 3D tracking<br />
The model nodes are used as 2D<br />
features when they approach the<br />
image border<br />
17:15–17:30 MoBT9.8<br />
Optimal Control of Multi-Input SMA Actuator<br />
Arrays Using Graph Theory<br />
Leslie Flemming, David E. Johnson and Stephen Mascaro<br />
Department of Mechanical Engineering, University of Utah, USA<br />
• Wet Shape Memory Alloy actuators are compact, high force-to-weight<br />
actuators that can be activated electrically or thermofluidically.<br />
• A scalable architecture of 2N switches/valves controls the delivery of<br />
thermal energy to an NxN array of actuators.<br />
• Expanding wavefront graph theory is used to identify optimal set of<br />
discrete control commands to operate the array.<br />
A partial graph of a 2x2 array of<br />
wet SMA actuators controlled by<br />
thermofluidic and electric inputs<br />
1 0<br />
0 0<br />
00<br />
CC 00<br />
EE<br />
B<br />
00<br />
HH<br />
1 0<br />
1 1<br />
EE<br />
EE<br />
HH<br />
HH<br />
0H<br />
00<br />
0E<br />
00<br />
0C<br />
00<br />
A C<br />
CC<br />
CC<br />
1 1<br />
1 1<br />
Legend<br />
Node<br />
Edge<br />
Hot Fluid<br />
Cold Fluid<br />
Electricity
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–38–
8:00-9:30<br />
Technical Program Digest<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–39–<br />
Tuesday<br />
September 27, <strong>2011</strong><br />
Session TuAT1 ⎯⎯ Object Recognition, Segmentation, and Detection<br />
Session TuAT2 ⎯⎯ Simultaneous Localization and Mapping<br />
Session TuAT3 ⎯⎯ Symposium: Microrobotics I<br />
Session TuAT4 ⎯⎯ Industrial Forum: Robots: The New Commercial Platforms<br />
Session TuAT5 ⎯⎯ Symposium: Medical Robotics I<br />
Session TuAT6 ⎯⎯ Symposium: Grasping and Manipulation: Control and Learning<br />
Session TuAT7 ⎯⎯ Software Architectures & Frameworks<br />
Session TuAT8 ⎯⎯ Bio-Inspired & Biomimetic Robots<br />
Session TuAT9 ⎯⎯ Probabilistic Exploration and Coverage<br />
10:00-11:30<br />
Session TuBT1 ⎯⎯ Perception, Saliency and Novelty<br />
Session TuBT2 ⎯⎯ Semantic SLAM & Loop Closure<br />
Session TuBT3 ⎯⎯ Symposium: Microrobotics II<br />
Session TuBT4 ⎯⎯ Industrial Forum: Robots: The Next Generation<br />
Session TuBT5 ⎯⎯ Symposium: Medical Robotics II<br />
Session TuBT6 ⎯⎯ Symposium: Grasping and Manipulation: Mechanics and Design<br />
Session TuBT7 ⎯⎯ Contact and Deformation<br />
Session TuBT8 ⎯⎯ Biomimetic Limbed Robots<br />
Session TuBT9 ⎯⎯ Perceptual Learning<br />
(continues on next page)
14:00-15:30<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–40–<br />
Tuesday<br />
September 27, <strong>2011</strong><br />
(continued)<br />
Session TuCT1 ⎯⎯ Object Detection & Collision Avoidance<br />
Session TuCT2 ⎯⎯ Motion Estimation, Mapping & SLAM<br />
Session TuCT3 ⎯⎯ Symposium: Microrobotics III<br />
Session TuCT4 ⎯⎯ Forum: Robotics: Beyond the Horizon<br />
Session TuCT5 ⎯⎯ Symposium: Medical Robotics III<br />
Session TuCT6 ⎯⎯ Symposium: Grasping and Manipulation: Grasp Planning and<br />
Quality<br />
Session TuCT7 ⎯⎯ Mechanisms: Actuator Design<br />
Session TuCT8 ⎯⎯ Reptile & Fish-inspired Robots<br />
Session TuCT9 ⎯⎯ Novel Sensors
Session TuAT1 Continental Parlor 1 Tuesday, September 27, <strong>2011</strong>, 08:00–09:30<br />
Object Recognition, Segmentation, and Detection<br />
Chair Trevor Darrell, UC Berkeley<br />
Co-Chair Ashley Desmond Tews, CSIRO<br />
08:00–08:15 TuAT1.1<br />
Robust Stereo-Vision Based 3D Modeling of<br />
Real World Objects for Assistive Robotic<br />
Applications<br />
S.K. Natarajan, D. Ristic-Durrant, A. Leu and A. Gräser<br />
Institute of Automation, University of Bremen, Germany<br />
• Novel stereo vision based segmentation of textured and uniformly<br />
colored objects<br />
• Robust closed-loop segmentation of uniformly colored segments in<br />
irregularly shaped object ROIs<br />
• Accurate 2D shape extraction for 3D object modeling<br />
• Needs no a-priori knowledge on object geometry<br />
Block Diagram of the proposed 3D Object Reconstruction System<br />
08:30–08:45 TuAT1.3<br />
Integrate Multi-Modal Cues for Category-<br />
Independent Object Detection and Localization<br />
Jianhua Zhang, Junhao Xiao and Jianwei Zhang<br />
Dept. Informatics, University of Hamburg, Germany<br />
Houxiang Zhang<br />
Dept. Technology and Nautical Sciences, Aalesund University College, Norway<br />
Shengyong Chen<br />
College of Computer Science, Zhejiang University of Technology, China<br />
• To detect and localize object instances<br />
that is category-independent<br />
• Our study uses multi-modal cues,<br />
including multi-scale saliency, superpixels<br />
straddling, intensity, depth and global<br />
information, into a uniform Bayesian<br />
framework to obtain accurate detection<br />
and localization.<br />
• Experiments show the promising<br />
performance of the proposed method<br />
based on the PASCAL VOC 08 dataset<br />
and our indoor scene dataset.<br />
Some experimental results of our<br />
study<br />
08:15–08:30 TuAT1.2<br />
Practical 3-D Object Detection Using Category<br />
and Instance-level Appearance Models<br />
Kate Saenko, Sergey Karayev, Yangqing Jia, Alex Shyr, Allison<br />
Janoch, Jonathan Long, Mario Fritz*, Trevor Darrell<br />
EECS, UC Berkeley, USA, *Max-Planck-Institute for Informatics, Germany<br />
• Effective interaction requires both<br />
category (e.g. bottle) and instance<br />
(e.g. coca-cola) detection<br />
• Categories are often best described<br />
by overall shape, instances by<br />
distinct local patterns (e.g. logos)<br />
• We combine both cues in a detection<br />
system based on the Kinect sensor<br />
• We exploit depth cues to provide<br />
better segmentation and object size<br />
constraints<br />
• We propose several ways to improve<br />
the efficiency of the search<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–41–<br />
shape path<br />
size zee constraints c<br />
categ category: bottle ttle<br />
instance: coca-cola<br />
08:45–08:50 TuAT1.4<br />
Visual and Physical Segmentation<br />
of Novel Objects<br />
Amr Almaddah, Yasushi Mae, Kenichi Ohara<br />
Tomohito Takubo, Tatsuo Arai<br />
Department of System Innovation, Osaka University, Japan<br />
• Visual segmentation of novel objects<br />
from complicated scenes by deploying<br />
multiple lights and objects reflectivity.<br />
• Physical segmentation of novel objects<br />
by using static electricity charge sensing<br />
technique.<br />
• By using extracted items visual and<br />
physical aspects, an autonomous robot<br />
manipulator successfully performed a<br />
trash separation task.<br />
local texture path
Session TuAT1 Continental Parlor 1 Tuesday, September 27, <strong>2011</strong>, 08:00–09:30<br />
Object Recognition, Segmentation, and Detection<br />
Chair Trevor Darrell, UC Berkeley<br />
Co-Chair Ashley Desmond Tews, CSIRO<br />
08:50–08:55 TuAT1.5<br />
Knowing Your Limits - Self-Evaluation and<br />
Prediction in Object Recognition<br />
Michael Zillich, Johann Prankl, Thomas Mörwald, Markus Vincze<br />
Inst. of Automation and Control, Vienna Univ. of Technology, Austria<br />
• Incremental acquisition of 3D object<br />
models for open-ended learning scenarios<br />
• Probabilistic measures of observed<br />
detection success, predicted detection<br />
success, model completeness<br />
• Support reasoning when to extend the<br />
model, where to look next<br />
• Predict the probability of successful<br />
detection given the model learned so far<br />
Object, learned views and<br />
indication of model completeness<br />
09:00–09:15 TuAT1.7<br />
Generating Object Hypotheses in Natural<br />
Scenes through Human-Robot Interaction<br />
Niklas Bergström, Mårten Björkman and Danica Kragic<br />
CVAP/CSC, Royal Institute of Technology (KTH), Sweden<br />
• We present an interactive multi label,<br />
real time segmentation system for<br />
object modeling and tracking.<br />
• A human operator instructs the robot<br />
with simple commands such as ”add<br />
segment” or “split segment”.<br />
• By additionally giving relational<br />
information about objects, the<br />
segmentation can be further refined.<br />
• We showed through experiments<br />
how the different interactive elements<br />
helps to improve the segmentation.<br />
08:55–09:00 TuAT1.6<br />
Depth Kernel Descriptors for Object Recognition<br />
Liefeng Bo and Dieter Fox<br />
Department of Computer Science, University of Washington, USA<br />
Xiaofeng Ren<br />
Intel Labs Seattle, USA<br />
• Depth kernel descriptors is a general<br />
framework to generate rich 3D features<br />
• Five features over depth maps and 3D<br />
point clouds are proposed to capture size,<br />
3D shape, and edge cues for recognition<br />
• Evaluated on the large RGB-D Object<br />
dataset, consisting of 300 objects in 51<br />
categories for both instance and category<br />
object recognition<br />
• The proposed features are diverse yet<br />
complementary, as shown in the right Fig.<br />
• Improves over state-of-the-art features<br />
such as spin images.<br />
09:15–09:30 TuAT1.8<br />
3D Payload Detection From 2D Range Scans<br />
Ash Tews<br />
Autonomous Systems Laboratory, CSIRO, Australia<br />
• Major challenge is how to use 2D scan<br />
segments to classify a 3D payload<br />
• No prior knowledge of the payload –<br />
scans obtained during vehicle travel<br />
• System selects highly discriminating target<br />
scan segments for the segment reference<br />
library<br />
• New scans matched to the reference<br />
library and a temporal confidence value<br />
used for classification<br />
• Results demonstrated in classifying a<br />
smelter crucible<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–42–
Session TuAT2 Continental Parlor 2 Tuesday, September 27, <strong>2011</strong>, 08:00–09:30<br />
Simultaneous Localization and Mapping<br />
Chair Jaime Valls Miro, Univ. of Tech. Sydney<br />
Co-Chair Denis Fernando Wolf, Univ. of Sao Paulo<br />
08:00–08:15 TuAT2.1<br />
A Hierarchical RBPF SLAM for Mobile Robot<br />
Coverage in Indoor Environments<br />
Tae-kyeong Lee and Se-young Oh<br />
Department of Electrical Engineering, POSTECH, Korea<br />
Seongsoo Lee<br />
Future IT Convergence Laboratory, LG Electronics Corporation, Korea<br />
• This paper presents a SLAM framework<br />
that can be applied to floor cleaning robots<br />
equipped with sparse and short range<br />
sensors<br />
• A line feature map is estimated based on<br />
RBPF, hierarchically by dividing the entire<br />
region into local maps<br />
• We improve filter performance by using an<br />
assumption of orthogonal environment<br />
• The proposed SLAM framework is<br />
combined with a coverage path planning<br />
algorithm, enabling online simultaneous<br />
coverage and SLAM<br />
(a) Map and trajectory by odometry<br />
(b) Map and trajectory by SLAM<br />
Experimental results in a real<br />
home environment<br />
08:30–08:45 TuAT2.3<br />
Multiple Robot Simultaneous Localization and<br />
Mapping<br />
Sajad Saeedi � , Liam Paull � , Michael Trentini �� and Howard Li �<br />
� Electrical and Computer Engineering, University of New Brunswick, Canada<br />
�� Defence Research and Development Canada, Canada<br />
• In this research, a decentralized platform<br />
for simultaneous localization and mapping<br />
with multiple robots has been developed<br />
and verified.<br />
• Map fusion is achieved through a multistep<br />
process which is based on image<br />
processing techniques.<br />
• Maps are preprocessed by Canny edge<br />
detection, edge smoothing and image<br />
segmentation.<br />
• Then the relative transformation matrix of<br />
maps is extracted by cross correlation,<br />
Radon transform, similarity index and<br />
image entropy.<br />
The proposed algorithm<br />
for the map fusion<br />
08:15–08:30 TuAT2.2<br />
Mapping of Multi-Floor Buildings: A Barometric<br />
Approach<br />
Ali Ozkil 1 , Zhun Fan 2 , Jizhong Xiao 3 , Jens Kristensen 4<br />
Steen Dawids 1 , Kim Christensen 4<br />
1: Technical University of Denmark, 2: Tongji University,China 3:The City<br />
College of New York, USA 4: Force Technology, Denmark<br />
• This paper presents a new method for<br />
mapping multi-floor buildings.<br />
• The method combines laser range<br />
sensor for metric mapping and<br />
barometric pressure sensor for detecting<br />
floor transitions and map segmentation.<br />
• We exploit the fact that the barometric<br />
pressure is a function of the elevation,<br />
and it varies between different floors.<br />
• The method is tested with a real robot in<br />
a typical indoor environment, and<br />
physically consistent multi-floor<br />
representations are achieved.<br />
08:45–08:50 TuAT2.4<br />
Improving Occupancy Grid FastSLAM<br />
by Integrating Navigation Sensors<br />
Christopher Weyers<br />
Sensors Directorate, Air Force Research Laboratory, USA<br />
Gilbert Peterson<br />
Department of Electrical & Computer Eng, Air Force Institute of Tech, USA<br />
• Simultaneous Localization and Mapping<br />
(SLAM) problem of constructing local grid<br />
maps using only internal sensors<br />
• Multiple Integrated Navigation Sensors<br />
(MINS) system combines IMU, odometry,<br />
stereo vision creating one input path<br />
• Implementation cascades MINS Kalman<br />
filter into FastSLAM particle filter using<br />
LIDAR to build 2D maps<br />
• MINS FastSLAM implementation<br />
decreased error 92% from navigation path<br />
and 79% from odometry path<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–43–<br />
Maps from Odometry, MINS,<br />
Odometry SLAM, MINS SLAM
Session TuAT2 Continental Parlor 2 Tuesday, September 27, <strong>2011</strong>, 08:00–09:30<br />
Simultaneous Localization and Mapping<br />
Chair Jaime Valls Miro, Univ. of Tech. Sydney<br />
Co-Chair Denis Fernando Wolf, Univ. of Sao Paulo<br />
08:50–08:55 TuAT2.5<br />
Efficient Information-Theoretic Graph Pruning for<br />
Graph-Based SLAM with Laser Range Finders<br />
Henrik Kretzschmar, Cyrill Stachniss<br />
Dept. of Computer Science, University of Freiburg, Germany<br />
Giorgio Grisetti<br />
Dept. of Systems and Computer Science, Sapienza University of Rome, Italy<br />
• Prune the pose graph in graph-based SLAM<br />
• Maximize the mutual information of laser<br />
measurements and the resulting occupancy<br />
grid map<br />
• Discard the least informative laser scans and<br />
marginalize out the corresponding pose<br />
nodes<br />
• Approximate marginalization based on<br />
Chow-Liu trees to avoid a dense pose graph<br />
• Allow for long-term map learning and<br />
any-space SLAM<br />
09:00–09:15 TuAT2.7<br />
Neural Network-based Multiple Robot<br />
Simultaneous Localization and Mapping<br />
Sajad Saeedi � , Liam Paull � , Michael Trentini �� and Howard Li �<br />
� Electrical and Computer Engineering, University of New Brunswick, Canada<br />
�� Defence Research and Development Canada, Canada<br />
• In this research, a decentralized platform<br />
for simultaneous localization and mapping<br />
with multiple robots has been developed.<br />
• A novel occupancy grid map fusion<br />
algorithm is proposed. Map fusion is<br />
achieved through a multistep process.<br />
• The proposed method learns the maps<br />
based on the Neural Network - Self<br />
Organizing Map (SOM).<br />
• In the learning phase, the obstacles of the<br />
map are learned by SOM which makes<br />
further analyses of the map easier and<br />
faster.<br />
Two maps (top and middle) are<br />
fused by the proposed method<br />
(button)<br />
08:55–09:00 TuAT2.6<br />
An Incremental Scheme for Dictionary-based<br />
Compressive SLAM<br />
Tomomi Nagasaka and Kanji Tanaka<br />
Engineering, University of Fukui, Japan<br />
• Obtaining a compact representation of a large-size pointset map built<br />
by mapper robots is a critical issue for recent SLAM applications.<br />
• This ``map compression" problem is explored from a novel perspective<br />
of dictionary-based map compression in the paper.<br />
• The primary contribution of the<br />
paper is proposal of an incremental<br />
scheme for simultaneous mapping<br />
and map-compression applications.<br />
• An incremental map compressor<br />
is presented by employing a<br />
modified RANSAC map-matching<br />
scheme as well as the compact<br />
projection visual search.<br />
Incremental map-compression while<br />
simultaneously building the map<br />
09:15–09:30 TuAT2.8<br />
Conservative Sparsification for Efficient and Consistent<br />
Approximate Estimation<br />
John Vial and Tim Bailey<br />
Australian Centre for Field Robotics, University of Sydney, Australia<br />
Hugh Durrant-Whyte<br />
National ICT Australia<br />
• A new approach for sparse<br />
approximation.<br />
• Guaranteed to maintain<br />
consistency.<br />
• Maintains smaller Kullback-Leibler<br />
Divergence than existing<br />
consistent approaches.<br />
• Complexity is bounded by the size<br />
of the Markov blanket surrounding<br />
link removal.<br />
• No new nonzero links are added.<br />
• Elements outside Markov blanket<br />
area are unaffected.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–44–<br />
Graph before and after sparsification.<br />
Light coloured link removed, no new fill<br />
in. Complexity is bounded by boxed<br />
area (may approach constant time for<br />
large sparse matrices).
Session TuAT3 Continental Parlor 3 Tuesday, September 27, <strong>2011</strong>, 08:00–09:30<br />
Symposium: Microrobotics I<br />
Chair Sylvain Martel, Ec. Pol. de Montreal (EPM)<br />
Co-Chair David Cappelleri, Stevens Inst. of Tech.<br />
08:00–08:15 TuAT3.1*<br />
Semi-Plenary Invited Talk: History of Microrobotics<br />
and Vision for the Future: Microassembly and Beyond<br />
Toshio Fukuda, Nagoya University<br />
08:30–08:45 TuAT3.3<br />
Remote Microscale Teleoperation through<br />
Virtual Reality and Haptic Feedback<br />
Aude Bolopion1 , Christian Stolle2 , Robert Tunnell2 , Sinan Haliyo1 ,<br />
Stéphane Régnier1 and Sergej Fatikow2 1 Institut des Systèmes Intelligents et de Robotique, Université Pierre et Marie<br />
Curie CNRS UMR 7222, France.<br />
2 Division Microrobotics and Control Engineering, Oldenburg University,<br />
Germany.<br />
• Teleoperation of microspheres between France and Germany is<br />
performed<br />
• Haptic feedback and 3D reconstruction of the scene are provided to<br />
ensure intuitiveness<br />
• Vision algorithms are developed to compensate the lack of force<br />
measurement in SEM manipulation<br />
• Modular software architecture and efficient transmission of the data<br />
ensures a wide range of applications<br />
08:15–08:30 TuAT3.2<br />
Semi-Plenary Invited Talk: History of Microrobotics<br />
and Vision for the Future: Microassembly and Beyond<br />
Toshio Fukuda, Nagoya University<br />
08:45–08:50 TuAT3.4<br />
Miniature Ferromagnetic Robot Fish Actuated by<br />
a Clinical Magnetic Resonance Scanner<br />
F. Gosselin, D. Zhou, V. Lalande,<br />
M. Vonthron and S. Martel<br />
NanoRobotics Laboratory, Ecole Polytechnique Montreal, Canada<br />
• A new actuation principle for a swimming<br />
robot is presented<br />
• This actuation method uses the magnetic<br />
field produced by a clinical MRI scanner<br />
• The performance of the robot fish is<br />
studied by varying different parameters<br />
• Preliminary results suggest that the<br />
technique could potentially be scaled<br />
Photograph of the actual<br />
down to be used for microrobots<br />
swimming robot compared<br />
with the new generation MRI<br />
swimmer<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–45–
Session TuAT3 Continental Parlor 3 Tuesday, September 27, <strong>2011</strong>, 08:00–09:30<br />
Symposium: Microrobotics I<br />
Chair Sylvain Martel, Ec. Pol. de Montreal (EPM)<br />
Co-Chair David Cappelleri, Stevens Inst. of Tech.<br />
08:50–08:55 TuAT3.5<br />
Hybrid Microassembly of Chips on Low<br />
Precision Patterns Assisted by Capillary Selfalignment<br />
Bo Chang, Mirva Jääskeläinen and Quan Zhou<br />
Department of Automation and Systems Technology, Aalto University, Finland<br />
• The segmented patterns having jagged<br />
edges are fabricated to mimic some realworld<br />
RFID antennas<br />
• Self-alignment occurs reliably despite the<br />
variation in size of the pattern and edge<br />
jaggedness<br />
• The alignment accuracy is likely to be<br />
affected when the total size of the pattern<br />
is different than the chip<br />
• The edge jaggedness seems not making<br />
the accuracy worse, which is a very<br />
interesting phenomenon<br />
09:00–09:15 TuAT3.7<br />
The Cellular Force Microscope (CFM):<br />
A microrobotic system for quantitating the growth<br />
mechanics of living, growing plant cells in situ<br />
Dimitrios Felekis, Simon Muntwyler,<br />
Felix Beyeler and Bradley J. Nelson<br />
Mechanical and Process Engineering, ETH Zurich, Switzerland<br />
• CFM is a system capable of performing<br />
automated mechanical characterization of<br />
living, growing cells in situ<br />
• CFM can be applied on organisms with<br />
changing and diverse morphology, from<br />
the sub-cellular to the whole organ level<br />
• SI-traceable calibration and<br />
characterization with uncertainty analysis<br />
is performed for the MEMS sensor<br />
• The results verify relevant works and<br />
quantify the stiffness difference between<br />
the tip and the shank of the cell<br />
CFM used to characterize single<br />
cell organisms<br />
08:55–09:00 TuAT3.6<br />
A Resonant Surface Sensitive to Out-of-plane Forces<br />
for the Indentation and Injection of Living Cells<br />
Denis Desmaële a,b and Mehdi Boukallel a<br />
a CEA, LIST, LISA, France<br />
Stéphane Régnier b<br />
b ISIR, Université Pierre et Marie Curie, France<br />
• This paper presents a monolithic structure<br />
where biological samples can be placed for<br />
manipulation.<br />
• Forces applied on samples can be<br />
estimated via frequency shifts of two<br />
resonators.<br />
• Static and dynamic behaviors of the force<br />
sensor are theoretically investigated.<br />
• Performances of a first prototype are<br />
reported. Tests on lobster eggs are also<br />
conducted.<br />
09:15–09:30 TuAT3.8<br />
Caging Grasps for Micromanipulation &<br />
Microassembly<br />
David J. Cappelleri, Michael Fatovic, and Zhenbo Fu<br />
Department of Mechanical Engineering, Stevens Institute of Technology, USA<br />
• Systematic method for<br />
defining caging grasps for<br />
micro-scale planar<br />
• Feature-Defined Cages: use<br />
of convex and nonconvex<br />
corner geometries<br />
• Form caging polygon to<br />
apply opposing force<br />
equivalents with probes<br />
• Theoretical error bounded<br />
analysis<br />
• Experimental validation of FD-<br />
Cages and error bounds<br />
• Combine with other primitives<br />
to execute sample assembly<br />
task<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–46–<br />
Micromanipulator<br />
Probes<br />
Fc F<br />
Fc c<br />
Fc Caging Polygon<br />
225 um<br />
[Start]<br />
N 4<br />
N 1<br />
225 um<br />
[End]<br />
N 2<br />
N 3
Session TuAT4 Continental Ballroom 4 Tuesday, September 27, <strong>2011</strong>, 08:00–09:30<br />
Industrial Forum: Robots: The New Commercial Platforms<br />
Chair Rüdiger Dillmann, KIT Karlsruher Inst. für Tech.<br />
Co-Chair<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–47–
Session TuAT5 Continental Ballroom 5 Tuesday, September 27, <strong>2011</strong>, 08:00–09:30<br />
Symposium: Medical Robotics I<br />
Chair Jaydev P. Desai, Univ. of Maryland<br />
Co-Chair Paolo Fiorini, Univ. of Verona<br />
08:00–08:15 TuAT5.1*<br />
Semi-Plenary Invited Talk: Future of Surgical Robotics:<br />
Confluence of Surgeon demands and Technology<br />
Paolo Fiorini, University of Verona<br />
08:30–08:45 TuAT5.3<br />
Design of a User Interface for<br />
Intuitive Colonoscope Control: the Grip<br />
Nicole Kuperij*, Rob Reilink*, Matthijs P. Schwartz + , Stefano<br />
Stramigioli*, Sarthak Misra* and Ivo A.M.J. Broeders* +<br />
University of Twente* and Meander Medical Center + , The Netherlands<br />
• Colonoscopy is a medical procedure<br />
(screening for polyps to prevent colon<br />
cancer)<br />
• Colonoscope control is<br />
difficult, the control<br />
handle is large and not<br />
ergonomic<br />
• Procedure efficiency can<br />
be improved by using “the<br />
Grip” that actuates a<br />
motorized control handle<br />
“The Grip” controls the colonoscope tip by<br />
simply aiming it in the desired direction<br />
• “the Grip”: an innovative user interface to provide<br />
• Single handed control<br />
• Intuitive control � a 3D orientation sensor is used to map the orientation<br />
of the colonoscope shaft to the orientation of the tip<br />
• This study: colonoscope DOF and use, Grip prototype, testing on<br />
colonoscopy simulator (human subject experiment), promising results<br />
08:15–08:30 TuAT5.2<br />
A Prototype of Pneumatically-Driven Forceps<br />
Manipulator with Force Sensing Capability<br />
Using a Simple Flexible Joint<br />
Daisuke Haraguchi, Kotaro Tadano and Kenji Kawashima<br />
Precision and Intelligence Lab., Tokyo Institute of Technology, Japan<br />
• Highly simplified, 2-DOF bending distal<br />
joint using a high performance spring<br />
component is developed<br />
• Wire-tendon drive system is implemented<br />
with 4 pneumatic cylinders<br />
• External forces can be estimated using a<br />
disturbance observer (no sensors at the<br />
tip part)<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–48–<br />
Design of the forceps manipulator<br />
08:45–08:50 TuAT5.4<br />
Robot for Ultrasound-Guided<br />
Prostate Imaging and Intervention<br />
Chunwoo Kim, Felix Schäfer, Doyoung Chang,<br />
Doru Petrisor, Misop Han, Dan Stoianovici<br />
Urology Robotics Laboratory, Johns Hopkins University, U.S.A.<br />
• 4 degree of freedom positions and orients<br />
the transrectal ultrasound probe for image<br />
scanning and needle targeting<br />
• Designed to accommodate the constraints<br />
of the clinical prostate intervention.<br />
• 2D image slices are tracked and mapped<br />
3D volume<br />
• In-vitro studies on prostate mockups verify<br />
3D imaging capabilities.<br />
• Clinically used in robot-assisted<br />
laparoscopic radical prostatectomy for<br />
providing intraoperative ultrasound-based<br />
navigation for the surgeon.<br />
CAD model of the robot(top)<br />
Post operative 3D reconstruction<br />
of prostate from intraoperative<br />
US images (bottom)
Session TuAT5 Continental Ballroom 5 Tuesday, September 27, <strong>2011</strong>, 08:00–09:30<br />
Symposium: Medical Robotics I<br />
Chair Jaydev P. Desai, Univ. of Maryland<br />
Co-Chair Paolo Fiorini, Univ. of Verona<br />
08:50–08:55 TuAT5.5<br />
A Modular, Mechatronic Joint Design for a<br />
Flexible Access Platform for MIS<br />
David Noonan, Valentina Vitiello, Jianzhong Shang<br />
Christopher Payne, Guang-Zhong Yang<br />
Hamlyn Centre for Robotic Surgery, Imperial College London, UK<br />
• Mechatronic joint module with 1-DoF or 2-<br />
DoF featuring embedded actuation (micro<br />
motor and pre-tensioned tendon)<br />
• Analysis of tendon path length variation,<br />
position repeatability and force<br />
transmission characteristics are presented<br />
• 7-DoF articulated robot constructed from<br />
two 2-DoF modules and three 1-DoF<br />
modules<br />
• Demonstrated diagnostic peritoneoscopy<br />
using probe based confocal laser<br />
endomicroscopy during in-vivo porcine<br />
trials<br />
7-DoF robot with three internal<br />
channels<br />
09:00–09:15 TuAT5.7<br />
Design of an Endoscopic Stitching Device for<br />
Surgical Obesity Treatment Using a N.O.T.E.S<br />
Approach<br />
Kai Xu � , Jiangran Zhao � and Minhua Zheng �<br />
UM-SJTU Joint Institute � and Department of Surgery � ,<br />
Shanghai Jiao Tong University, China<br />
James Geiger � and Albert J. Shih �<br />
Department of Surgery � and Department of Mechanical Engineering � ,<br />
University of Michigan, USA<br />
• This paper proposed a design of an endoscopic stitching device for<br />
gastroplasty using a N.O.T.E.S<br />
(Natural Orifice Translumenal<br />
Endoscopic Surgery) approach, which<br />
performs stitching and resizing of a<br />
stomach from inside the stomach.<br />
• Major contribution of this paper is the<br />
proposal of fabricating suture using<br />
pre-curved super-elastic NiTi alloy to<br />
simplify suturing motion.<br />
• Minor contribution of this paper is the<br />
presentation of one possible design to<br />
realize this proposed concept.<br />
Design of an endoscopic stitching device using<br />
needles made from pre-curved super-elastic<br />
NiTi alloy: (a) the folded configuration and (b) the unfolded working configuration.<br />
08:55–09:00 TuAT5.6<br />
Development of a “Steerable Drill” for ACL<br />
Reconstruction to Create the Arbitrary<br />
Trajectory of a Bone Tunnel<br />
Hiroki Watanabe, Kazuki Kanou<br />
Graduate school of Science and Engineering, Waseda University, Japan<br />
Yo Kobayashi, Masakatsu G. Fujie<br />
Faculty of Science and Engineering, Waseda University, Japan<br />
• Objective: Design of a steerable drill for ACL<br />
(anterior cruciate ligament) reconstruction to<br />
avoid LCL (lateral collateral ligament) injury.<br />
• Requirement:<br />
�Sufficient flexibility to bent to avoid LCL<br />
while cutting the bone.<br />
�Rigidity to avoid the buckle at the base<br />
of the drill while cutting the bone.<br />
• Solution: Adoption of a spring sheath with<br />
stiffness that varied between top and base of<br />
the drill to satisfy the two requirements.<br />
• Result: The steerable drill achieved 3 times<br />
the bending deformation than the drill with<br />
uniform stiffness as preventing the buckling.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–49–<br />
Steerable drill with the nonuniform<br />
stiffness.<br />
09:15–09:30 TuAT5.8<br />
Active Bending Endoscope Robot System for<br />
Navigation through Sinus Area<br />
Hyun-Soo Yoon, Se Min Oh, Jin Hyeok Jeong, Seung Hwan Lee,<br />
Kyung Tae, and Byung-Ju Yi<br />
Hanyang University, Korea<br />
Kyoung-Chul Koh<br />
Sun Moon University, Korea<br />
• A new endoscope robot system for<br />
general sinus surgery is proposed.<br />
• The endoscope mechanism can be bent<br />
by 180 degrees with a small radius of<br />
curvature (1cm).<br />
• The proposed system was successfully<br />
implemented to inspection of a simulated<br />
maxillary sinus area through the nostril of<br />
the human silicon model without any<br />
collision.<br />
Endoscopic view<br />
Active Bending Endoscope Robot<br />
System
Session TuAT6 Continental Ballroom 6 Tuesday, September 27, <strong>2011</strong>, 08:00–09:30<br />
Symposium: Grasping and Manipulation: Control and Learning<br />
Chair Matei Ciocarlie, Willow Garage<br />
Co-Chair Peter Allen, Columbia Univ.<br />
08:00–08:15 TuAT6.1*<br />
Semi-Plenary Invited Talk: Get a Grip -- Robotic<br />
Dexterous Manipulation from Finger Choreography to<br />
The Power Pinch<br />
Mark Cutkosky, Stanford University<br />
08:30–08:45 TuAT6.3<br />
Synergy Level Impedance Control for<br />
Multifingered Hands<br />
Thomas Wimböck 1 , Benjamin Jahn 1,2 , and Gerd Hirzinger 1<br />
1 Institute of Robotics and Mechatronics, DLR, Germany<br />
2 Control Engineering Group, Computer Science and Automation Faculty,<br />
Ilmenau University of Technology, Germany<br />
• Controller to imitate the behaviour of a<br />
synergistic, respectively underactuated,<br />
hand.<br />
• Extension of the work on passivity based<br />
hand control at DLR.<br />
• Based on the analysis of the grasp<br />
database of the DLR Hand II.<br />
• Desired equilibrium in synergy coordinates<br />
and the corresponding synergy stiffness<br />
are setpoints/parameters.<br />
• The nullspace impedance allows to adjust<br />
the mechanical coupling.<br />
• Implementation and verification on the DLR<br />
Hand II.<br />
DLR Hand II controlled by a<br />
synergy impedance controller<br />
08:15–08:30 TuAT6.2<br />
Semi-Plenary Invited Talk: Get a Grip -- Robotic<br />
Dexterous Manipulation from Finger Choreography to<br />
The Power Pinch<br />
Mark Cutkosky, Stanford University<br />
08:45–08:50 TuAT6.4<br />
Embodiment-Specific Representation of Robot<br />
Grasping using Graphical Models and Latent-Space<br />
Discretization<br />
Dan Song, Carl Henrik Ek, Kai Huebner and Danica Kragic<br />
School of Computer Science and Communications,<br />
Royal Institute of Technology, Sweden<br />
• We propose an embodimentspecific<br />
representation<br />
framework for robot grasping.<br />
• To evaluate the framework, we<br />
train two task models, one for<br />
the human hand and one for<br />
the Armar robot hand.<br />
• Both models successfully<br />
encode the semantic task<br />
requirements in the continuous<br />
observation spaces<br />
• And different hand kinematics<br />
affect both the Bayesian<br />
network structure and the<br />
conditional distributions over<br />
the modeled variables<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–50–
Session TuAT6 Continental Ballroom 6 Tuesday, September 27, <strong>2011</strong>, 08:00–09:30<br />
Symposium: Grasping and Manipulation: Control and Learning<br />
Chair Matei Ciocarlie, Willow Garage<br />
Co-Chair Peter Allen, Columbia Univ.<br />
08:50–08:55 TuAT6.5<br />
Grasping Unknown Objects using an Early Cognitive<br />
Vision System for General Scene Understanding<br />
Mila Popović and Jimmy Alison Jørgensen and Norbert Krüger<br />
The Mærsk Institute, University of Southern Denmark, Denmark<br />
Gert Kootstra and Danica Kragic<br />
Computer Vision and Active Perception Lab, KTH, Sweden<br />
• Early Cognitive Vision system builds a<br />
hierarchical representation based on edge<br />
and texture information from real stereo<br />
images.<br />
• Edge-based and surface-based grasps<br />
are generated and tested in the simulation<br />
environment.<br />
• Method generates successful grasps, also<br />
for complex scenes. Edge and surface<br />
information are complementary.<br />
� The system can be used as benchmark<br />
for visual-based grasping.<br />
09:00–09:15 TuAT6.7<br />
Imitation Learning of Human Grasping Skills<br />
from Motion and Force Data<br />
Alexander M. Schmidts, Dongheui Lee, Angelika Peer<br />
Institute of Automatic Control Engineering<br />
Technische Universität München<br />
Germany<br />
• Imitation learning of precision grasps with multiple interaction points from<br />
motion and force data<br />
• Physical consistency between demonstrations and reproduction<br />
guaranteed by learning tensions compared to finger interaction forces<br />
• Enhanced generalisability by including force information in the learning<br />
process<br />
Teleoperation system used for demonstration. Using the data<br />
glove and motion tracker attached to the users hand (right) the<br />
simulated Elumotion Hand is controlled (left). Haptic force<br />
feedback is given to the user by means of a Cybergrasp.<br />
08:55–09:00 TuAT6.6<br />
Grasping of Unknown Objects via Curvature<br />
Maximization using Active Vision<br />
Berk Calli, Martijn Wisse and Pieter Jonker<br />
Biomechanical Engineering Department, Delft University of Technology,<br />
The Netherlands<br />
• In this paper, we propose a novel grasping<br />
algorithm that uses active vision as basis.<br />
• The algorithm does not require a 3D<br />
model or any offline data.<br />
• The algorithm uses the curvature<br />
information obtained from the silhouette of<br />
the object.<br />
• The silhouette is modeled using EFDs,<br />
and grasping points and approach vector<br />
have been obtained.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–51–<br />
The motion that robot<br />
goes through for grasping.<br />
09:15–09:30 TuAT6.8<br />
Internal force control with no object motion in<br />
compliant robotic grasps<br />
Monica Malvezzi and Domenico Prattichizzo<br />
Department of Information Engineering, University of Siena, Italy<br />
Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genova, Italy<br />
• The control of internal forces is one of the<br />
key issues in grasping.<br />
• When the robotic hand is compliant, and<br />
the number of controlled variables is low, it<br />
is possible that the control of internal<br />
forces implies the motion of the<br />
manipulated object.<br />
• The paper studies the structural conditions<br />
for the control of internal forces which do<br />
not involve any motion of the grasped<br />
object.<br />
• The analysis is constructive and a<br />
controller of internal forces is proposed.
Session TuAT7 Continental Parlor 7 Tuesday, September 27, <strong>2011</strong>, 08:00–09:30<br />
Software Architectures & Frameworks<br />
Chair Geoffrey Biggs, National Inst. of AIST<br />
Co-Chair Roland Philippsen, Stanford Univ.<br />
08:00–08:15 TuAT7.1<br />
Intelligent System Architectures –<br />
Comparison by Translation<br />
Benjamin Dittes and Christian Goerick<br />
Honda Research Institute Europe GmbH, Germany<br />
• System architectures are important for<br />
constructing large software systems,<br />
scientific discourse requires comparability<br />
• We present translation to a common<br />
language, which we call `Systematica 2D’,<br />
as a way to allow comparison<br />
• The approach is verified on three recent,<br />
popular system architectures: CogX<br />
`George’, `ALIS3’ and the iCub Cognitive<br />
Architecture<br />
• Resulting Systematica 2D diagrams allow<br />
analysis of common patterns, constraints<br />
and design decisions<br />
Main information flows in ‘George’,<br />
‘ALIS3’ and ‘iCub’ architectures,<br />
derived from Systematica 2D notations<br />
08:30–08:45 TuAT7.3<br />
Analysis of Software Connectors in Robotics<br />
Azamat Shakhimardanov, Nico Hochgeschwender,<br />
Michael Reckhaus, Gerhard K. Kraetzschmar<br />
Bonn-Rhine-Sieg University of Applied Sciences, Germany<br />
• Method to analyse quality attributes in component-oriented robotic<br />
software<br />
• Protocol stack view used for the analysis of distributed robotic<br />
software<br />
• Scalability experiments with ROS and ZeroMQ<br />
• Lessons learned in system design for large-scale robotic applications<br />
08:15–08:30 TuAT7.2<br />
Conductor: A Controller Development<br />
Framework for High Degree of Freedom Systems<br />
Robert Sherbert and Paul Oh<br />
Mechanical Engineering, Drexel University, USA<br />
• Conductor is a software development<br />
framework for building controllers for high<br />
degree of freedom systems.<br />
• Conductor simplifies porting controllers<br />
between robots by representing hardware<br />
in terms of state variables.<br />
• Conductor can achieve significant<br />
increases in bandwidth usage efficiency<br />
within complex systems.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–52–<br />
A time progression of Conductor<br />
running a walking trajectory on a<br />
high degree of freedom robot.<br />
08:45–08:50 TuAT7.4<br />
An Open Source Extensible Software Package to Create<br />
Whole-Body Compliant Skills in Personal Mobile Manipulators<br />
Roland Philippsen, Luis Sentis, Oussama Khatib<br />
Stanford Robotics and AI Lab, USA<br />
University of Texas at Austin, USA<br />
• Whole-body operational space<br />
control is a powerful compliant<br />
control approach for robots that<br />
physically interact with their<br />
environment<br />
• Open-source implementation<br />
with runtime configurability,<br />
ease of reuse and extension,<br />
and independence from specific<br />
middlewares or operating<br />
systems<br />
• Experiments on Dreamer and<br />
PR2 show the technical<br />
performance and portability.<br />
Visit http://stanford-wbc.sourceforge.net
Session TuAT7 Continental Parlor 7 Tuesday, September 27, <strong>2011</strong>, 08:00–09:30<br />
Software Architectures & Frameworks<br />
Chair Geoffrey Biggs, National Inst. of AIST<br />
Co-Chair Roland Philippsen, Stanford Univ.<br />
08:50–08:55 TuAT7.5<br />
A component supervisor for RT-Middleware<br />
using supervision trees<br />
Geoffrey Biggs and Noriaki Ando and Tetsuo Kotoku<br />
Intelligent Systems Research Institute<br />
National Institute of Advanced Industrial Science and Technology<br />
Japan<br />
• Deployment and run-time management is<br />
an important part of a component-based<br />
software system.<br />
• We have developed a deployment and<br />
management tool for RT-Middleware.<br />
• The tool is based on the Supervision Tree e<br />
concept from the Erlang programming<br />
language.<br />
• The tool shows the validity of the<br />
Supervision Tree approach, although<br />
further research is needed to adapt it to<br />
robotics.<br />
09:00–09:15 TuAT7.7<br />
The Computing and Communication<br />
Architecture of the DLR Hand Arm System<br />
Stefan Jörg, Mathias Nickl, Alexander Nothhelfer,<br />
Thomas Bahls, and Gerd Hirzinger<br />
Inst. of Robotics and Mechatronics, German Aerospace Center, Germany<br />
• How to handle 52 motors and 430<br />
sensors?<br />
• How to achieve a convenient but highperformance<br />
interface to control the<br />
robot?<br />
• The presented solution uses a hierarchical<br />
net of computing nodes and combines the<br />
concepts of middleware and device<br />
drivers<br />
• First experiments with control applications<br />
(100 kHz/3KHz cycles) demonstrate the<br />
performance of the architecture<br />
08:55–09:00 TuAT7.6<br />
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09:15–09:30 TuAT7.8<br />
Caliper: A Universal Robot Simulation<br />
Framework for Tendon-Driven Robots<br />
S. Wittmeier, M. Jäntsch, K. Dalamagkidis, M. Rickert and A. Knoll<br />
Faculty of Informatics, TU München, Germany<br />
H.G. Marques<br />
Department of Informatics, University of Zurich, Switzerland<br />
• Caliper is a newly developed simulation<br />
framework targeted to the emerging class<br />
of tendon-driven robots.<br />
• The framework uses a modular, CORBA-<br />
and component-based approach. This<br />
provides easy extensibility and tailoring to<br />
meet user requirements.<br />
• The simulation models are defined in the<br />
standardized XML-based COLLADA<br />
format.<br />
• Physics-engine extensions are included to<br />
simulate tendon-driven actuators.<br />
• Caliper also provides tools for real-time<br />
data acquisition and visualization.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–53–<br />
Caliper screenshot (top) and<br />
prototype of the anthropomimetic,<br />
tendon-driven robot ECCE-I<br />
(bottom)
Session TuAT8 Continental Parlor 8 Tuesday, September 27, <strong>2011</strong>, 08:00–09:30<br />
Bio-inspired & Biomimetic Robots<br />
Chair Robert Wood, Harvard Univ.<br />
Co-Chair Howie Choset, Carnegie Mellon Univ.<br />
08:00–08:15 TuAT8.1<br />
Using Response Surfaces and Expected<br />
Improvement to Optimize Snake Robot Locomotion<br />
Matthew Tesch, Jeff Schneider and Howie Choset<br />
Robotics Institute, Carnegie Mellon University, United States of America<br />
• Goal: Improve the speed and capabilities<br />
of various snake robot gaits<br />
• Problem: Calculating the performance of a<br />
gait is expensive, as accurate models are<br />
unavailable<br />
• Gait optimization requires evaluations on<br />
an actual robot, which are expensive in<br />
time and resources<br />
• Solution: Fit a response surface as a<br />
surrogate objective function to assist with<br />
experiment selection<br />
08:30–08:45 TuAT8.3<br />
Snake-like Active Wheel Robot ACM-R4.1<br />
with Joint Torque Sensor and Limiter<br />
Shunichi Takaoka and Hiroya Yamada<br />
Mechanical and Aerospace Engineering, Tokyo Institute of Technology, Japan<br />
Shigeo Hirose<br />
Mechanical and Aerospace Engineering, Tokyo Institute of Technology, Japan<br />
• This paper proposes a snake-like active<br />
wheel robot named “ACM-R4.1”<br />
• ACM-R4.1 has a new mechanism which<br />
works as both a torque sensor and a<br />
torque limiter<br />
• We made theoretical consideration of<br />
design of the torque sensor and did<br />
torque measurement experiments<br />
• We also demonstrated the terrain<br />
adaptive motion by using torque sensors<br />
ACM-R4.1<br />
08:15–08:30 TuAT8.2<br />
State Estimation for Snake Robots<br />
David Rollinson, Austin Buchan and Howie Choset<br />
The Robotics Institute, Carnegie Mellon University, USA<br />
• We use an EKF to fuse<br />
proprioceptive data from the<br />
individual modules of a snake<br />
robot.<br />
• Using the Virtual Chassis<br />
instead of a fixed body frame<br />
allows more accurate estimates<br />
of pitch and roll.<br />
• State size is reduced by using<br />
gait parameters to approximate<br />
the shape of the robot during<br />
estimation.<br />
08:45–08:50 TuAT8.4<br />
Task-Space Control of Extensible Continuum<br />
Manipulators<br />
Apoorva Kapadia and Ian Walker<br />
Department of Electrical & Computer Engineering, Clemson University, USA<br />
• A new approach towards control of<br />
extensible continuum robots is presented.<br />
• Task-space control considered for<br />
continuum manipulators for first time.<br />
• Model-based control developed extending<br />
controllers for rigid-link robots.<br />
• Regulation of any location along the<br />
backbone of robot as well as tip.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–54–<br />
The Octarm Grasping a<br />
Cylindrical Object
Session TuAT8 Continental Parlor 8 Tuesday, September 27, <strong>2011</strong>, 08:00–09:30<br />
Bio-inspired & Biomimetic Robots<br />
Chair Robert Wood, Harvard Univ.<br />
Co-Chair Howie Choset, Carnegie Mellon Univ.<br />
08:50–08:55 TuAT8.5<br />
Novel Modal Approach for Kinematics of<br />
Multisection Continuum Arms<br />
Isuru S. Godage, Emanuele Guglielmino, David T. Branson,<br />
Gustavo A. Medrano-Cerda, and Darwin G. Caldwell<br />
Advanced Robotics Department,<br />
Istituto Italiano di Tecnologia, Italy.<br />
• Uses mode shape function (MSF) based<br />
approach to simplify and solve<br />
singularities<br />
• Introduces an intuitive way of deriving<br />
correct MSFs eliminating mode switching<br />
• Simulates forward kinematic spatial<br />
bending and pure elongation/contraction<br />
• Introduces inverse orientation kinematics<br />
for multisection continuum arms<br />
• Computes inverse position and/with<br />
orientation kinematics efficiently<br />
• Proposed method is applicable to a broad<br />
spectrum of continuum robotic arm<br />
designs<br />
Arm demonstrating inverse<br />
position with orientation<br />
kinematics in a simulated object<br />
inspection task<br />
09:00–09:15 TuAT8.7<br />
System identification and linear time-invariant modeling<br />
of an insect-sized flapping-wing micro air vehicle<br />
Ben Finio, Néstor Pérez-Arancibia and Robert Wood<br />
School of Engineering and Applied Sciences, Harvard University, USA<br />
• Dynamic modeling of flapping-wing MAVs<br />
is typically highly nonlinear<br />
• We present a linear time-invariant (LTI)<br />
model and compare it to an identified<br />
model derived using system identification<br />
• We show that the linear model can<br />
provide an accurate estimate of system<br />
behavior despite inherent nonlinearities<br />
• Implications for vehicle design and<br />
optimization are discussed<br />
New prototype of the Harvard<br />
Microrobotic Fly<br />
08:55–09:00 TuAT8.6<br />
Hardware in the Loop for<br />
Optical Flow Sensing in a Robotic Bee<br />
Pierre-Emile Duhamel, Judson Porter, Ben Finio,<br />
David Brooks, Gu-Yeon Wei, and Robert Wood<br />
School of Engineering and Applied Science, Harvard University, USA<br />
Geoffrey Barrows<br />
Centeye, Inc., USA<br />
• Successful autonomous robotic bees<br />
require optimized weight and power<br />
consumption to increase flight time.<br />
• Optimizing components is challenging<br />
due to interconnectedness and varying<br />
interdisciplinary component<br />
development timelines.<br />
• Hardware in the loop (HWIL) provides<br />
a modular combination of robotic bee<br />
hardware and software components.<br />
• We demonstrate the value of HWIL for<br />
desing of an optical flow sensor.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–55–<br />
Robotic Bee Hardware in the Loop<br />
09:15–09:30 TuAT8.8<br />
The acquisition of intentionally indexed and<br />
object centered affordance gradients<br />
Martí Sánchez-Fibla, Armin Duff and Paul Verschure<br />
SPECS, Technology Department, Universitat Pompeu Fabra (UPF), Spain<br />
• We introduce Affordance Gradients<br />
(AGs) : sensorimotor structures that<br />
allow to predict the consequences of<br />
“pushing” actions.<br />
• We show results in two proposed<br />
benchmarks:<br />
• (I) Pushing an object along a<br />
predefined trajectory<br />
• (II) Placing an object at a target<br />
position and orientation<br />
• Figure (a) shows a learned AG of a<br />
square object in Webots simulation<br />
environment.<br />
• Figure (b) shows the trajectory<br />
displayed by triangular and l-shaped<br />
pieces of benchmark (II).<br />
(a)<br />
(b)
Session TuAT9 Continental Parlor 9 Tuesday, September 27, <strong>2011</strong>, 08:00–09:30<br />
Probabilistic Exploration and Coverage<br />
Chair S.K. Gupta, Univ. of Maryland<br />
Co-Chair Timothy Bretl, Univ. of Illinois at Urbana-Champaign<br />
08:00–08:15 TuAT9.1<br />
Exploration driven by Local Potential Distortions<br />
Edson Prestes and Paulo Engel<br />
Instituto de Informática<br />
Universidade Federal do Rio Grande do Sul (UFRGS)<br />
Brazil<br />
• We extend our exploration method based on BVP to allow the robot to<br />
explore an unknown environment dynamically while considering<br />
environment preferences<br />
• These preferences generate distortions<br />
in the potential field that change locally<br />
the concavity/convexity of the potential<br />
• They can be thought of as an utility<br />
measure that provides an estimate of the<br />
environment information gain and/or the<br />
localization accuracy<br />
Exploration of an unknown environment<br />
with high (dark gray) and low (light gray)<br />
preferences regions<br />
08:30–08:45 TuAT9.3<br />
Adaptive Look-Ahead for Robotic Navigation in<br />
Unknown Environments<br />
Greg Droge and Magnus Egerstedt<br />
Electrical and Computer Engineering<br />
Georgia Institute of Technology, USA<br />
• Schema-based behaviors allow for<br />
navigation based on sensor<br />
measurements alone.<br />
• We introduce a receding horizon approach<br />
for adaptively coordinating these<br />
behaviors.<br />
• We are operating in an unknown<br />
environment: not clear what the time<br />
horizon should be for the receding horizon<br />
problem.<br />
• We introduce a method for online<br />
adaptation of the time horizon based on<br />
how well we have predicted the state<br />
trajectory.<br />
08:15–08:30 TuAT9.2<br />
Histogram Based Frontier Exploration<br />
Amir Mobarhani, Shaghayegh Nazari, Amir H. Tamjidi, Hamid D.<br />
Taghirad<br />
Advanced Robotics and Automated Systems Lab (ARAS), Electrical and<br />
Computer Engineering Department, K. N. Toosi University of Technology, Iran<br />
• Histogram based exploration is less<br />
dependent on thresholds.<br />
• It is a weighted combination of local<br />
search (for closest frontiers) and global<br />
search (for biggest frontiers)<br />
• The criteria for choosing the exploration<br />
sub-goal is simple to implement and<br />
flexible.<br />
• Experimental results show that with low<br />
computational cost we can achieve better<br />
performance compared to the original<br />
paradigm of frontier exploration.<br />
08:45–08:50 TuAT9.4<br />
A receding horizon approach to generating<br />
dynamically feasible plans<br />
for vehicles that operate over large areas<br />
Daniel J. Stilwell and Aditya S. Gadre<br />
ECE Department, Virginia Tech, USA<br />
Andrew J. Kurdila<br />
ME Department, Virginia Tech, USA<br />
• Real-time planning of dynamically<br />
feasible trajectories for large operating<br />
environments.<br />
• Short-horizon trajectories are computed<br />
continuously. Global path is computed<br />
only when needed.<br />
• Formal guarantees that sequence of<br />
trajectories will converge to desired<br />
location<br />
• Suitable for vehicle systems where<br />
kinematic model is poor approximation of<br />
vehicle dynamics.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–56–
Session TuAT9 Continental Parlor 9 Tuesday, September 27, <strong>2011</strong>, 08:00–09:30<br />
Probabilistic Exploration and Coverage<br />
Chair S.K. Gupta, Univ. of Maryland<br />
Co-Chair Timothy Bretl, Univ. of Illinois at Urbana-Champaign<br />
08:50–08:55 TuAT9.5<br />
Planning for Landing Site Selection in the Aerial<br />
Supply Delivery<br />
Aleksandr Kushleyev and Brian MacAllister<br />
University of Pennsylvania, USA<br />
Maxim Likhachev<br />
Carnegie Mellon University, USA<br />
• Our goal is to generate an optimal policy<br />
for a UAV that minimizes the expected cost<br />
of flying, sensing and landing<br />
• Availability of landing sites is unknown<br />
• Modeling uncertainty in the problem<br />
representation allows the planner to reason<br />
about missing information<br />
• Resulting policies are more realistic, but<br />
complexity of the problem increases<br />
exponentially with number of unknowns<br />
• Existing PPCP framework efficiently and<br />
optimally solves such problems if certain<br />
conditions hold (Clear Preferences)<br />
• Our implementation provides rigorous<br />
theoretical guarantees and runs on a<br />
custom quad-rotor helicopter<br />
Prior terrain data<br />
Landing sites and sample paths<br />
09:00–09:15 TuAT9.7<br />
Probably Approximately Correct Coverage<br />
for Robots with Uncertainty<br />
Colin Das 1 , Aaron Becker 2 , and Timothy Bretl 1<br />
1 Aerospace Eng., University of Illinois at Urbana-Champaign, USA<br />
2 Electrical and Computer Eng., University of Illinois at Urbana-Champaign, USA<br />
• With uncertainty, it may be<br />
impossible to guarantee that a<br />
robot covers all the free space<br />
in finite time — how should we<br />
measure performance?<br />
• New performance measure:<br />
probability 1−ε of covering a<br />
fraction 1−δ of free space<br />
P(C 1 ) 1<br />
, [0,1]<br />
• Our paper shows the practical<br />
utility of this new performance<br />
measure<br />
robot<br />
area<br />
covered<br />
coverage with negligible uncertainty in<br />
sensing and actuation<br />
coverage with significant uncertainty<br />
in sensing and actuation<br />
How do we define “good performance” for coverage with uncertainty?<br />
08:55–09:00 TuAT9.6<br />
Trajectory Planning with Look-Ahead for<br />
Unmanned Sea Surface Vehicles to<br />
Handle Environmental Disturbances<br />
Petr Svec and Atul Thakur<br />
Department of Mechanical Engineering,<br />
University of Maryland, College Park, USA<br />
Maxim Schwartz<br />
Energetics Technology Center, USA<br />
Satyandra K. Gupta<br />
Department of Mechanical Engineering and Institute for Systems Research,<br />
University of Maryland, College Park, USA<br />
• Developed a look-ahead based<br />
algorithm for trajectory planning<br />
under motion uncertainty for<br />
USSVs<br />
• Motion uncertainty is modeled<br />
and explicitly used during the<br />
trajectory computation<br />
• Algorithm combines heuristic<br />
search with a variation of<br />
minimax game-tree search<br />
• Contingency plan is generated<br />
to efficiently handle sudden large<br />
deviations from the intended<br />
trajectory<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–57–<br />
Trajectory A: Overly conservative trajectory<br />
Trajectory B: Nominal trajectory computed by the finite<br />
horizon look-ahead planner<br />
Trajectory C: Contingency trajectory<br />
Trajectory planning under motion<br />
uncertainty for USSVs<br />
09:15–09:30 TuAT9.8<br />
A Dynamic Sensor Placement Algorithm for<br />
Dense Sampling<br />
Vineet Bhatawadekar, Ravishankar Sivalingam<br />
and Nikolaos Papanikolopoulos<br />
Department of Computer Science, University of Minnesota Twin Cities, USA<br />
• Objective: Determine the next move of<br />
the robot based on the spatial sampling<br />
density of the sensor<br />
• Approach: A convex optimization<br />
framework for determining the next-bestmove.<br />
• Design a convex constraint function<br />
based on the sampling density, and<br />
satisfy as many constraints as possible to<br />
obtain uniform and dense sampling<br />
Object search with our placement<br />
algorithm
Session TuBT1 Continental Parlor 1 Tuesday, September 27, <strong>2011</strong>, 10:00–11:30<br />
Perception, Saliency and Novelty<br />
Chair Nicholas Gans, Univ. Texas at Dallas<br />
Co-Chair Michael Zillich, Vienna Univ. of Tech.<br />
10:00–10:15 TuBT1.1<br />
Multimodal Saliency-based Attention for<br />
Object-based Scene Analysis<br />
Boris Schauerte 1 , Benjamin Kühn 1 , Kristian Kroschel 2<br />
and Rainer Stiefelhagen 1,2<br />
1 Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT), Germany<br />
2 Fraunhofer IOSB, Germany<br />
• Multimodal attention is important for robots<br />
to operate in complex environments and<br />
act as social, cognitive human partners<br />
• Overt attention: active saliency-driven<br />
sensor alignment to improve the perception<br />
• Isophote-based saliency map<br />
segmentation for visual proto-objects<br />
• Surprise-based auditory saliency<br />
• Parametric 3-D saliency model enables<br />
efficient audio-visual saliency fusion<br />
• Audio-visual object validation and<br />
hierarchical, knowledge-driven analysis<br />
• Object-based inhibition of return<br />
Auditory Surprise (bottom)<br />
Fitted Gaussian models (uses Isophotebased<br />
Segmentation; top) and<br />
sequence of attentional shifts (bottom)<br />
10:30–10:45 TuBT1.3<br />
Optimisation of Gaze Movement for Multitasking<br />
Using Rewards<br />
Cem Karaoguz 1,2 , Tobias Rodemann 2 and Britta Wrede 1<br />
1 ) Research Institute for Cognition and Robotics (CoR-Lab), Bielefeld<br />
University, Germany<br />
2 ) Honda Research Institute Europe GmbH, Offenbach, Germany<br />
• A framework that optimises active scene<br />
perception for modular systems is<br />
presented.<br />
• Individual visual processes were<br />
encapsulated in modules.<br />
• Our framework learns how to utilise these<br />
modules in time for different tasks.<br />
• Demonstrated in a multitasking scenario<br />
and performed better than preprogramming<br />
and saliency approaches.<br />
10:15–10:30 TuBT1.2<br />
Robots Looking for Interesting Things:<br />
Extremum Seeking Control on Saliency Maps<br />
Yinghua Zhang, Jinglin Shen and Nicholas Gans<br />
Dept. of Electrical Engineering, University of Texas at Dallas, USA<br />
Mario Rotea<br />
Dept. of Mechanical Engineering, University of Texas at Dallas, USA<br />
• We present a Simplex Guided<br />
Extremum Seeking Control<br />
algorithm applied on a 6-DOF<br />
eye-in-hand robot arm to<br />
optimize the visual stimuli.<br />
• We use Saliency to represent<br />
the level of attractiveness of<br />
visual stimuli, which we take as<br />
a measurement of interest.<br />
• Simulations and experiments<br />
show the new algorithm is more<br />
likely to find a global maximum<br />
than the conventional<br />
Extremum Seeking Control<br />
10:45–10:50 TuBT1.4<br />
Novelty Detection Using Growing Neural Gas for<br />
Visuo-Spatial Memory<br />
Dmitry Kit and Dana Ballard<br />
Computer Science, University of Texas, USA<br />
Brian Sullivan<br />
Psycology, University of Texas, USA<br />
• Change detection is a mechanism that can<br />
be used as an online sensory input filter<br />
• A learned distribution of visuo-spatial<br />
features is used to detect changes<br />
• Trained a self-organizing map with color<br />
histograms and spatial data from a 1 st<br />
person camera<br />
• Online solution predicts color content for a<br />
viewpoint and localizes changes that<br />
deviate from priors<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–58–<br />
Change detection allows for online<br />
filtering so that image parsing<br />
computation can be constrained to<br />
small image regions (black regions)
Session TuBT1 Continental Parlor 1 Tuesday, September 27, <strong>2011</strong>, 10:00–11:30<br />
Perception, Saliency and Novelty<br />
Chair Nicholas Gans, Univ. Texas at Dallas<br />
Co-Chair Michael Zillich, Vienna Univ. of Tech.<br />
10:50–10:55 TuBT1.5<br />
Coherent Spatial Abstraction and Stereo Line<br />
Detection for Robotic Visual Attention<br />
Kai Zhou, Andreas Richtsfeld, Michael Zillich and Markus Vincze<br />
Automation and Control Institute, Vienna University of Technology, Austria<br />
• Planar surfaces are estimated from 3D<br />
point cloud data for spatial reasoning<br />
• Matching edges in image pairs to compute<br />
stereo lines<br />
• Assessing planes and lines to compute<br />
significance values for probabilistic<br />
optimization<br />
• Refine 2D saliency map by eliminating<br />
wrong attentions according to spatial<br />
restriction and enhancing correct<br />
attentions wherein stereo lines are<br />
detected<br />
Up: robotic attention with 2D saliency<br />
Bottom: attention with combination of<br />
spatial and stereo line information<br />
11:00–11:15 TuBT1.7<br />
Visual Anomaly Detection under Temporal and<br />
Spatial Non-uniformity for News Finding Robot<br />
Takahiro Suzuki, Fumihiro Bessho<br />
Tatsuya Harada and Yasuo Kuniyoshi<br />
Department of Mechano-Informatics, Graduate School of Information<br />
Science and Technology, The University of Tokyo, Japan<br />
• Visual anomaly detection framework for a<br />
news finding robot system is proposed.<br />
• Non-uniform Hidden Markov Model is<br />
proposed to handle intermittent<br />
observation at each place.<br />
• The framework reuses information from<br />
similar places. Place similarity is<br />
automatically acquired.<br />
• Experimental results show that the robot<br />
can find news in the real world.<br />
A robot system based on<br />
proposed anomaly detection.<br />
10:55–11:00 TuBT1.6<br />
Visual Machinery Surveillance<br />
for High-Speed Periodic Operations<br />
Idaku Ishii, Yao-dang Wang and Takeshi Takaki<br />
Robotics Laboratory, Hiroshima University, JAPAN<br />
• Machinery surveillance algorithm for<br />
abnormal behavior detection in long-term<br />
abnormal behavior in a sewing machine<br />
t =20.589s<br />
periodic machinery operations in factories<br />
• Automatics phase encoding using several<br />
phase = 0.80 M0 = 34<br />
t =20.596s<br />
significant pixels for periodic operations<br />
phase = 1.13 M0 = 25<br />
• Fast abnormity detection by differencing<br />
input images from their synchronized<br />
t =20.603s<br />
reference images.<br />
• Automated HFR video logging system<br />
phase = 1.91<br />
t =20.610s<br />
M0 = 9<br />
using the machinery surveillance algorithm<br />
phase = 2.43 M0 = 9<br />
on a 1000 fps high-speed vision platform<br />
• Abnormal behavior of a sewing machine<br />
t =20.617s<br />
operating at 12 Hz was recorded in real<br />
time as a 512x512 pixel video at 1000 fps.<br />
phase = 3.02 M0 = 28<br />
t =20.624s<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–59–<br />
(a) input image (b) reference image<br />
phase = 3.61 M0 = 12<br />
(c) difference image<br />
HFR image sequence, selected reference<br />
images, and detected abnormal pixels<br />
11:15–11:30 TuBT1.8<br />
Representation of Manipulation-Relevant Object<br />
Properties and Actions<br />
Susanne Petsch and Darius Burschka<br />
Department of Informatics, Technische Universität München, Germany<br />
• Detection of changes in physical<br />
and functional properties of<br />
objects from unexpected human<br />
actions<br />
• Object-centric representation of<br />
manipulation constraints<br />
• Grounding of a-priori information<br />
to observed geometric and<br />
action attributes<br />
• Efficient monitoring system for<br />
detection of unexpected events<br />
in varying repetitive actions
Session TuBT2 Continental Parlor 2 Tuesday, September 27, <strong>2011</strong>, 10:00–11:30<br />
Semantic SLAM & Loop Closure<br />
Chair Wolfram Burgard, Univ. of Freiburg<br />
Co-Chair Javier Civera, Univ. de Zaragoza<br />
10:00–10:15 TuBT2.1<br />
Application of Locality Sensitive Hashing to<br />
Realtime Loop Closure Detection<br />
Hossein Shahbazi and Hong Zhang<br />
Department of Computing Science, University of Alberta, Canada<br />
• Using Euclidean Locality Sensitive<br />
Hashing (E2LSH) for finding similar visual<br />
features<br />
• Automatic parameter tuning considering<br />
only the distribution of data<br />
• Feature matching using the distance ratio<br />
criterion<br />
• Accurate loop closure detection by<br />
computing image similarities<br />
System Overview:<br />
individual features are<br />
lookup in the map using<br />
LSH<br />
10:30–10:45 TuBT2.3<br />
Bathymetric SLAM with No Map Overlap using<br />
Gaussian Processes<br />
Stephen Barkby, Stefan Williams, Oscar Pizarro and Michael<br />
Jakuba<br />
Australian Centre for Field Robotics, University of Sydney, Australia<br />
• Navigation and Mapping for AUVs can<br />
be corrected by performing SLAM with<br />
multibeam sonar and a Rao<br />
Blackwellized Particle Filter.<br />
• Memory requirements significantly<br />
reduced by storing maps as trajectories<br />
linked to common bathymetry.<br />
• Gaussian Process Regression allows<br />
loop closures to be performed even<br />
when no map overlap is present.<br />
10:15–10:30 TuBT2.2<br />
BRIEF-GIST –<br />
Closing the Loop by Simple Means<br />
Niko Sünderhauf and Peter Protzel<br />
Department of Electrical Engineering and Information Technology,<br />
Chemnitz University of Technology, Germany<br />
• We propose BRIEF-Gist, a highly efficient<br />
scene descriptor for place recognition and<br />
loop closure detection.<br />
• We compare it to FAB-Map on two<br />
standard datasets. Despite its simplicity,<br />
BRIEF-Gist performs comparably well.<br />
• We perform city-scale SLAM using a novel<br />
optimization-based SLAM formulation that<br />
is robust against the few remaining falsepositive<br />
loop closures.<br />
10:45–10:50 TuBT2.4<br />
Place Recognition in 3D Scans Using a Combination of Bag<br />
of Words and Point Feature based Relative Pose Estimation<br />
Bastian Steder, Michael Ruhnke,<br />
Slawomir Grzonka, and Wolfram Burgard<br />
Dept.of Comp. Science, University of Freiburg, Germany<br />
• Place recognition based on 3D range scans.<br />
• A bag-of-words approach is used for a fast<br />
similarity pre-ordering of the scan pairs.<br />
• Uses NARFs (Normal Aligned<br />
Radial Features) to find relative<br />
transformations.<br />
• Experiments using publicly<br />
available datasets and<br />
comparison to other<br />
approaches.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–60–<br />
Detected loop closures for the Hanover2 dataset<br />
(Dataset courtesy of Oliver Wulf)
Session TuBT2 Continental Parlor 2 Tuesday, September 27, <strong>2011</strong>, 10:00–11:30<br />
Semantic SLAM & Loop Closure<br />
Chair Wolfram Burgard, Univ. of Freiburg<br />
Co-Chair Javier Civera, Univ. de Zaragoza<br />
10:50–10:55 TuBT2.5<br />
Adaptive Appearance Based Loop-Closing in<br />
Heterogeneous Environments<br />
András Majdik and Gheorghe Lazea<br />
Robotics Research Group,Technical University of Cluj-Napoca, Romania<br />
Dorian Gálvez-López and José A. Castellanos<br />
Instituto de Ingeniería de Aragón, University of Zaragoza, Spain<br />
• The paper concerns the problem of<br />
detecting loop-closure situations from a<br />
visual appearance-based perspective<br />
• A novel probabilistic on-line weight<br />
updating algorithm is proposed for the<br />
bag-of-words description of the gathered<br />
images<br />
• An intuitive measure of the ability of a<br />
certain word to contribute to the detection<br />
of a correct loop-closure is presented<br />
• The proposed strategy is extensively<br />
tested on challenging large-scale urban<br />
environments<br />
11:00–11:15 TuBT2.7<br />
Memory Management for Real-Time<br />
Appearance-Based Loop Closure Detection<br />
Mathieu Labbé and François Michaud<br />
Department of Electrical and Computer Engineering,<br />
Université de Sherbrooke, QC, Canada<br />
• Appearance-based loop closure detection<br />
approach that respects real-time<br />
constraints for large-scale SLAM<br />
• Memory management (Working Memory,<br />
Long-Term Memory) keeps computation<br />
time for each image acquired under a<br />
fixed time limit<br />
• Incremental approach: no prior knowledge<br />
of the environment is required<br />
• Results demonstrate the approach’s<br />
adaptability and scalability using four<br />
standard data sets<br />
Wait<br />
image<br />
Transfer<br />
(WM�LTM)<br />
yes<br />
no<br />
Image Location<br />
Process time<br />
over T time?<br />
Rehearsal<br />
(STM)<br />
Loop closure<br />
rejected<br />
Retrieval<br />
(LTM�WM)<br />
Bayesian Filter<br />
Update<br />
(WM)<br />
Any<br />
no<br />
hypothesis<br />
over Tloop ?<br />
yes<br />
Flow chart of the appearancebased<br />
loop closure detection<br />
approach<br />
Loop closure<br />
accepted<br />
10:55–11:00 TuBT2.6<br />
SLAM with Learned Object Recognition and<br />
Semantic Data Association<br />
John G Rogers III, Alexander J.B. Trevor, Carlos Nieto-Granda,<br />
Henrik I. Christensen<br />
Robotics and Intelligent Machines, Georgia Institute of Technology, USA<br />
• Complex and structured landmarks like<br />
objects have many advantages over lowlevel<br />
image features for semantic mapping<br />
• Human environments contain many objects<br />
which can serve as suitable landmarks for<br />
robot navigation<br />
• Maps based on high level features which<br />
are identified by a learned classifier could<br />
better inform tasks such as semantic<br />
mapping and mobile manipulation<br />
• This paper presents a technique for<br />
recognizing door signs using a learned<br />
classifier and perform semantic reasoning<br />
for data association<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–61–<br />
Large loop closed by recognizing<br />
and understanding semantic<br />
meaning of office door sign<br />
11:15–11:30 TuBT2.8<br />
Towards Semantic SLAM using a Monocular Camera<br />
Javier Civera, Dorian Gálvez-López, L. Riazuelo, Juan D. Tardós<br />
and J. M. M. Montiel<br />
University of Zaragoza, Spain<br />
• Monocular SLAM maps are usually composed of<br />
‘meaningless’ geometric entities (points or lines).<br />
• Our aim is to augment the high level information<br />
(semantics) in a monocular SLAM map.<br />
• We combine a geometric monocular SLAM<br />
algorithm, visual object recognition and<br />
registration to populate a meaningless pointbased<br />
map with objects.<br />
• Our approach runs in real-time at 7 Hz for local<br />
maps and a small number of objects (6 in our<br />
experiments).<br />
Image sequence and<br />
tracked points and objects<br />
Estimated camera trajectory, point<br />
map and inserted object<br />
Two objects inserted at a later step<br />
in the sequence
Session TuBT3 Continental Parlor 3 Tuesday, September 27, <strong>2011</strong>, 10:00–11:30<br />
Symposium: Microrobotics II<br />
Chair David Cappelleri, Stevens Inst. of Tech.<br />
Co-Chair Sylvain Martel, Ec. Pol. de Montreal (EPM)<br />
10:00–10:15 TuBT3.1*<br />
Semi-Plenary Invited Talk: Microrobotics for<br />
Biomedical Applications<br />
Paolo Dario, Scuola Superiore Sant'Anna<br />
10:30–10:45 TuBT3.3<br />
A MRI-based Integrated Platform for the<br />
Navigation of Microdevices and Microrobots<br />
Manuel Vonthron, Viviane Lalande, Gaël Bringout<br />
Charles Tremblay and Sylvain Martel<br />
Nanorobotics Laboratory, École Polytechnique de Montréal, Canada<br />
• Magnetic Resonance Navigation (MRN)<br />
platform architecture with 3D Tracking<br />
and magnetic gradient based navigation<br />
• 3D wireless navigation with a custom<br />
magnetic gradient coils set providing up<br />
to 460mT/m<br />
• Experimental validation on a catheter<br />
with custom ferro-magnetic tip<br />
• Integrated platform enabling manual<br />
control or closed-loop control<br />
Superimposed deflections on a<br />
catheter obtained by varying a<br />
magnetic gradient inside a clinical MRI<br />
10:15–10:30 TuBT3.2<br />
Semi-Plenary Invited Talk: Microrobotics for<br />
Biomedical Applications<br />
Paolo Dario, Scuola Superiore Sant'Anna<br />
10:45–10:50 TuBT3.4<br />
Rotating Magnetic Micro-Robots for Versatile<br />
Non-Contact Fluidic Manipulation of Micro-Objects<br />
Eric Diller, Zhou Ye, and Metin Sitti<br />
Mechanical Engineering, Carnegie Mellon University, USA<br />
• Low Reynolds number micro-object<br />
manipulation by magnetic micro-robots<br />
• Rotational fluid flow induced to translate<br />
micro-objects<br />
• Position of microrobots is controlled by<br />
magnetic docks embedded in the<br />
substrate or by feedback control<br />
• High speed and precision manipulation<br />
is accomplished by regulating the<br />
spinning speed and separation distance<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–62–<br />
Magnetic dock concept to create<br />
reconfigurable virtual flow channels. The<br />
array of docks is embedded in the surface<br />
and acts to trap spinning micro-robots at<br />
prescribed locations to achieve controlled<br />
parallel fluid manipulation of micro-objects<br />
along complex paths
Session TuBT3 Continental Parlor 3 Tuesday, September 27, <strong>2011</strong>, 10:00–11:30<br />
Symposium: Microrobotics II<br />
Chair David Cappelleri, Stevens Inst. of Tech.<br />
Co-Chair Sylvain Martel, Ec. Pol. de Montreal (EPM)<br />
10:50–10:55 TuBT3.5<br />
MRI Magnetic Signature Imaging, Tracking and<br />
Navigation for Targeted Micro/Nano-capsule<br />
Therapeutics<br />
David Folio and Antoine Ferreira<br />
PRISME, ENSI Bourges, France<br />
Christian Dahmen, Tim Wortmann and Sergej Fatikow<br />
AMiR, University of Oldenburg, Germany<br />
M. Arif Zeeshan, Kaiyu Shou, Salvador Pané, Bradley J. Nelson<br />
ASRL, IRIS, ETH Zurich, Switzerland<br />
• MRI as a medical imaging device to be<br />
used for targeted drug delivery<br />
• Tailored nanoparticles are designed as<br />
drug carriers with magnetic moment for<br />
actuation<br />
• MRI gradient coils propel magnetic<br />
objects, position feedback by artifact<br />
imaging and tracking of artifacts<br />
• Path extraction using Franqi filter and<br />
FMM, Navigation planning with modeled<br />
uncertainty<br />
11:00–11:15 TuBT3.7<br />
Design and Fabrication of<br />
Air-Flow based Single Particle Dispensing System<br />
Tomohiro Kawahara1 , Shigeo Ohashi1 , Masaya Hagiwara1 ,<br />
Yoko Yamanishi1,2 , and Fumihito Arai1,3 1 Graduate School of Engineering, Nagoya University, Japan<br />
2 PRESTO, JST, Japan<br />
3 Seoul National University, Korea<br />
• Design and fabrication to increase<br />
the success rate of single particle<br />
dispensing.<br />
• Disposable microchip which is<br />
composed of two capacitance<br />
sensors and air-flow based inkjet<br />
mechanism.<br />
• The developed system has capability<br />
to eject 3 particles/sec and maximum<br />
flow velocity is 10 mm/s.<br />
• We succeed in automatic dispensing<br />
of a single particle (=100 um) from a<br />
biochip to culture well atmosphere<br />
with the success rate of 50 %.<br />
• Application to single swine oocyte<br />
dispensing.<br />
Concept<br />
Results<br />
10:55–11:00 TuBT3.6<br />
Tumor Targeting by Computer Controlled<br />
Guidance of Magnetotactic Bacteria Acting Like<br />
Autonomous Microrobots<br />
Ouajdi Felfoul, Mahmood Mohammadi, Louis Gaboury, and Sylvain Martel<br />
NanoRobotics Laboratory, Ecole Polytechnique Montreal, Canada<br />
• Magnetotactic Bacteria are controlled by<br />
computer as microrobots<br />
• A directional magnetic field is used to<br />
indicate the target to be reached by the<br />
bacteria<br />
• These bacteria have the capability to<br />
travel in the human microvasculature<br />
• As such, they could potentially deliver<br />
therapeutics inside tumors<br />
• Here we show that they can indeed<br />
penetrate solid tumors<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–63–<br />
Flagella<br />
Directional System<br />
MC-1 Bacterium<br />
11:15–11:30 TuBT3.8<br />
Microrobotic Simulator for Assisted Biological<br />
Cell Injection<br />
Hamid Ladjal 1,2 , Jean-Luc Hanus 2 , Antoine Ferreira 2<br />
1 LIRIS CNRS UMR 5205, Université Claude Bernard Lyon 1, France<br />
2 Laboratoire PRISME, ENSI Bourges, France<br />
• ICSI simulator<br />
• Biomechanical models for cell<br />
injection:<br />
- Linear elastic FEM<br />
- Hyperelastic FEM<br />
• 3D real-time virtual reality based<br />
ICSI simulator:<br />
- Off-line computation<br />
- Real-time haptics-enable simulator<br />
-Virtual coupling for stability of the<br />
haptic rendering<br />
• Experiments and validation<br />
Visual comparison between<br />
FEM simulations and<br />
experimental data.
Session TuBT4 Continental Ballroom 4 Tuesday, September 27, <strong>2011</strong>, 10:00–11:30<br />
Industrial Forum: Robots: The Next Generation<br />
Chair Steve Cousins, Willow Garage, Inc<br />
Co-Chair<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–64–
Session TuBT5 Continental Ballroom 5 Tuesday, September 27, <strong>2011</strong>, 10:00–11:30<br />
Symposium: Medical Robotics II<br />
Chair Paolo Fiorini, Univ. of Verona<br />
Co-Chair Jaydev P. Desai, Univ. of Maryland<br />
10:00–10:15 TuBT5.1*<br />
Semi-Plenary Invited Talk: Challenges in MRI-guided<br />
Interventions<br />
Jaydev P. Desai, University of Maryland<br />
10:30–10:45 TuBT5.3<br />
On the Design of an Interactive, Patient-Specific<br />
Surgical Simulator for Mitral Valve Repair<br />
Neil A. Tenenholtz, Robert J. Schneider, and Robert D. Howe<br />
School of Engineering and Applied Sciences, Harvard University, USA<br />
Peter E. Hammer and Nikolay V. Vasilyev<br />
Department of Cardiac Surgery, Children’s Hospital Boston, USA<br />
• A patient-specific surgical simulator<br />
was developed to assist in planning<br />
mitral valve repair.<br />
• Simulation occurred at 1kHz allowing for<br />
real-time haptic interaction with the<br />
virtual model.<br />
• The valve was simulated to closure in<br />
less than 1s with sub-millimeter<br />
accuracy.<br />
• A cardiac surgeon used the system to<br />
repair 3 pathological valves with minimal<br />
training.<br />
10:15–10:30 TuBT5.2<br />
Investigation of Magnetic Guidance of Cochlear<br />
Implants<br />
James R. Clark, Lisandro Leon, and Jake J. Abbott<br />
Department of Mechanical Engineering, University of Utah, USA<br />
Frank M. Warren<br />
Department of Otolaryngology, Oregon Health & Science University, USA<br />
• Cochlear implant insertions are widely<br />
known to produce trauma, often leading to<br />
loss of residual hearing.<br />
• One of the goals for development of new<br />
electrode arrays is consistent atraumatic<br />
insertions.<br />
• We propose a concept in which a<br />
magnetically tipped cochlear-implant<br />
electrode array is guided during insertions.<br />
• In scaled in vitro studies, insertion forces<br />
were reduced by approximately 50% using<br />
magnetic guidance.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–65–<br />
Concept for magnetically guided<br />
cochlear implant surgery. Red<br />
wide arrows indicate the three<br />
controlled degrees of freedom.<br />
10:45–10:50 TuBT5.4<br />
Ergonomic and Gesture Performance of<br />
Robotized Instruments for Laparoscopic Surgery<br />
Benoît Herman 1,2 , Ali Hassan Zahraee 1 , Jérôme Szewczyk 1 ,<br />
Guillaume Morel 1 , Christophe Bourdin 3 , Jean-Louis Vercher 3<br />
and Brice Gayet 4<br />
1 ISIR, UPMC Univ Paris 06 - CNRS, France<br />
2 CEREM, Université catholique de Louvain - FNRS, Belgium<br />
3 ISM, Université de la Méditerranée - CNRS, France<br />
4 Department of Digestive Diseases, Montsouris Institute, France<br />
• New laparoscopic instrument is designed<br />
with robotized intra-abdominal DOFs<br />
and articulated handle<br />
• Two surgical tasks are implemented in a<br />
virtual reality simulator<br />
• A new RULA-based real-time ergonomic<br />
score is proposed<br />
• Surgeons and PhD students performed an<br />
experimental comparison of several<br />
instruments<br />
• The new instrument was more ergonomic<br />
• Design should be improved to increase<br />
precision
Session TuBT5 Continental Ballroom 5 Tuesday, September 27, <strong>2011</strong>, 10:00–11:30<br />
Symposium: Medical Robotics II<br />
Chair Paolo Fiorini, Univ. of Verona<br />
Co-Chair Jaydev P. Desai, Univ. of Maryland<br />
10:50–10:55 TuBT5.5<br />
Laparoscopic Optical Biopsies: In Vivo<br />
Robotized Mosaicing with Probe-based Confocal<br />
Endomicroscopy<br />
Benoît Rosa 1 and Benoît Herman 1,3 and Jerôme Szewczyk 1 and<br />
Brice Gayet 1,2 and Guillaume Morel 1<br />
1 ISIR, UPMC Univ Paris 06 - CNRS, France<br />
2 Department of Digestive Diseases, Montsouris Institute, France<br />
3 CEREM, Université Catholique de Louvain - FNRS, Belgium<br />
a – in vivo validation of the prototype<br />
b – computed mosaic from the acquired images<br />
• A minimally invasive device for performing optical biopsies<br />
• Microactuation using hydraulic microballoons<br />
• Mechanical passive physiological motion compensation<br />
• Preliminary in vivo validation on porcine model<br />
11:00–11:15 TuBT5.7<br />
Shape Estimation for Image-Guided Surgery with<br />
a Highly Articulated Snake Robot<br />
Stephen Tully 1 , George Kantor 2 , Marco Zenati 3 , Howie Choset 2<br />
1 Electrical and Computer Engineering, Carnegie Mellon University, USA<br />
2 Robotics Institute, Carnegie Mellon University, USA<br />
3 Harvard Medical School, Harvard University, USA<br />
• Using an EKF, we estimate the full shape<br />
of a surgical robot using a 5-DOF<br />
magnetic tracker measurement at the<br />
distal tip<br />
• For the prediction step of the filter, we<br />
introduce motion models for the HARP<br />
surgical snake robot<br />
• We analyze the observability of shape<br />
estimation for this system and show that<br />
given sufficient motion, the state is fully<br />
observable<br />
• To demonstrate our methods, we show<br />
results from an image-guidance<br />
experiment on a porcine subject<br />
Shape estimation for the HARP<br />
snake robot during an imageguidance<br />
experiment<br />
10:55–11:00 TuBT5.6<br />
Sensor and Sampling-based Motion Planning<br />
for Minimally Invasive Robotic Exploration<br />
of Osteolytic Lesions<br />
Wen P. Liu, Blake C. Lucas, Kelleher Guerin<br />
Department of Computer Science, Johns Hopkins University, USA<br />
Erion Plaku<br />
Department of Electrical Engineering and Computer Science,<br />
Catholic University of America, USA<br />
• Paper develops planning framework to<br />
control a flexible cannula robot to<br />
explore osteolytic lesions<br />
• Framework effectively combines global<br />
and local planning with information<br />
gain<br />
• Overall goal is to assist orthopaedic<br />
surgeons in minimally invasive<br />
treatment of osteolysis<br />
• Osteolysis is a result of bearing<br />
material wear in total hip replacement<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–66–<br />
Planner controls the snake-like<br />
cannula robot in minimally invasive<br />
treatment of osteolysis in order to<br />
effectively explore with its tip the<br />
osteolytic cavity<br />
11:15–11:30 TuBT5.8<br />
A Virtual Scalpel System for Computer-Assisted<br />
Laser Microsurgery<br />
Leonardo S. Mattos, Giulio Dagnino, Gabriele Becattini,<br />
Darwin G. Caldwell<br />
Advanced Robotics Dept., Italian Institute of Technology, Italy<br />
Massimo Dellepiane<br />
ENT Dept., UNIGE, Italy<br />
• Allows precise and safe laser<br />
control directly for live video of<br />
the surgical site<br />
• New motorized laser<br />
micromanipulator with 1μm<br />
accuracy<br />
• Intuitive intraoperative planning<br />
and automatic plan execution<br />
• Virtual features for increased<br />
safety during teleoperation<br />
• No operation training required<br />
• 50% reduction in laser aiming<br />
errors<br />
[Patient drawing from Hochman et al.]
Session TuBT6 Continental Ballroom 6 Tuesday, September 27, <strong>2011</strong>, 10:00–11:30<br />
Symposium: Grasping and Manipulation: Mechanics and Design<br />
Chair Aaron Dollar, Yale Univ.<br />
Co-Chair Aaron Edsinger, Meka Robotics<br />
10:00–10:15 TuBT6.1*<br />
Semi-Plenary Invited Talk: Preliminary Results with<br />
NASA's Robonaut 2 Hand<br />
Robert Ambrose, NASA Johnson Space Center<br />
10:30–10:45 TuBT6.3<br />
FAS A flexible Antagonistic spring element for a<br />
high performance over actuated hand<br />
Werner Friedl, Maxime Chalon, Jens Reinecke and Markus<br />
Grebenstein<br />
Institute of Robotics and Mechatronics, German Aerospace Center<br />
DLR Hand-Arm-System with<br />
opened forearm with 19 FAS<br />
• The FAS is an antagonistic spring element<br />
designed for the over actuated hand of<br />
the DLR hand-arm system<br />
• It showed its design flexibility to adapt on<br />
38 various stiffness characteristics and<br />
tendon lengths<br />
• The developed magnet sensor fulfils the<br />
design goals to be very compact and<br />
precise.<br />
• The tests show a double increase of the<br />
performance against the version in the<br />
testbed.<br />
10:15–10:30 TuBT6.2<br />
Semi-Plenary Invited Talk: Preliminary Results with<br />
NASA's Robonaut 2 Hand<br />
Robert Ambrose, NASA Johnson Space Center<br />
10:45–10:50 TuBT6.4<br />
Varying spring preloads<br />
to select grasp strategies in an adaptive hand<br />
Daniel Aukes, Barrett Heyneman, and Mark Cutkosky<br />
Mechanical Engineering, Stanford University, USA<br />
Vincent Duchaine<br />
Mechanical Engineering, École de Technologie Supérieure, Canada<br />
• Variable spring preloads allow an<br />
underactuated robotic hand to<br />
change grasping poses,<br />
accommodating large and small<br />
objects.<br />
• Grasp stability can be ensured for<br />
a larger range of object sizes<br />
than with a highly underactuated<br />
hand.<br />
• Grasps can be adjusted for<br />
situations that require higher<br />
precision or more safety.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–67–<br />
The Seabed Rig Hand in a<br />
“power-pinch” grasp
Session TuBT6 Continental Ballroom 6 Tuesday, September 27, <strong>2011</strong>, 10:00–11:30<br />
Symposium: Grasping and Manipulation: Mechanics and Design<br />
Chair Aaron Dollar, Yale Univ.<br />
Co-Chair Aaron Edsinger, Meka Robotics<br />
10:50–10:55 TuBT6.5<br />
A highly-underactuated robotic hand<br />
with force and joint angle sensors<br />
Long Wang*, Joseph DelPreto**,<br />
Sam Bhattacharyya*. Jonathan Weisz***,<br />
and Peter K. Allen***<br />
*Mechanical Engineering, **Electrical Engineering, ***Computer Science,<br />
Columbia University, USA<br />
• Columbia Hand – a three-fingered<br />
underactuated robotic hand is<br />
designed, prototyped, and tested<br />
• Highly-Underactuated – one<br />
motor actuates 9 DOF, while<br />
another actuates “Thumb” rotation<br />
• Position and Tactile Sensors –<br />
precise angle feedback and binary<br />
force feedback<br />
• Experiments – natural pre-shape<br />
stage; stable self-adaptive grasp<br />
for different shapes<br />
Columbia Hand<br />
with Position and Force sensors<br />
11:00–11:15 TuBT6.7<br />
Dynamic Nonprehensile Shaping<br />
of a Thin Rheological Object<br />
Tomoyuki Inahara, Mitsuru Higashimori,<br />
Kenjiro Tadakuma and Makoto Kaneko<br />
Department of Mechanical Engineering, Osaka University, Japan<br />
• A rheological object is remotely shaped on<br />
a plate attached at the tip of a bar.<br />
• A one-dimensional viscous model is<br />
introduced by focusing on the plastic<br />
deformation of the object.<br />
• Sufficient conditions for the plate<br />
accelerations to enlarge and to contract<br />
the object are derived.<br />
• Experimental results are shown for<br />
confirming the validity of the proposed<br />
shaping method.<br />
Nonprehensile shaping of a<br />
rheological object<br />
10:55–11:00 TuBT6.6<br />
Active Outline Shaping of a Rheological Object<br />
Based on Plastic Deformation Distribution<br />
Kayo Yoshimoto<br />
Department of Health Science, Osaka University, Japan<br />
Mitsuru Higashimori, Kenjiro Tadakuma, and Makoto Kaneko<br />
Department of Mechanical Engineering, Osaka University, Japan<br />
• A handling issue for a rheological object is<br />
discussed.<br />
• Based on plastic deformation distribution,<br />
the shaping method of the object’s outline<br />
by using an uniaxial gripper is proposed.<br />
• The desired shape outline is controlled by<br />
actively managing the integrated stress.<br />
• Experimental results by using a real<br />
rheological object is shown for confirming<br />
the validity of the proposed method.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–68–<br />
Active shaping of a rheological<br />
object<br />
11:15–11:30 TuBT6.8<br />
Softness Effects on Manipulability and Grasp<br />
Stability<br />
Tetsuyou Watanabe<br />
School of Mechanical Engineering, Kanazawa University, Japan<br />
• Derivation of criterion index for<br />
manipulability including softness effect<br />
• Derivation of criterion index for grasp<br />
stability including softness effect<br />
• Softness effect on manipulability and<br />
grasp stability: the increase of the<br />
softness (decrease of stiffness)<br />
decreases the manipulability while it<br />
increases generable object wrench<br />
(grasp stability)<br />
Manipulability index : Im (m/s)<br />
0.66<br />
0.64<br />
0.62<br />
0.6<br />
Manipulability index<br />
0.6<br />
0.5<br />
0.4<br />
0.3<br />
Generable wrench index : If (N)<br />
0.58<br />
0.2<br />
Grasp stability index<br />
0.56<br />
0.1<br />
1 3 5 7 9<br />
Stiffness parameter : ki (�10000)
Session TuBT7 Continental Parlor 7 Tuesday, September 27, <strong>2011</strong>, 10:00–11:30<br />
Contact and Deformation<br />
Chair Marsette Vona, Northeastern Univ.<br />
Co-Chair Jeff Trinkle, Rensselaer Pol. Inst.<br />
10:00–10:15 TuBT7.1<br />
Combining Imitation and Reinforcement<br />
Learning to Fold Deformable Planar Objects<br />
Benjamin Balaguer and Stefano Carpin<br />
School of Engineering, University of California, Merced, USA<br />
• Learning algorithm allowing<br />
cooperative manipulators to perform<br />
towel folding tasks;<br />
• Human demonstrations are<br />
exploited in an imitation learning<br />
framework to find a good starting<br />
seed for reinforcement learning;<br />
• PoWER’s reinforcement learning<br />
algorithm is applied to refine the<br />
imitation learning process and<br />
converge to a successful action;<br />
• Strengths of the algorithm are its<br />
efficient processing, fast learning<br />
capabilities, absence of a<br />
deformable object model, and<br />
applicability to other problems<br />
exhibiting temporally incoherent<br />
parameter spaces.<br />
Figure caption is optional,<br />
use Arial Narrow 20pt<br />
10:30–10:45 TuBT7.3<br />
Toward Simpler Models of Bending Sheet Joints<br />
Lael U Odhner and Aaron M Dollar<br />
Department of Mechanical Engineering, Yale University, USA<br />
• In this paper, we introduce a<br />
variational model that uses only five<br />
parameters to describe the threedimensional<br />
shape of a sheet<br />
flexure joint<br />
• This model can be used to capture<br />
the parasitic twisting motions of the<br />
sheet.<br />
• We parameterize the twists on the<br />
backbone curve running along the<br />
length of the sheet<br />
• The Jacobians and Hessians of the<br />
joint kinematics can be computed in<br />
a straightforward manner, enabling<br />
classical robot modeling and control<br />
of these continuum structures<br />
10:15–10:30 TuBT7.2<br />
Bimanual Robotic Cloth Manipulation for<br />
Laundry Folding<br />
Christian Bersch, Benjamin Pitzer, Sören Kammel<br />
Robert Bosch Research and Technology Center North America, USA<br />
• Autonomous folding of garments from a<br />
random initial cloth configuration with a<br />
PR2 robot<br />
• Novel method for computing grasp poses<br />
accounting for cloth deformability<br />
• Automated learning of an evaluation<br />
function for assessment of grasp pose<br />
quality<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–69–<br />
PR2 robot manipulates T-shirt<br />
10:45–10:50 TuBT7.4<br />
Bi-Manual Robotic Paper Manipulation<br />
Based on Real-Time Marker Tracking and<br />
Physical Modelling<br />
Christof Elbrechter, Robert Haschke and Helge Ritter<br />
Neuroinfomatics Group, Bielefeld University, Germany<br />
• Bi-manual manipulation of paper<br />
using two Shadow dexterous<br />
robot hands (20 DOF)<br />
• Comparison between a purely<br />
mathematical approach and a<br />
physical modeling approach<br />
• Multi-camera real-time tracking in<br />
presence of occlusions using a<br />
new fiducial marker design
Session TuBT7 Continental Parlor 7 Tuesday, September 27, <strong>2011</strong>, 10:00–11:30<br />
Contact and Deformation<br />
Chair Marsette Vona, Northeastern Univ.<br />
Co-Chair Jeff Trinkle, Rensselaer Pol. Inst.<br />
10:50–10:55 TuBT7.5<br />
Understanding the difference between prox and<br />
complementarity formulations for simulation of<br />
systems with contact<br />
Thorsten Schindler<br />
INRIA Grenoble – Rhône-Alpes, France<br />
Binh Nguyen, Jeff Trinkle<br />
Rensselaer Polytechnic Institute, USA<br />
• Complementarity formulations are well<br />
known to represent constraints in robotics<br />
• We present the prox formulation as an<br />
alternative and mathematically equivalent<br />
representation offering new numerical<br />
solution strategies<br />
• A study of fixed-point solution schemes<br />
for prox function systems shows that<br />
convergence depends on an additional<br />
parameter<br />
• The paradox of Painlevé illustrates that<br />
fixed-point schemes can fail while pivoting<br />
for complementarity formulations will<br />
succeed<br />
Two solutions in the paradox of<br />
Painlevé illustrate the fixed-point<br />
behavior of prox formulations<br />
11:00–11:15 TuBT7.7<br />
Estimation of Unknown Curvature using a Coarse-<br />
Resolution Sensor and Contact Kinematics<br />
Tri Cong Phung, Hansang Chae, Min Jeong Kim, Dongmin Choi,<br />
Seung Hoon Shin, Hyungpil Moon, Ja Choon Koo<br />
and Hyouk Ryeol Choi<br />
School of Mechanical Engineering, Sungkyunkwan University, Korea<br />
• Active sensing of unknown object by using<br />
rolling and sliding motion of fingertip<br />
• Applicable to an off-the-shelf sensor not<br />
with very high-resolution<br />
• Method for tracking unknown surface and<br />
estimating sliding motion is proposed<br />
• Simulations and experiments have been<br />
done to verify the proposed algorithm.<br />
Experimental setup<br />
10:55–11:00 TuBT7.6<br />
Curved Surface Contact Patches<br />
with Quantified Uncertainty<br />
Marsette Vona and Dimitrios Kanoulas<br />
College of Computer and Information Science, Northeastern University, USA<br />
• detailed models for 10 bounded curvedsurface<br />
patch types for contact regions<br />
both in the environment and on a robot<br />
• minimal geometric parameterizations<br />
using the exponential map for spatial pose<br />
• fast nonlinear fitting algorithm including<br />
quantified uncertainty both in the input<br />
points and the output patch<br />
• experimental results using Kinect<br />
• all sourcecode provided as the open-source<br />
surface patch library (SPL)<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–70–<br />
rock sampled with Kinect<br />
and 21 fitted surface patches<br />
11:15–11:30 TuBT7.8<br />
Singular surfaces and cusps in symmetric<br />
planar 3-RPR manipulators<br />
Michel Coste<br />
IRMAR, Université de Rennes I, France<br />
Philippe Wenger and Damien Chablat<br />
IRCCyN, CNRS - Ecole Centrale de Nantes, France<br />
• Moving triangle congruent to base<br />
triangle by indirect isometry<br />
• Coordinates reflecting the de-coupling<br />
of the DKP in two successive steps of<br />
degrees 3 and 2 are used<br />
• Precise description of the singularity<br />
surfaces and their cusp edges for all<br />
these manipulators<br />
• This allows sorting of assembly<br />
modes, used for motion planning in the<br />
joint space
Session TuBT8 Continental Parlor 8 Tuesday, September 27, <strong>2011</strong>, 10:00–11:30<br />
Biomimetic Limbed Robots<br />
Chair Elena Garcia, Centre for Automation and Robotics - CSIC-UPM<br />
Co-Chair Olivier Ly, INRIA / Lab. - Bordeaux Univ.<br />
10:00–10:15 TuBT8.1<br />
Arm-Hand Movement: Imitation of Human<br />
Natural Gestures with Tenodesis Effect<br />
Kien-Cuong Nguyen and Véronique Perdereau<br />
UPMC Univ Paris 06, UMR 7222, ISIR,<br />
F-75005, FRANCE<br />
• Decipher natural<br />
movements of<br />
the human armhand<br />
system<br />
based on<br />
mechanical<br />
constraints<br />
• Elaborate the<br />
notion of muscle<br />
comfort which<br />
helps finding the<br />
human natural<br />
postures in the<br />
situation of<br />
redundancy<br />
Optimal choice of palm position in cylindrical grasp<br />
10:30–10:45 TuBT8.3<br />
Climbot: A Modular Bio-inspired Biped<br />
Climbing Robot<br />
Yisheng Guan, Li Jiang, Haifei Zhu, Xuefeng Zhou, Chuanwu Cai,<br />
Wenqiang Wu, Zhanchu Li, Hong Zhang and Xianmin Zhang<br />
Biomimetic and Intelligent Robotics Lab (BIRL)<br />
School of Mechanical and Automotive Engineering<br />
South China University of Technology, Guangzhou, China<br />
�A modular bio-inspired biped climbing<br />
robot - Climbot is developed for highrise<br />
work in agriculture, forestry and<br />
construction<br />
� The robot has both climbing and<br />
manipulating functions<br />
�The mechanical and control system of<br />
the robot are introduced<br />
�Three climbing gaits – inchworm gait,<br />
swinging-around gait and flipping-over<br />
gait – are illustrated with experiments<br />
�An application is also demonstrated<br />
Climbot unscrewing a light-bulb<br />
10:15–10:30 TuBT8.2<br />
Bio-inspired vertebral column, compliance and semipassive<br />
dynamics in a lightweight humanoid robot<br />
Olivier Ly<br />
INRIA / LaBRI – Bordeaux University - France<br />
and Matthieu Lapeyre and Pierre-Yves Oudeyer<br />
Flowers – INRIA Bordeaux - France<br />
• Compliance and semi-passive dy-<br />
namics for locomotion of humanoid<br />
robots.<br />
• Robustness against unknown<br />
external perturbations.<br />
• The advantages of a bio-inspired<br />
multi-articulated vertebral column.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–71–<br />
Compliance of the Acroban Robot<br />
10:45–10:50 TuBT8.4<br />
Passive undulatory gaits enhance walking in a<br />
myriapod millirobot<br />
Katie L. Hoffman and Robert J. Wood<br />
School of Engineering and Applied Sciences, Harvard University, USA<br />
• Design and modeling of a<br />
centipede millirobot with a<br />
compliant body and passive<br />
intersegmental connections<br />
• Body undulations result from<br />
changing phase of stance between<br />
adjacent segments in simulation<br />
and experiments<br />
• These undulations enhance<br />
locomotion when compared to nonundulatory<br />
gaits<br />
• The model and millirobot can be<br />
used to study aspects of biological<br />
myriapod locomotion<br />
Photo of a 10-segment, 20-leg<br />
centipede millirobot
Session TuBT8 Continental Parlor 8 Tuesday, September 27, <strong>2011</strong>, 10:00–11:30<br />
Biomimetic Limbed Robots<br />
Chair Elena Garcia, Centre for Automation and Robotics - CSIC-UPM<br />
Co-Chair Olivier Ly, INRIA / Lab. - Bordeaux Univ.<br />
10:50–10:55 TuBT8.5<br />
Mechanical Design of a Tree Gripper for<br />
Miniature Tree-Climbing Robots<br />
Tin Lun Lam and Yangsheng Xu<br />
Department of Mechanical and Automation Engineering, The Chinese<br />
University of Hong Kong, Hong Kong, China<br />
• Development of a claw-based gripper for<br />
miniature tree-climbing robots<br />
• Capable of attaching on a wide variety of<br />
trees with a wide range of gripping<br />
curvature<br />
• Actuated by one actuator<br />
• Lightweight and simple in control<br />
• Allows zero energy consumption in static<br />
gripping<br />
A tree-climbing robot - Treebot<br />
11:00–11:15 TuBT8.7<br />
Neural-Body Coupling for Emergent<br />
Locomotion: a Musculoskeletal Quadruped<br />
Robot with Spinobulbar Model<br />
Yasunori Yamada, Satoshi Nishikawa, Kazuya Shida<br />
and Yasuo Kuniyoshi<br />
Grad. School of Info. Sci. & Tech., the Univ. of Tokyo, Japan<br />
Ryuma Niiyama<br />
Computer Science and Artificial Intelligence Lab, MIT, USA<br />
• The robot with biologically-realistic<br />
features in actuator and its configuration,<br />
sensor and nervous system<br />
• Quantifying the contribution of the<br />
morphology in structuring sensorimotor<br />
information via embodied interaction<br />
• Various and coordinated locomotion<br />
emerging from dynamic interaction with<br />
no pre-defined coordination circuit<br />
Robot and muscle configuration.<br />
10:55–11:00 TuBT8.6<br />
Bio-inspired Step Crossing Algorithm<br />
for a Hexapod Robot<br />
Ya-Cheng Chou, Wei-Shun Yu, Ke-Jung Huang, and Pei-Chun Lin<br />
Department of Mechanical Engineering, National Taiwan University, TAIWAN<br />
• The algorithm is inspired by the<br />
observation that the cockroach changes<br />
the tripod gait to a special gait to cross the<br />
high steps<br />
• Gait is composed by two stages:<br />
• “Rearing stage” to lift the front side of<br />
the body<br />
• “Lifting stage” to maneuver the body<br />
COM to pass the edge of the step<br />
• With the inclinometer feedback, the robot<br />
can automatically adjust its gait for<br />
crossing steps with different heights<br />
• The performance of the algorithm is<br />
experimentally evaluated<br />
11:15–11:30 TuBT8.8<br />
Design and development of a biomimetic leg<br />
using hybrid actuators<br />
Elena Garcia, Juan C. Arevalo, Fernando Sanchez, Javier F.<br />
Sarria and Pablo Gonzalez-de-Santos<br />
Centre for Automation and Robotics,<br />
Spanish National Research Council, Spain<br />
• The design, development and preliminary<br />
tests of a robotic leg for agile locomotion<br />
is described.<br />
• The multidisciplinary performance of the<br />
natural muscle is imitated by combination<br />
of technologies.<br />
• The actuation system is based on the<br />
hybrid use of series elasticity and<br />
magneto-rheological dampers .<br />
• Experiments with the real leg prototype<br />
show a natural-looking motion following<br />
reference data from CGA .<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–72–<br />
The HADE leg prototype
Session TuBT9 Continental Parlor 9 Tuesday, September 27, <strong>2011</strong>, 10:00–11:30<br />
Perceptual Learning<br />
Chair Danica Kragic, KTH<br />
Co-Chair Timothy Bretl, Univ. of Illinois at Urbana-Champaign<br />
10:00–10:15 TuBT9.1<br />
Learning spatial relations<br />
from functional simulation<br />
Kristoffer Sjöö and Patric Jensfelt<br />
Centre for Autonomous Systems,<br />
Royal Institute of Technology, Sweden<br />
• Understanding spatial relationships will be<br />
important for autonomous robots in<br />
complex environments<br />
• We submit that functional distinctions are<br />
crucial for conceptualizing spatial relations<br />
• 5 different functional distinctions are<br />
learned from data obtained through<br />
physics simulation<br />
• Resulting models can successfully predict<br />
qualitative action outcomes<br />
10:30–10:45 TuBT9.3<br />
Online Multiple Instance Learning Applied to<br />
Hand Detection in a Humanoid Robot<br />
Carlo Ciliberto 1 , Fabrizio Smeraldi 2<br />
Lorenzo Natale 1 , Giorgio Metta 1<br />
1) RBCS, Istituto Italiano di Tecnologia, Italy<br />
2) EECS, Queen Mary University of London, United Kingdom<br />
• We propose an online algorithm for the<br />
visual detection and localisation of the<br />
hand of the iCub robot that imposes low<br />
requirements on supervision.<br />
• Learning is achieved by online boosting<br />
using ‘Multiple Instance’ weak learners<br />
implemented as balls in feature space.<br />
• The weak learners are wrapped by<br />
‘selectors’ to allow for feature selection.<br />
• Coarse labeling strategies (e.g. motion)<br />
can be used for self-supervised<br />
learning.<br />
• To the best of our knowledge, this is the<br />
first application of online Multiple<br />
Instance Learning in robotics.<br />
Examples of localisation for the<br />
hand of the iCub humanoid robot<br />
10:15–10:30 TuBT9.2<br />
Multimodal Categorization<br />
by Hierarchical Dirichlet Process<br />
Tomoaki Nakamura, Takayuki Nagai<br />
Dept. of Elec. Eng., The Univ. of Electro-Communications, Japan<br />
Naoto Iwahashi<br />
NICT Knowledge Creating Communication Research Center, Japan<br />
• A nonparametric Bayesian framework for<br />
unsupervised object categorization by a<br />
robot<br />
• Multimodal (visual, audio and haptic)<br />
information is utilized for the<br />
categorization<br />
• Hierarchical Dirichlet Process (HDP) is<br />
extended to multimodal HDP<br />
It enables the system to estimate<br />
the number of categories<br />
• The proposed method provides a<br />
probabilistic framework for inferring<br />
object properties from limited<br />
observations<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–73–<br />
Objects<br />
Multimodal<br />
Information<br />
Category1 Category2 Category3<br />
10:45–10:50 TuBT9.4<br />
Active learning using a Variational Dirichlet<br />
Process model for pre-clustering and<br />
classification of underwater stereo imagery<br />
Ariell Friedman, Daniel Steinberg,<br />
Oscar Pizarro and Stefan B. Williams<br />
Australian Centre for Field Robotics, The University of Sydney, Australia<br />
• Active learning using a Variational<br />
Dirichlet Process (VDP) model:<br />
- Pre-clustering & classification<br />
- Minimises human effort for training &<br />
improves classification accuracy<br />
- Utilises structure in unlabelled data<br />
• Features: 3D morphology combined with<br />
colour and image texture<br />
• Comparison: similar implementations<br />
using Expectation Maximisation (EM)<br />
and Naive Bayes classifier (NB)<br />
• Result: fewer labels are required to<br />
achieve a given level of accuracy<br />
Mean accuracy<br />
Std dev<br />
Least L t confident fid t sampling li with ithdiff different t classifiers l ifi<br />
1<br />
0.8<br />
0.6<br />
10 0<br />
0.4<br />
10 0<br />
0.2<br />
0.1<br />
0<br />
10 1<br />
10 2<br />
10 1<br />
10 2<br />
# labeled instances (log scale)<br />
EM<br />
NB<br />
VDP<br />
10 3<br />
10 3
Session TuBT9 Continental Parlor 9 Tuesday, September 27, <strong>2011</strong>, 10:00–11:30<br />
Perceptual Learning<br />
Chair Danica Kragic, KTH<br />
Co-Chair Timothy Bretl, Univ. of Illinois at Urbana-Champaign<br />
10:50–10:55 TuBT9.5<br />
Autonomous Acquisition of Multimodal Information<br />
for Online Object Concept Formation by Robots<br />
Takaya Araki, Tomoaki Nakamura and Takayuki Nagai<br />
Dept. of Elec. Eng., The Univ. of Electro-Communications, Japan<br />
Kotaro Funakoshi and Mikio Nakano<br />
Honda Research Institute Japan Co., Ltd., Japan<br />
Naoto Iwahashi<br />
NICT Knowledge Creating Communication Research Center, Japan<br />
• A novel framework for acquisition of<br />
multimodal information and concept<br />
formation by robots<br />
• The robot autonomously acquires haptic,<br />
audio and visual information by grasping,<br />
shaking and observing the target object<br />
• The algorithm of multimodal categorization<br />
using Gibbs sampling is extended to an<br />
online version<br />
Online Multimodal LDA<br />
• This framework enables inference,<br />
classification, and the model update<br />
simultaneously<br />
Autonomous<br />
Information<br />
Acquisition<br />
Multimodal object concepts<br />
Category 1<br />
Words<br />
Plushie, Soft,<br />
Animal<br />
Category 2<br />
Words<br />
Cup, Hard,<br />
Handle<br />
Category 3<br />
Words<br />
Plastic bottle,<br />
Hard, Liquid<br />
11:00–11:15 TuBT9.7<br />
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10:55–11:00 TuBT9.6<br />
Learning Robot Grasping from 3-D Images with<br />
Markov Random Fields<br />
Abdeslam Boularias, Oliver Kroemer, Jan Peters<br />
Max-Planck Institute for Intelligent Systems, Tuebingen, Germany<br />
• Use a depth camera<br />
(Kinect,Swissranger, .. ) to create a 3D<br />
image of an object (a point cloud).<br />
• Transform the point cloud to a knearest<br />
neighbor graph.<br />
• The graph defines a Markov Random<br />
Field, where the vertices are labeled as<br />
“good” or “bad” grasping points.<br />
• The parameters of the Network are<br />
learned by using grasping examples<br />
provided by a human.<br />
• A new object is grasped by classifying<br />
its 3-D points, sampling a good<br />
grasping point, and using a heuristic for<br />
the orientation and the direction of the<br />
hand.<br />
11:15–11:30 TuBT9.8<br />
Inverse Reinforcement Learning<br />
using Path Integrals<br />
Navid Aghasadeghi and Timothy W. Bretl<br />
University of Illinois at Urbana-Champaign, USA<br />
• Problem: Given an optimal trajectory<br />
generated by a continuous-time system,<br />
find a weights of parameterized cost<br />
function that would explain it<br />
• Use the Path Integral formulation to find<br />
probability over trajectories,<br />
parameterized � by<br />
P (� m | �,� ) �<br />
• Solution involves two parts:<br />
• Maximum likelihood to find the weights of the<br />
parameterized cost function<br />
• Iteratively update the set of sampled<br />
trajectories, by solving a forward optimal<br />
control with respect to the current cost estimate<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–74–<br />
K<br />
�<br />
1 *T<br />
� � � (� m )<br />
� e<br />
k �1<br />
e<br />
1 *T<br />
� � � (� k )<br />
�<br />
True cost and optimal trajectory<br />
Estimate cost and trajectory
Session TuCT1 Continental Parlor 1 Tuesday, September 27, <strong>2011</strong>, 14:00–15:30<br />
Object Detection & Collision Avoidance<br />
Chair Paolo Zani, Univ. of Parma, Italy<br />
Co-Chair Jyh-Ming Lien, George Mason Univ.<br />
14:00–14:15 TuCT1.1<br />
Real-time Bézier Trajectory Deformation for<br />
Potential Fields Planning Methods<br />
L.Hilario * , N. Montés * , M.C.Mora ‡ , A.Falcó*<br />
* Physics, Mathematics and Computation Sciences Dept.,Cardenal Herrera<br />
University CEU, Spain<br />
‡ Mechanical Engineering and Construction Dept., Jaume I Universtity, Spain<br />
• A novel technique for obtaining trajectories<br />
based on the deformation of Bézier curves<br />
(BTD) is presented.<br />
• The deformation is computed through<br />
vectors. These are obtained with any<br />
Artificial Potential field methods.<br />
• In the present paper, the BTD has been<br />
combined with the Potential Field<br />
Projection method (PFP).<br />
• Next image shows a PFP-BTD example in<br />
an environment with 5 mobile obstacles.<br />
14:30–14:45 TuCT1.3<br />
Positive and Negative Obstacle Detection<br />
using the HLD Classifier<br />
Ryan D. Morton and Edwin Olson<br />
Computer Science & Engineering, University of Michigan, USA<br />
• New Terrain Classifier<br />
• Detects both positive and negative<br />
obstacles<br />
• Handles partial observability<br />
• Gives designers ability to manage<br />
classification risk<br />
• Subsumes and outperforms previous<br />
methods<br />
14:15–14:30 TuCT1.2<br />
Fast and Robust 2D Minkowski Sum<br />
Using Reduced Convolution<br />
Evan Behar and Jyh-Ming Lien<br />
Department of Computer Science, George Mason University, USA<br />
� M-sum is used for many tasks, such as<br />
C-space mapping and motion planning<br />
� Convolution of P, Q is sum of P's edges<br />
with Q's vertices, and vice versa<br />
� Prevailing convolution-based M-sum<br />
method computes the arrangement of full<br />
convolution, and then throws most of it<br />
away<br />
� Our method uses a reduced<br />
convolution and fast filters to avoid this<br />
wasted computation<br />
� It is shown experimentally to be faster<br />
than the full convolution method used by<br />
CGAL<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–75–<br />
An example reduced convolution<br />
generated from a neuron with<br />
holes and a circle<br />
14:45–14:50 TuCT1.4<br />
Real-time Swept Volume and Distance<br />
Computation for Self Collision Detection<br />
Holger Täubig<br />
Safe & Secure Cognitive Systems, DFKI, Germany<br />
Berthold Bäuml<br />
DLR Institute of Robotics and Mechatronics, Germany<br />
Udo Frese<br />
Safe & Secure Cognitive Systems, DFKI, Germany<br />
• Continuous collision detection algorithm<br />
for industrial and humanoid robots<br />
• Computes swept volumes of all bodies<br />
and checks them pairwise for collisions –<br />
real-time (0.4ms)<br />
• Input: joint angle intervals – cover whole<br />
movement<br />
• Volume representation: sphere swept<br />
convex hulls – tight, efficient, numerically<br />
stable<br />
• Kinematic tree: operation set models<br />
different joints – allows for a trade-off<br />
between accuracy and computation time
Session TuCT1 Continental Parlor 1 Tuesday, September 27, <strong>2011</strong>, 14:00–15:30<br />
Object Detection & Collision Avoidance<br />
Chair Paolo Zani, Univ. of Parma, Italy<br />
Co-Chair Jyh-Ming Lien, George Mason Univ.<br />
14:50–14:55 TuCT1.5<br />
Visual Navigation With Obstacle Avoidance<br />
Andrea Cherubini and François Chaumette<br />
INRIA Rennes – Bretagne Atlantique<br />
• Visual navigation with<br />
obstacle avoidance is<br />
achieved<br />
• Obstacles are detected by a<br />
range scanner and modeled<br />
with potential fields<br />
• Collision avoidance and<br />
visual navigation are<br />
achieved concurrently by<br />
actuating the camera pan<br />
angle<br />
• The approach is validated in<br />
a series of real outdoor<br />
experiments<br />
15:00–15:15 TuCT1.7<br />
Clustering Obstacle Predictions to Improve<br />
Contingency Planning for Autonomous Road<br />
Vehicles in Congested Environments<br />
Jason Hardy and Mark Campbell<br />
Department of Mechanical and Aerospace Engineering,<br />
Cornell University, United States<br />
• Hierarchical clustering algorithm<br />
for improving the computational<br />
scaling of contingency planning<br />
• Contingency planning correctly<br />
handles mutually exclusive<br />
obstacle trajectory predictions<br />
• Increase the utility of the<br />
available contingency plans by<br />
maximizing dissimilarity between<br />
obstacle prediction clusters<br />
• Goal: capture the most important<br />
mutually exclusive obstacle<br />
decisions and uncertainties with a<br />
limited set of contingency clusters<br />
Application: autonomous road<br />
vehicles in congested environments<br />
14:55–15:00 TuCT1.6<br />
Stereo obstacle detection in challenging<br />
environments: the VIAC experience<br />
Alberto Broggi and Michele Buzzoni<br />
and Mirko Felisa and Paolo Zani<br />
VisLab, University of Parma, Italy<br />
• Stereo-based 3D world mapping using a<br />
parallel SGM approach<br />
• Obstacles points are clustered according<br />
to a spatial compatibility criterion<br />
• Very general approach, working reliably in<br />
a wide variety of scenarios<br />
• Tested on an intercontinental route from<br />
Parma, Italy, to Shanghai, China.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–76–<br />
(a)<br />
(b)<br />
(a) The test vehicle, and (b) the<br />
obstacle detection algorithm<br />
output: color encodes disparity<br />
15:15–15:30 TuCT1.8<br />
Time Parametrization of Prioritized Inverse<br />
Kinematics Based on Terminal Attractors<br />
Gerardo Jarquín, Gustavo Arechavaleta and Vicente Parra-Vega<br />
Robotics and Advanced Manufacturing Group, CINVESTAV-IPN, México<br />
• Terminal attractors are incorporated<br />
to the task-priority redundancy<br />
formalism.<br />
• Each prioritized reaching task is<br />
achieved according to a desired time.<br />
• The strategy guarantees smooth<br />
transitions between active and<br />
inactive tasks.
Session TuCT2 Continental Parlor 2 Tuesday, September 27, <strong>2011</strong>, 14:00–15:30<br />
Motion Estimation, Mapping & SLAM<br />
Chair Ryan Eustice, Univ. of Michigan<br />
Co-Chair Davide Scaramuzza, Univ. of Pennsylvania<br />
14:00–14:15 TuCT2.1<br />
Stereo Depth Map Fusion<br />
for Robot Navigation<br />
Christian Häne 1 , Christopher Zach 1 , Jongwoo Lim 2 ,<br />
Ananth Ranganathan 2 and Marc Pollefeys 1<br />
Department of Computer Science, ETH Zürich, Switzerland 1<br />
Honda Research Institute, Mountain View, CA, USA 2<br />
• Fusion of range data from stereo image<br />
pairs to a representation suitable for robot<br />
navigation<br />
• Reconstruction restricted to a two level<br />
height map representation with a floor and<br />
a ceiling for efficient processing<br />
• Regularization of height levels with a<br />
convex total variation energy functional to<br />
get globally optimal solutions<br />
Top: Left image from one input stereo pair<br />
Middle: Raw depth map from stereo matching<br />
Bottom: Final regularized two level reconstruction<br />
14:30–14:45 TuCT2.3<br />
Building Facade Detection, Segmentation, and<br />
Parameter Estimation for Mobile Robot<br />
Localization and Guidance<br />
Jeffrey A. Delmerico and Jason J. Corso<br />
Dept. of Computer Science and Engineering, SUNY at Buffalo, USA<br />
Philip David<br />
US Army Research Laboratory, USA<br />
• Method for segmenting and modeling building facades in stereo imagery.<br />
• Proposed application in mobile robotics is localization in urban<br />
environments by registration of façade orientations to aerial images.<br />
• 85% accuracy for segmentation into individual facades; 10� accuracy for<br />
modeling planar façade orientations.<br />
• New benchmark dataset – human-annotated stereo images of buildings.<br />
14:15–14:30 TuCT2.2<br />
An Embedded Stereo Vision Module<br />
for 6D Pose Estimation and Mapping<br />
Giacomo Spampinato, Jörgen Lidholm, Carl Ahlberg,<br />
Fredrik Ekstrand, Michael Ekström and Lars Asplund<br />
School of Innovation Design and Engineering,<br />
Mälardalen University, Sweden<br />
• The paper presents an embedded stereo<br />
vision system based on FPGA to perform<br />
6D vSLAM<br />
• The proposed architecture is both energy<br />
and cost efficient with high versatility<br />
• A real-time sparse visual feature detector<br />
is used as the only source of information<br />
• Experiments on small and large scale in<br />
non flat scenarios are presented<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–77–<br />
Large scale visual SLAM in<br />
industrial scenario<br />
14:45–14:50 TuCT2.4<br />
A Strategy for Efficient Observation Pruning in<br />
Multi-Objective 3D SLAM<br />
Jaime Valls Miro, Weizhen Zhou and Gamini Dissanayake<br />
Centre of Excellence for Autonomous Systems,<br />
Faculty of Engineering and IT,<br />
University of Technology Sydney (UTS),<br />
Australia<br />
• Efficient solution to feature-based 3D<br />
SLAM when competing objectives<br />
operate simultaneously<br />
• Formulation automatically exploits<br />
observations to best fulfil differing<br />
objectives: localisation, mapping,<br />
exploration, feature distribution, victim<br />
search, etc.<br />
• Tested in Search and Rescue scenario:<br />
21% of observations processed in 1/3 of<br />
time required for full SLAM, with little<br />
degradation in map size and quality<br />
2D projection of spatial<br />
coverage/map expansion (in a<br />
USAR environment), one of the<br />
competing objectives assessed<br />
by the proposed dynamic SLAM<br />
strategy
Session TuCT2 Continental Parlor 2 Tuesday, September 27, <strong>2011</strong>, 14:00–15:30<br />
Motion Estimation, Mapping & SLAM<br />
Chair Ryan Eustice, Univ. of Michigan<br />
Co-Chair Davide Scaramuzza, Univ. of Pennsylvania<br />
14:50–14:55 TuCT2.5<br />
Combined Visually and Geometrically<br />
Informative Link Hypothesis for Pose-graph<br />
Visual SLAM using Bag-of-Words<br />
Ayoung Kim* and Ryan Eustice †<br />
*Department of Mechanical Engineering<br />
† Department of Naval Architecture & Marine Engineering<br />
University of Michigan, USA<br />
• Expected information gain combined with<br />
visual saliency score is reported.<br />
• Geometrically and visually informative<br />
loop-closure candidates are selected.<br />
• Two different bag-of-words saliency<br />
metrics are introduced – global saliency<br />
and local saliency.<br />
• Global saliency measures the rarity.<br />
• Local saliency describes texture richness<br />
and usefulness for camera measurement.<br />
• Results with both indoor and underwater<br />
imagery are presented.<br />
15:00–15:15 TuCT2.7<br />
3D Surveillance Coverage Using Maps Extracted<br />
by a Monocular SLAM Algorithm<br />
Lefteris Doitsidis Alessandro Stephan Weiss<br />
Department of<br />
Electronics, TEI Crete &<br />
CERTH/ITI, Greece<br />
Renzaglia<br />
INRIA,France<br />
ETHZ, Switzerland<br />
Elias Kosmatopoulos Davide Scaramuzza Roland Siegwart<br />
Dept. of ECE, DUTH & University of<br />
ETHZ, Switzerland<br />
CERTH/ITI, Greece Pennsylvania, Greece<br />
• Surveillance Coverage over a terrain of<br />
arbitrary morphology.<br />
• Two-step centralized procedure for<br />
optimal alignment of the robot team.<br />
• A single robot constructs the map using a<br />
novel monocular-vision-based approach.<br />
• The optimal arrangement of the robot<br />
team is produced using a Cognitive based<br />
optimization approach and maximizes the<br />
monitored area.<br />
14:55–15:00 TuCT2.6<br />
RS-SLAM: RANSAC Sampling for Visual SLAM<br />
Gim Hee Lee, Friedrich Fraundorfer, and Marc Pollefeys<br />
Computer Vision and Geometry Laboratory, Department of Computer Science,<br />
ETH Zürich, Switzerland<br />
• RS-SLAM is a monocular FastSLAM<br />
framework that uses hypotheses from<br />
5-point RANSAC and image feature<br />
uncertainties as proposal distribution<br />
• Our proposal distribution is more<br />
robust than the commonly used<br />
constant velocity model which could<br />
be easily violated<br />
• We also showed results from the<br />
extension of our RS-SLAM for stereo<br />
camera<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–78–<br />
(Top) Particles trajectories during loop closure<br />
(Bottom) 3D map generated from the particle<br />
with the highest weight<br />
15:15–15:30 TuCT2.8<br />
Adaptive Sampling Using Mobile Robotic<br />
Sensors<br />
Shuo Huang and Jindong Tan<br />
Electrical & Computering Engineering<br />
Michigan Technological University, USA<br />
• An adaptive sparse sampling approach<br />
based on mobile robotic sensors<br />
• Unknown sensing fields modeled as<br />
sparse signals under Haar Wavelet<br />
domain<br />
• Measurements determined and collected<br />
considering possible distribution of target<br />
sparse signals<br />
• Sensing information maximized with<br />
number of measurements substantially<br />
reduced<br />
Great Lakes surface temperature<br />
sensing and reconstruction
Session TuCT3 Continental Parlor 3 Tuesday, September 27, <strong>2011</strong>, 14:00–15:30<br />
Symposium: Microrobotics III<br />
Chair Sylvain Martel, Ec. Pol. de Montreal (EPM)<br />
Co-Chair David Cappelleri, Stevens Inst. of Tech.<br />
14:00–14:15 TuCT3.1*<br />
Semi-Plenary Invited Talk: Artificial, Biomimetic and<br />
Biological Microrobotics<br />
Metin Sitti, Carnegie Mellon University<br />
14:30–14:45 TuCT3.3<br />
Chemotactic Behavior and Dynamics of<br />
Bacteria Propelled Microbeads<br />
Dongwook Kim, Albert Liu, and Metin Sitti<br />
Carnegie Mellon University, USA<br />
• Proposed chemotaxis based steering<br />
control of swimming micro-robotic<br />
bodies propelled by attached<br />
flagellated S. marcescens bacteria<br />
• Proposed a method to create<br />
chemoattactant gradient in enclosed<br />
fluidic chambers using a mixture<br />
consisting of agar and<br />
chemoattractants<br />
• Demonstrated that chemical<br />
attractant directs the stochastic<br />
motion of bacteria propelled beads,<br />
which is promising as a steering<br />
control method<br />
Bead trajectories when there is<br />
no chemical attractant in the medium<br />
Bead trajectories when there is<br />
chemical gradient in the south direction<br />
14:15–14:30 TuCT3.2<br />
Semi-Plenary Invited Talk: Artificial, Biomimetic and<br />
Biological Microrobotics<br />
Metin Sitti, Carnegie Mellon University<br />
14:45–14:50 TuCT3.4<br />
First Leaps Toward Jumping Microrobots<br />
Wayne A. Churaman 1,3 , Aaron P. Gerratt 1 , and<br />
Sarah Bergbreiter 1,2<br />
1 Mechanical Engineering, University of Maryland, College Park, USA<br />
2 Institute for Systems Research, University of Maryland, College Park, USA<br />
3 Army Research Lab, USA<br />
• Two jumping microrobots demonstrated<br />
using both stored mechanical energy and<br />
stored chemical energy to provide thrust<br />
• Robot using stored mechanical energy is<br />
8 mg, 4 x 4 x 0.3 mm 3 and has<br />
demonstrated jump heights of 32 cm<br />
when loaded and released with tweezers<br />
• Robot using stored chemical energy is<br />
300 mg and 4 x 7 x 4 mm 3 but also<br />
includes power, sensing, and control on<br />
board in addition to chemical actuators –<br />
autonomous jumps 8 cm high<br />
demonstrated in response to light stimulus<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–79–<br />
Two jumping microrobots using<br />
(a) stored mechanical energy and<br />
(b) stored chemical energy for<br />
jump
Session TuCT3 Continental Parlor 3 Tuesday, September 27, <strong>2011</strong>, 14:00–15:30<br />
Symposium: Microrobotics III<br />
Chair Sylvain Martel, Ec. Pol. de Montreal (EPM)<br />
Co-Chair David Cappelleri, Stevens Inst. of Tech.<br />
14:50–14:55 TuCT3.5<br />
Micro-Scale Propulsion using<br />
Multiple Flexible Artificial Flagella<br />
John Singleton, Eric Diller, Tim Andersen, Metin Sitti<br />
Mechanical Engineering, Carnegie Mellon University, USA<br />
Stéphane Regnier<br />
Institute of Intelligent Systems and Robotics<br />
Univ. Pierre et Marie Curie, France<br />
• Multiple artificial flagella are used<br />
to increase propulsion of microscale<br />
swimmers<br />
• For simplicity of actuation and<br />
fabrication, all flagella rotate<br />
about common axis<br />
• Stiff and flexible helices and rods<br />
are tested and analyzed,<br />
suggesting increase in thrust<br />
over single flagella<br />
Concept sketch of the proposed<br />
micro-scale propulsion system<br />
with multiple flexible flagella<br />
protruding downwards from the<br />
perimeter of the cylindrical body<br />
15:00–15:15 TuCT3.7<br />
Precision Evaluation of Modular Multiscale<br />
Robots for Peg-in-Hole Microassembly Tasks<br />
Aditya N. Das and Dan O. Popa<br />
The University of Texas at Arlington, USA<br />
• Peg-in-hole microassembly is a generic<br />
problem, with yield dependent on both<br />
part tolerance and robot precision<br />
• We present simulation results evaluating<br />
the precision of several modular robotic<br />
configurations<br />
• Simulations are based on explicit<br />
uncertainty models and Monte-Carlo runs<br />
• Results confirm intuition that most precise<br />
robot design include rotation DOFs at the<br />
end of two manipulator chains<br />
Rotational<br />
stages<br />
Angle adapter<br />
(c)<br />
Translational stages<br />
Stages<br />
End-effector<br />
Final pose<br />
Initial pose<br />
Evaluation of peg-in-hole<br />
microassembly yield<br />
with modular robotic stages<br />
14:55–15:00 TuCT3.6<br />
Manipulation of<br />
Multiple Bacteria-driven Microobjects<br />
Based on Bacterial Autonomous Movement<br />
Kousuke Nogawa 1 , Masaru Kojima 1 , Masahiro Nakajima 1 ,<br />
Michio Homma 2 , Fumihito Arai and Toshio Fukuda 1<br />
1 Institute for Advanced Research, Nagoya University, Japan<br />
2 Department of Micro-Nano Systems Engineering, Nagoya University, Japan<br />
3 Center For Micro-nano Mechatronics, Nagoya University, Japan<br />
4 Division of Biological Science, Nagoya University, Japan<br />
• We propose a method of<br />
smart manipulation of<br />
multiple bacteria-driven<br />
microobjects with single<br />
manipulated guiderobot<br />
based on bacterial<br />
autonomous movement,<br />
named “SMARTBOT”.<br />
• Fundamental capability of<br />
SMARTBOT is verified by<br />
using the micro/nano pipettes<br />
instead of the guiderobot.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–80–<br />
Signal<br />
Target<br />
source<br />
Global control methods<br />
Blood<br />
vessels<br />
Microrobots<br />
Bacteria-driven<br />
microrobots<br />
Fully manipulated multi-argents Fully autonomous by bacterial function<br />
Bacteria-driven<br />
microrobots<br />
Guide robot<br />
Smart manipulation of multiple agent with single manipulated guide robot<br />
Autonomous mass manipulation of<br />
bacteria- driven microobjects with<br />
single manipulated guide robot<br />
15:15–15:30 TuCT3.8<br />
Multipoint Sliding Probe Methods for In situ<br />
Electrical Transport Property Characterization of<br />
Individual Nanostructures<br />
Zheng Fan 1 , Xinyong Tao 2 , Xiaodong Li 3 , and Lixin Dong 1<br />
1 Dept. of Elec. & Comput. Engr., Michigan State University, USA<br />
2 College of Chem. Engr. & Mater. Science, Zhejiang Univ. of Tech., China<br />
3 Dept. of Mech. Engr., University of South Carolina, USA<br />
• Sliding probe methods are<br />
designed for the in situ electrical<br />
characterization of individual 1D<br />
nanostructures.<br />
• Contact force is controlled with a<br />
Cu-nanowire-tipped flexible probe.<br />
• Specimen-shape adapting probe is<br />
developed for keeping a constant<br />
contact area.<br />
• The technique has a higher<br />
resolution and simplicity in setup<br />
as compared with conventional<br />
two- and four-terminal methods,<br />
respectively.<br />
Sliding Probe Methods and TEM<br />
Images of Measurements
Session TuCT4 Continental Ballroom 4 Tuesday, September 27, <strong>2011</strong>, 14:00–15:30<br />
Forum: Robotics: Beyond the Horizon<br />
Chair Hirochika Inoue, AIST<br />
Co-Chair<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–81–
Session TuCT5 Continental Ballroom 5 Tuesday, September 27, <strong>2011</strong>, 14:00–15:30<br />
Symposium: Medical Robotics III<br />
Chair Jaydev P. Desai, Univ. of Maryland<br />
Co-Chair Paolo Fiorini, Univ. of Verona<br />
14:00–14:15 TuCT5.1<br />
Spatial and Temporal Movement Characteristics<br />
after Robotic Training of Arm and Hand:<br />
A Case Study of a Person with<br />
Incomplete Spinal Cord Injury<br />
D.P. Eng, Dept of MEMS, Rice University, USA<br />
Z. Kadivar, Dept of PM&R, Baylor College of Medicine,USA<br />
J.L. Sullivan, A.U. Pehlivan, M.K. O’Malley<br />
Dept of MEMS, Rice University, USA<br />
N. Yozbatiran and G.E. Francisco, Dept of PM&R, UTHSC, USA<br />
• Application of RiceWrist robotic device for<br />
upper-limbs of a patient with spinal cord<br />
injury<br />
• Spatial and temporal movement aspects<br />
were determined before and after the<br />
training<br />
• Greater improvements were observed for<br />
temporal aspects of forearm and wrist<br />
movements<br />
14:30–14:45 TuCT5.3<br />
Simulating Prosthetic Devices with<br />
Human-Inspired Hybrid Control<br />
Ryan W. Sinnet, Huihua Zhao, and Aaron D. Ames<br />
Department of Mechanical Engineering, Texas A&M University, USA<br />
• A human walking experiment provides<br />
data from test subjects. Data from four<br />
of the subjects are considered to<br />
strengthen the result.<br />
• Human functions are designed which<br />
represent the most fundamental<br />
behaviors underlying human walking.<br />
• A simulation environment is described<br />
which attempts to reproduce human<br />
walking using human functions on a<br />
model with humanlike measurements.<br />
• A transfemoral prosthesis is substituted<br />
for one of the legs and simulation<br />
results in stable walking; see figure.<br />
Hybrid model of prosthesis (top),<br />
healthy gait with human-inspired<br />
control (middle), and prosthesis gait<br />
(bottom)<br />
14:15–14:30 TuCT5.2<br />
Robotic Rehabilitation System Using Human<br />
Hand Trajectory Generation Model<br />
in Virtual Curling Task<br />
Yoshiyuki Tanaka, Toru Sanemasa, Toshio Tsuji<br />
Graduate School of Engineering, Hiroshima University, Japan<br />
Nobuaki Imamura<br />
Department of Engineering, Hiroshima Kokusai University, Japan<br />
• This paper discusses the training<br />
methodology using hand trajectory<br />
generation model in virtual curling.<br />
• The hand motion by well-trained subject is<br />
expressed within the framework of the<br />
minimum-jerk model.<br />
• The model is utilized for teaching and<br />
assisting trainee's motion according to<br />
individual motor abilities.<br />
• Effectiveness of the proposed training<br />
approach was validated through<br />
preliminary training experiments with the<br />
beginners.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–82–<br />
Velocity, vy [m/s]<br />
0.45<br />
0.3<br />
0.15<br />
0<br />
House<br />
Release Release<br />
line<br />
Stone Stone<br />
Distance, l [m]<br />
Reference motion<br />
A minimum-jerk model<br />
with task-related constraints<br />
-0.15<br />
4.0<br />
3.5<br />
1.0<br />
3.0<br />
0<br />
-1.0<br />
2.5<br />
-2.0 [s]<br />
2.0<br />
-3.0 Time<br />
14:45–14:50 TuCT5.4<br />
Dual Predictive Control of Electrically Stimulated<br />
Muscle using Biofeedback for Drop Foot Correction<br />
Mitsuhiro Hayashibe, Qin Zhang, Christine Azevedo-Coste<br />
DEMAR INRIA Sophia Antipolis, and LIRMM, France<br />
• Development of an ES control strategy for<br />
drop foot correction with torque tracking<br />
performance.<br />
• ES-evoked EMG signal and joint torque<br />
were recorded for the model identification<br />
with Kalman filter with forgetting factor.<br />
• A dual predictive controller was proposed<br />
for explicit consideration of muscle activity.<br />
• The method was verified by generating<br />
control signal adaptively according to the<br />
current muscle condition.
Session TuCT5 Continental Ballroom 5 Tuesday, September 27, <strong>2011</strong>, 14:00–15:30<br />
Symposium: Medical Robotics III<br />
Chair Jaydev P. Desai, Univ. of Maryland<br />
Co-Chair Paolo Fiorini, Univ. of Verona<br />
14:50–14:55 TuCT5.5<br />
Gait Support for Complete Spinal Cord Injury<br />
Patient by Synchronized Leg-Swing with HAL<br />
A. Tsukahara, Y. Hasegawa and Y. Sankai<br />
Center for Cybenics Research, University of Tsukuba, Japan<br />
• This paper proposes an estimation<br />
algorithm that infers the intention related<br />
to the forward leg-swing<br />
- Algorithm: Gait-intention estimator<br />
• The system including the algorithm infers<br />
the intention in synchronization with the<br />
center of ground reaction force<br />
- System: Hybrid Assistive Limb (HAL)<br />
• The effectiveness of the proposed<br />
algorithm was verified through a clinical<br />
trial<br />
- Clinical trial: 10m walking test on a treadmill<br />
Complete spinal cord injury<br />
patient wearing HAL<br />
15:00–15:15 TuCT5.7<br />
Knee Joint Movement Assistance Through<br />
Robust Control of an Actuated Orthosis<br />
Saber Mefoued, Samer Mohammed, and Yacine Amirat<br />
University of Paris Est Creteil - (UPEC)<br />
LISSI Laboratory, 94400<br />
Vitry Sur Seine, France<br />
• Design of an actuated orthosis intended to<br />
restore lower limb movements of dependent<br />
persons.<br />
• Dynamic modeling and parametric<br />
identification of the biomechanical – orthosis<br />
model.<br />
• Experimental implementation of a High<br />
Order Sliding Mode Controller (HOSMC).<br />
• Evaluation of controllers’ performances in<br />
terms of tracking errors and robustness<br />
against external perturbations.<br />
Actuated knee joint orthosis<br />
14:55–15:00 TuCT5.6<br />
Subject-based Motion Generation Model<br />
with Adjustable Walking Pattern<br />
for a Gait Robotic Trainer: NaTUre-gaits<br />
Ping Wang and K. H. Low<br />
School of Mechanical and Aerospace Engineering<br />
Nanyang Technological University (NTU), Singapore<br />
A. H. McGregor<br />
Charing Cross Hospital, Imperial College London, UK<br />
• The exoskeleton modules are mounted on<br />
the mobile platform and attached to the<br />
lower limbs and pelvis in parallel<br />
• The synchronized motion generation for<br />
the exoskeleton modules is provided by<br />
virtue of the inverse kinematic model<br />
analysis<br />
• The pelvic trajectory is predefined and ten<br />
points are specified within one gait cycle<br />
to obtain the foot trajectory from the<br />
designated step length and height<br />
• The trajectory is obtained via curve fitting<br />
based on these specified points<br />
• On the other hand, in order to keep the<br />
desired foot trajectory, the pelvic motion is<br />
compensated to accommodate hip and<br />
knee joint angles<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–83–<br />
Figure caption is optional,<br />
Ten points definition as seen<br />
from fixed external observer<br />
15:15–15:30 TuCT5.8<br />
Stairs-Ascending/Descending Assist for a Lower-<br />
Limb Power-Assist Robot Considering ZMP<br />
Yoshiaki Hayashi and Kazuo Kiguchi<br />
Dept. of Advanced Technology Fusion, Saga University, Japan<br />
• The perception-assist for a lower-limb power-assist robot<br />
has been studied.<br />
• In this paper, the stairs-ascending/descending assist is discussed<br />
to prevent the user from falling down.<br />
• In the proposed method, the perception-assist is carried out<br />
considering ZMP.<br />
• The effectiveness of the proposed method has been evaluated<br />
by the performing experiments.<br />
Lower-limb power-assist<br />
exoskeleton robot.
Session TuCT6 Continental Ballroom 6 Tuesday, September 27, <strong>2011</strong>, 14:00–15:30<br />
Symposium: Grasping and Manipulation: Grasp Planning and Quality<br />
Chair Jeff Trinkle, Rensselaer Pol. Inst.<br />
Co-Chair Elon Rimon, Tech. - Israel Inst. of Tech.<br />
14:00–14:15 TuCT6.1*<br />
Semi-Plenary Invited Talk: Sensorless Grasping with<br />
Multiple Contacts and its Computational Modeling<br />
Suguru Arimoto, Ritsumeikan University<br />
14:30–14:45 TuCT6.3<br />
Graspability Map:<br />
A Tool for Evaluating Grasp Capabilities<br />
Maximo A. Roa, Katharina Hertkorn, Franziska Zacharias,<br />
Christoph Borst and Gerd Hirzinger<br />
Institute of Robotics and Mechatronics, German Aerospace Center, Germany<br />
Graspability maps for a coffee cup with the DLR hand II and the DLR-HIT hand II<br />
• Novel representation of the positions and orientations that allow force<br />
closure precision grasps for a given object and mechanical hand<br />
• The maps allow the comparison of grasp capabilities for different<br />
mechanical hands<br />
• Potential applications of the maps include autonomous grasp and<br />
manipulation planning<br />
14:15–14:30 TuCT6.2<br />
The OpenGRASP Benchmarking Suite:<br />
An Environment for the Comparative Analysis of<br />
Grasping and Dexterous Manipulation<br />
Stefan Ulbrich, Daniel Kappler, Tamim Asfour,<br />
Nikolaus Vahrenkamp, Alexander Bierbaum, Markus Przybylski<br />
and Rüdiger Dillmann<br />
Institute for Anthropomatics, Karlsruhe Institute of Technology, Germany<br />
• A new software environment for<br />
comparative evaluation of algorithms for<br />
grasping and dexterous manipulation.<br />
• Allows the reproduction of well-defined<br />
experiments in real-life scenarios in every<br />
laboratory.<br />
• Extendable structure in order to include a<br />
wider range of benchmarks.<br />
• Real-life scenario featuring a humanoid<br />
robot acting in a kitchen with domestic<br />
everyday objects.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–84–<br />
Screenshot of the<br />
real-life scenario.<br />
14:45–14:50 TuCT6.4<br />
Experimental evaluation of Postural Synergies<br />
during Reach to Grasp with the UB Hand IV<br />
Fanny Ficuciello1 , Gianluca Palli2 , Claudio Melchiorri2 and Bruno Siciliano1 1Dipartimento di Informatica e Sistemistica,<br />
Università degli studi di Napoli Federico II, Italy<br />
2Dipartimento di Elettronica, Informatica e Sistemistica,<br />
Università di Bologna, Italy<br />
• The Postural Synergies configuration<br />
subspace given by the fundamental<br />
eigengrasps of the UB Hand IV is derived<br />
through experiments.<br />
• The study is based on the kinematic<br />
structure of the hand and on the taxonomy<br />
of the grasps of common objects.<br />
• Experimental results show that grasp<br />
synthesis for precision or power grasps is<br />
obtained by using only the first two<br />
eigengrasps.<br />
• The tasks are planned for reach and grasp<br />
phases, unique for each object/grasp<br />
combination, by suitable tuning the<br />
eigenpostures weight.<br />
Power and Precise Grasp of<br />
Prismatic and Circular objects<br />
obtained using Postural<br />
Synergies
Session TuCT6 Continental Ballroom 6 Tuesday, September 27, <strong>2011</strong>, 14:00–15:30<br />
Symposium: Grasping and Manipulation: Grasp Planning and Quality<br />
Chair Jeff Trinkle, Rensselaer Pol. Inst.<br />
Co-Chair Elon Rimon, Tech. - Israel Inst. of Tech.<br />
14:50–14:55 TuCT6.5<br />
Planning Grasps for Robotic Hands using a Novel<br />
Object Representation based on the Medial Axis<br />
Transform<br />
Markus Przybylski, Tamim Asfour and Rüdiger Dillmann<br />
KIT – Karlsruhe Institute of Technology, Germany<br />
• We present the novel grid of medial<br />
spheres object representation.<br />
• The grid of medial spheres contains<br />
symmetry properties of an object.<br />
• We present a grasp planning algorithm<br />
that operates on the grid of medial<br />
spheres.<br />
• We show experimental results with two<br />
robot hands on two sets of object models.<br />
15:00–15:15 TuCT6.7<br />
Prioritized Independent Contact Regions for<br />
Form Closure Grasps<br />
Robert Krug, Dimitar Dimitrov, Krzysztof Charusta and Boyko Iliev<br />
AASS Research Center, Oerebro University, Sweden<br />
• Independent Contact Regions (ICR)<br />
provide robustness to grasp uncertainty<br />
• We propose an Algorithm allowing for<br />
user-input regarding the shape of such<br />
regions<br />
• An extension of the Grasp Wrench Space<br />
(GWS) to sets of grasps is contributed<br />
• We introduce the concept of the Exertable<br />
Wrench Space (EWS) to evaluate grasp<br />
families<br />
14:55–15:00 TuCT6.6<br />
Exploiting potential energy storage for cyclic<br />
manipulation: An analysis for elastic dribbling<br />
with an anthropomorphic robot<br />
S. Haddadin, K. Krieger, M. Kunze, A. Albu-Schäffer<br />
Institute of Robotics and Mechatronics (DLR), Germany<br />
• Stable model based ball dribbling by<br />
proprioceptive feedback only, no vision<br />
feedback<br />
• Utilizing intrinsically compliant fingers for<br />
protecting the robot and enlarging contact<br />
time<br />
• Exploiting dynamic elastic energy storage<br />
and release for better peak power<br />
performance and robustness<br />
• Full 6DoF dribbling experiments with a<br />
Cartesian impedance controlled 7DoF<br />
anthropomorphic LWR<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–85–<br />
Elastic dribbling: from modeling to experiment<br />
15:15–15:30 TuCT6.8<br />
Abort and Retry in Grasping<br />
Alberto Rodriguez, Matthew T. Mason, Siddhartha S. Srinivasa<br />
Matthew Bernstein, and Alex Zirbel<br />
The Robotics Institute, Carnegie Mellon University, USA<br />
• Early abort and retry in a bin-picking task<br />
• The system learns a probabilistic model of<br />
the instantaneous likelihood of success of<br />
the grasp<br />
Successful grasp Failed grasp<br />
• Markov chain model of the iterative process<br />
of abort and retry is used to optimize the<br />
expected time to completion of a successful<br />
grasp<br />
• Experiments with a simple hand show<br />
significant increase in time efficiency<br />
Bin picking scenario
Session TuCT7 Continental Parlor 7 Tuesday, September 27, <strong>2011</strong>, 14:00–15:30<br />
Mechanisms: Actuator Design<br />
Chair I-Ming Chen, Nanyang Tech. Univ.<br />
Co-Chair Ravi Balasubramanian, Yale Univ.<br />
14:00–14:15 TuCT7.1<br />
Stiffness Adjustment of a Series Elastic Actuator<br />
in a Knee Prosthesis for Walking and Running:<br />
The Trade-off between Energy and Peak Power Optimization<br />
Martin Grimmer and André Seyfarth<br />
Lauflabor Locomotion Laboratory, University of Jena, Germany<br />
Modelling on human experimental data shows:<br />
• a SEA can highly reduce peak power and<br />
energy for running and high speed walking<br />
• for preferred walking speeds only a energy<br />
reduction is possible<br />
• a benefit with constant stiffness values for<br />
running and walking at different speeds is<br />
possible<br />
Concept of an active knee prosthesis<br />
used to estimate peak power and<br />
energy requirements with a SEA in<br />
comparison to a direct drive<br />
14:30–14:45 TuCT7.3<br />
Performance of Serial Underactuated Mechanisms:<br />
Number of Degrees of Freedom and Actuators<br />
Ravi Balasubramanian and Aaron M. Dollar<br />
Department of Mechanical Engineering, Yale University, USA<br />
• Most underactuated mechanisms utilize<br />
only one actuator to actuate many<br />
degrees of freedom in a serial chain.<br />
• If additional actuators are to be used, what<br />
is the additional benefit that each actuator<br />
provides?<br />
• Metric used in paper: the number of<br />
contacts the mechanism makes with<br />
the environment.<br />
• What is the optimal way to route the<br />
additional actuators across the degrees of<br />
freedom?<br />
A three degree-of-freedom linear<br />
underactuated system a) with one<br />
actuator, and b) with two actuators. c)<br />
A schematic of the mechanism<br />
interacting with the environment.<br />
14:15–14:30 TuCT7.2<br />
Static and Dynamic Characteristics of<br />
McKibben Pneumatic Actuator for Realization<br />
of Stable Robot Motion<br />
Yasuhiro Sugimoto and Koichi Osuka<br />
Dept. of Mechanical Engineering, Osaka University, Japan<br />
Keisuke Naniwa<br />
Dept. of Mechanical Engineering, Kobe University, Japan<br />
• McKibben Pneumatic Actuator (MPA) has<br />
favorable properties on the stability of robot’s<br />
motion<br />
• The dependency of MPA tension-velocity<br />
through dynamic experiments was measured<br />
• It was verified that the static and dynamic<br />
MPA’s properties contribute to stable robot<br />
motions<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–86–<br />
Relation between MPA tension and<br />
contraction velocity at closed valve<br />
14:45–14:50 TuCT7.4<br />
Variable Radius Pulley Design Methodology for<br />
Pneumatic Artificial Muscle-based Antagonistic<br />
Actuation Systems<br />
Dongjun Shin1 , Xiyang Yeh2 , Oussama Khatib1 1<br />
Artificial Intelligence Laboratory, Stanford University, USA<br />
2<br />
Mechanical Engineering , Stanford University, USA<br />
• Analysis of the limitations of<br />
conventional circular pulleys<br />
• A new design methodology for<br />
variable radius pulleys<br />
• Experimental comparisons to<br />
show performance<br />
improvement in position control<br />
in the enlarged workspace.<br />
Variable Radius Pulleys in Pneumatic<br />
Muscle-driven 1-DOF Testbed
Session TuCT7 Continental Parlor 7 Tuesday, September 27, <strong>2011</strong>, 14:00–15:30<br />
Mechanisms: Actuator Design<br />
Chair I-Ming Chen, Nanyang Tech. Univ.<br />
Co-Chair Ravi Balasubramanian, Yale Univ.<br />
14:50–14:55 TuCT7.5<br />
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15:00–15:15 TuCT7.7<br />
Kinematic and Dynamic Analysis of a<br />
Novel 6-DOF Serial Manipulator for<br />
Underground Distribution Power Lines<br />
J.-F. Allan, S. Lavoie, S. Reiher, and G. Lambert<br />
Hydro-Quebec’s Research Institute (IREQ), Canada<br />
• New manipulator with five revolute joints<br />
and one prismatic joint<br />
• Manipulator architecture is intended to<br />
solve space constraint problems in<br />
confined environments<br />
• Presentation of the geometrical model,<br />
analytical solution for the inverse<br />
kinematic equations, and dynamic model<br />
• Simulations performed with MATLAB and<br />
CATIA, manipulator workspace<br />
• The only robot application in the world<br />
designed to perform operation tasks on<br />
equipment in vaults of an underground<br />
power distribution network<br />
Manipulator design in CATIA<br />
14:55–15:00 TuCT7.6<br />
High-Backdrivable Parallel-Link Manipulator<br />
with Continuously Variable Transmission<br />
Kenji Tahara, Shingo Iwasa, Shu Naba and Motoji Yamamoto<br />
Kyushu University, Japan<br />
• A novel parallel-link manipulator<br />
composed of several linear shaft motors is<br />
proposed.<br />
• A continuously variable transmission<br />
(CVT) mechanism is utilized.<br />
• Kinematics and dynamics of the 1 DOF<br />
parallel-link manipulator are modeled<br />
• A control signal to regulate the arm's<br />
angle and the CVT machanism is<br />
designed.<br />
• A static relation between the output force<br />
and the CVT mechanism is analyzed.<br />
• Experiments are conducted using a<br />
prototype.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–87–<br />
Effect of the continuously variable<br />
transmission mechanism<br />
15:15–15:30 TuCT7.8<br />
Design of a Static Balancing Mechanism with<br />
Unit Gravity Compensators<br />
Changhyun Cho<br />
Dept. of Control, Instruments, and Robot, Chosun University<br />
Sungchul Kang<br />
Center for Bionics, KIST<br />
• Design method of a static balancing<br />
mechanism using unit gravity<br />
compensators (e.g., 1-dof gravity<br />
compensator)<br />
• Determination of the number of springs<br />
and their locations by computing the<br />
mapping matrix between the joint space<br />
and gravity compensator space.<br />
• The number of rows of the mapping matrix<br />
indicates the amount of unit gravity<br />
compensators and linear joint constraints<br />
representing locations of unit gravity<br />
compensators<br />
�<br />
Joint<br />
space<br />
J� = �g<br />
J<br />
J<br />
Gravity<br />
Compensator<br />
space<br />
-1 �g<br />
Mapping between the joint space<br />
and gravity compensator space
Session TuCT8 Continental Parlor 8 Tuesday, September 27, <strong>2011</strong>, 14:00–15:30<br />
Reptile & Fish-inspired Robots<br />
Chair Akio Ishiguro, Tohoku Univ.<br />
Co-Chair Fumitoshi Matsuno, Kyoto Univ.<br />
14:00–14:15 TuCT8.1<br />
Learning Fish-like Swimming with a CPG-based<br />
Locomotion Controller<br />
Y. Hu, W. Tian, J. Liang, and T. Wang<br />
Beihang University, China<br />
• Fish-like swimming can be acquired with<br />
an adaptive CPG network<br />
• Teaching signals are derived from fish<br />
kinematics with trajectory approximation<br />
method<br />
• A coupling scheme that eliminates the<br />
influence of afferent signals on amplitude<br />
of the oscillator is proposed<br />
• CPG parameters are converted into<br />
dynamical systems that evolve as part of<br />
the CPG network<br />
14:30–14:45 TuCT8.3<br />
Decentralized Control of Multi-articular<br />
Snake-like Robot for Efficient Locomotion<br />
Takeshi Kano 1 , Takahide Sato 1 ,<br />
Ryo Kobayashi 2,3 , and Akio Ishiguro 1,3<br />
1 Research Institute of Electrical Communication, Tohoku University, Japan<br />
2 Dept. of Mathematical and Life Sciences, Hiroshima University, Japan<br />
3 Japan Science and Technology Agency, CREST, Japan<br />
• We theoretically show that multiarticular<br />
muscles of snakes<br />
enhance locomotion efficiency<br />
• We propose a decentralized control<br />
scheme for efficient locomotion of a<br />
multi-articular snake-like robot on<br />
the basis of the theoretical result<br />
• The validity of the proposed control<br />
scheme is confirmed by simulations<br />
n � 1<br />
n � 2<br />
n � 3<br />
n � 4<br />
n � 5<br />
Direction of motion<br />
14:15–14:30 TuCT8.2<br />
A Self-tuning Multi-phase CPG Enabling the<br />
Snake Robot to Adapt to Environments<br />
Chaoquan Tang 1,2 , Shugen Ma 2 , Bin Li 1 , Yuechao Wang 1<br />
1, State Key Laboratory of Robotics, Automation,<br />
Chinese Academy of Sciences, Shenyang, China<br />
2, Department of Robotics, College of Science and Engineering,<br />
Ritsumeikan University, Shiga-ken, Japan<br />
• A control method imitates the control<br />
strategy of natural snake’s movement in<br />
different environments, which enables<br />
the snake robot to move more quickly<br />
and naturally.<br />
• Through kinematic and dynamic analysis<br />
of snake robots, optimal control<br />
parameters are chosen for the decision<br />
strategy.<br />
• Due to the intrinsic property of the multiphase<br />
CPG, this model can change the<br />
movement patterns and control<br />
parameters autonomously according to<br />
external information.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–88–<br />
The comparison of adaptability<br />
between multi-phase CPG control<br />
method(b) and traditional control<br />
method(a).<br />
14:45–14:50 TuCT8.4<br />
A Snake-like Robot Driven by<br />
a Decentralized Control That Enables<br />
Both Phasic and Tonic Control<br />
Takahide Sato1 , Takeshi Kano1 , and Akio Ishiguro1,2 1Research Institute of Electrical Communication, Tohoku University, Japan<br />
2Science and Technology Agency CREST, Japan<br />
• We have developed two-dimensional<br />
snake-like robot employing a<br />
decentralized control scheme, in which<br />
phasic and tonic control are well<br />
coordinated.<br />
• A well-balanced coupling between the<br />
phasic and tonic control enables the robot<br />
to locomote on the upslope, which it<br />
cannot negotiate with only phasic control.<br />
Real physical serpentine<br />
robot developed
Session TuCT8 Continental Parlor 8 Tuesday, September 27, <strong>2011</strong>, 14:00–15:30<br />
Reptile & Fish-inspired Robots<br />
Chair Akio Ishiguro, Tohoku Univ.<br />
Co-Chair Fumitoshi Matsuno, Kyoto Univ.<br />
14:50–14:55 TuCT8.5<br />
A Lizard-Inspired Active Tail Enables Rapid Maneuvers<br />
and Dynamic Stabilization in a Terrestrial Robot<br />
Evan Chang-Siu , Thomas Libby, Masayoshi Tomizuka<br />
Dept. of Mechanical Engineering, University of California Berkeley, USA<br />
Robert J. Full<br />
Dept. of Integrative Biology, University of California Berkeley, USA<br />
• Bio-inspiration from leaping lizards<br />
• 177g active tailed robot<br />
• Active tail allows redirection of angular<br />
momentum from body to tail<br />
• Active tail enables perturbation mitigation<br />
• Active tail enables steering of the body<br />
angle<br />
• High speed video demonstrating behavior<br />
Angle (deg)<br />
200<br />
100<br />
0<br />
0 0.05 0.1 0.15 0.2<br />
Time (sec)<br />
0.25 0.3 0.35<br />
Bio-Inspiration and Tailbot<br />
Tail Angle<br />
Body Angle<br />
15:00–15:15 TuCT8.7<br />
Multi-physics model of an electric fish-like robot:<br />
numerical aspects and<br />
application to obstacle avoidance<br />
Mathieu Porez*�, Vincent Lebastard�,<br />
Auke Jan Ijspeert* and Frédéric Boyer�<br />
*BioRob, EPFL, Switzerland - �IRCCyN, EMN, France<br />
• Modeling of a fish-like robot equipped with<br />
the electric sense suited to the sensorimotricity<br />
study<br />
• Study of interactions between body<br />
deformations and perception variables<br />
• Design and testing of an electric<br />
extroceptive feedback loop based on a<br />
direct current measurement method<br />
14:55–15:00 TuCT8.6<br />
Moving Right Arm in the Right Place:<br />
Ophiuroid-inspired Omnidirectional Robot<br />
Driven by Coupled Dynamical Systems<br />
Wataru Watanabe, Shota Suzuki and Takeshi Kano<br />
Research Institute of Electrical Communication, Tohoku University, Japan<br />
Akio Ishiguro<br />
Research Institute of Electrical Communication, Tohoku University, Japan,<br />
and Japan Science and Technology Agency CREST, Japan<br />
• Discuss a decentralized control that<br />
exhibits the coordination of<br />
rhythmic and non-rhythmic<br />
movements.<br />
• Focus on the ophiuroid<br />
omnidirectional locomotion as a<br />
simple best example.<br />
• Use an active rotator model that<br />
can describe both oscillatory and<br />
excitatory properties.<br />
• Simulated robot exhibits the<br />
spontaneous role assignment of<br />
versatile arm movements and<br />
generates omnidirectional locomotion.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–89–<br />
Snapshots of the simulated<br />
locomotion of the ophiuroid robot.<br />
15:15–15:30 TuCT8.8<br />
Front-Unit-Following Control of a Snake-like<br />
Robot Using Screw Drive Mechanism Based on<br />
Past Velocity Commands<br />
Ryo Ariizumi, Hiroaki Fukushima and Fumitoshi Matsuno<br />
Dept. of Mechanical Engineering and Science, Graduate School of<br />
Engineering, Kyoto University, Japan<br />
• Present a front-unit-following control<br />
method for a snake-like robot using screw<br />
drive mechanism<br />
• Operators only have to command the first<br />
unit, then the other units automatically<br />
follow it<br />
• Proposed method estimates tracking<br />
errors expressed in Frenet frames using<br />
past velocity commands<br />
• Effectiveness of the method is<br />
demonstrated by computer simulations<br />
and laboratory experiments
Session TuCT9 Continental Parlor 9 Tuesday, September 27, <strong>2011</strong>, 14:00–15:30<br />
Novel Sensors<br />
Chair Dario Floreano, Ec. Pol. Federal, Lausanne<br />
Co-Chair Sanford Meek, Univ. of Utah<br />
14:00–14:15 TuCT9.1<br />
Contactless deflection sensor for soft robots<br />
Michal Karol Dobrzynski, Ramon Pericet-Camara<br />
and Dario Floreano<br />
Laboratory of Intelligent Systems,<br />
Ecole Federale Polytechnique de Lausanne, Switzerland<br />
• Deflection sensor capable of shape<br />
estimation with no impact on<br />
deflected substrate’s softness.<br />
• Prototype devices show a resolution<br />
of 1.29� with 400 Hz data acquisition.<br />
• Average agreement of 5% between<br />
experimental data and simulation.<br />
• Application to soft robotics domain is<br />
discussed.<br />
Aa<br />
B<br />
Ab<br />
Aa: prototype device consist of 12 contactless<br />
deflection sensors; Ab: shape visualization;<br />
B: operational principle for a single contactless<br />
deflection sensor based on light.<br />
14:30–14:45 TuCT9.3<br />
Characterizing the performance of an optical slip<br />
sensor for grip control in a prosthesis<br />
Hamidreza N. Sani and Sanford G. Meek<br />
Mechanical Engineering, University of Utah, USA<br />
• Capable of detecting slip on surfaces with<br />
various surface proprieties such as<br />
roughness, curvature, transparency,<br />
reflectivity, and hardness<br />
• All tests were done with a 0.5 mm clear<br />
plastic to simulate the cosmetic glove<br />
• Accurate displacement detection up to<br />
1mm away<br />
• Only failed at extreme cases of surface<br />
properties<br />
• Minimal to no disturbance from an<br />
external light source even under the sun<br />
. The prototype optical based<br />
slip detection sensor mounted<br />
between the Motion Control<br />
prosthetic hand fingers<br />
14:15–14:30 TuCT9.2<br />
Soft Curvature Sensors for<br />
Joint Angle Proprioception<br />
Rebecca K. Kramer, Carmel Majidi,<br />
Ranjana Sahai and Robert J. Wood<br />
School of Engineering and Applied Sciences, Harvard University, USA<br />
• Elastomer-based curvature<br />
sensors allow mechanically<br />
noninvasive measurement of<br />
human body motion and robot<br />
kinematics.<br />
• Stretch, pressure, and curvature<br />
sensing are combined to<br />
introduce a family of wearable soft<br />
(E ~ 1 MPa) and stretchable<br />
(>350%) sensors that detect<br />
bending and joint position.<br />
• Curvature sensors are composed<br />
of a thin, transparent elastomer<br />
film embedded with a<br />
microchannel of conductive liquid.<br />
• The sensors are demonstrated via<br />
testing on a human finger joint.<br />
14:45–14:50 TuCT9.4<br />
DLR VR-SCAN: A Versatile and Robust<br />
Miniaturized Laser Scanner for short range 3D-<br />
Modelling and Exploration in Robotics<br />
Simon Kielhöfer, Thomas Bahls, Franz Hacker, Tilo Wüsthoff and<br />
Michael Suppa<br />
Institute for Robotics and Mechatronics,<br />
German Aerospace Center (DLR), Germany<br />
• Miniaturized triangulation based laser scanner with a MEMS scan head<br />
• “Eye in hand” sensor attachable to the TCP<br />
• High quality 3D-modelling of objects e.g. for grasp-planning<br />
• Robust exploration of safe-for-motion (SFM) areas<br />
of the robot through confidence rating<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–90–
Session TuCT9 Continental Parlor 9 Tuesday, September 27, <strong>2011</strong>, 14:00–15:30<br />
Novel Sensors<br />
Chair Dario Floreano, Ec. Pol. Federal, Lausanne<br />
Co-Chair Sanford Meek, Univ. of Utah<br />
14:50–14:55 TuCT9.5<br />
Piezoelectric Self-Sensing Technique for<br />
Tweezer Style End-Effector<br />
Timothy McPherson and Jun Ueda<br />
George W. Woodruff School of Mechanical Engineering<br />
Georgia Institute of Technology, USA<br />
• Piezoelectric tweezers<br />
use rhombus strain<br />
amplification to achieve<br />
macro scale displacement<br />
• Self-sensing technique<br />
uses shunt resistance to<br />
measure discharged<br />
current<br />
• Technique is well suited to<br />
static force and<br />
displacement<br />
measurements<br />
• Experimental verification<br />
shows 0.15 mm max error<br />
vs. laser displacement<br />
sensor<br />
Piezoelectric Tweezers<br />
15:00–15:15 TuCT9.7<br />
Development of<br />
Omni-Directional and Fast-Responsive<br />
Net-Structure Proximity Sensor<br />
Kazuki Terada, Yosuke Suzuki, Hiroaki Hasegawa,<br />
Satoshi Sone, Aiguo Ming and Makoto Shimojo<br />
Graduate School of Informatics and Engineering, UEC, JAPAN<br />
Masatoshi Ishikawa<br />
Graduate School of Information Science and Technology, Tokyo Univ., JAPAN<br />
• All photo-transistors on the full surface<br />
are connected by an unique analog<br />
computing network circuit.<br />
• The output signal means the angle and<br />
the distance of a nearby object.<br />
• Response time is less than 1 ms, and<br />
the accuracy of object angle detection<br />
is less than 3 deg.<br />
• The sensor is especially available for<br />
mobile robots to avoid collision with<br />
rapidly approaching objects.<br />
“Ring-Shaped Proximity Sensor”<br />
14:55–15:00 TuCT9.6<br />
Development of a Low-Profile Sensor Using Electro-<br />
Conductive Yarns in Recognition of Slippage<br />
Van Anh Ho*, Daisuke Kondo*, Shima Okada*, Takahiho Araki**,<br />
Emi Fujita**, Masaaki Makikawa*, and Shinichi Hirai<br />
(*)Department of Robotics, Ritsumeikan University , Japan<br />
(**) Research and Development Division, Okamoto Corporation, Japan<br />
Upper row shows a complete template of a fabric slip sensor and construction of piles.<br />
Lower row plots the output of the sensor when suffering a rubbing action of human<br />
fingertip, and application of Discrete Wavelet Transform in slip detection.<br />
15:15–15:30 TuCT9.8<br />
Design of a Compact Camera-Orienting<br />
Mechanism with Flexural Pan and Tilt Axes<br />
Chao-Chieh Lan and Yi-Chiao Lee<br />
Dept. of Mechanical Eng., National Cheng Kung University, Taiwan<br />
Jinn-Feng Jiang, Yi-Jie Chen, and Hung-Yuan Wei<br />
Metal Industries Research and Development Centre, Taiwan<br />
• Design and prototype of a camera-orienting mechanism is proposed.<br />
Pan-tilt motion is achieved by using parallel placed flexural beams to<br />
actuate a spherical 5-bar mechanism.<br />
• Flexible mechanism design can reduce the<br />
number of parts, avoid joint clearance, achieve<br />
linear output-input relation, and fit irregular<br />
design domain.<br />
• Mechanism specifications<br />
� Size: 42�43�116 mm<br />
� Weight: 140 g<br />
� Pan range: �35�<br />
� Tilt range: �33�<br />
� Resolution: 0.2�<br />
� Max. speed: 150�/s<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–91–
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–92–
8:00-9:30<br />
Technical Program Digest<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–93–<br />
Wednesday<br />
September 28, <strong>2011</strong><br />
Session WeAT1 ⎯⎯ HRI: Modeling Human Behavior<br />
Session WeAT2 ⎯⎯ Models and Representation<br />
Session WeAT3 ⎯⎯ Medical Robotics: Tracking & Detection<br />
Session WeAT4 ⎯⎯ Symposium: Haptics Interfaces for the Fingertip, Hand, and Arm<br />
Session WeAT5 ⎯⎯ Symposium: Robot Motion Planning: Achievements and<br />
Emerging Approaches<br />
Session WeAT6 ⎯⎯ Symposium: Aerial Robotics: Estimation, Perception and Control<br />
Session WeAT7 ⎯⎯ Robot Walking<br />
Session WeAT8 ⎯⎯ Networked Robots<br />
Session WeAT9 ⎯⎯ Vision: From Features to Applications<br />
10:00-11:30<br />
Session WeBT1 ⎯⎯ Socially Assistive Robots<br />
Session WeBT2 ⎯⎯ Estimation & Sensor Fusion<br />
Session WeBT3 ⎯⎯ Surgical Robotics<br />
Session WeBT4 ⎯⎯ Symposium: Hardware and Software Design for Haptic Systems<br />
Session WeBT5 ⎯⎯ Symposium: Foundations and Future Prospects of Samplingbased<br />
Motion Planning<br />
Session WeBT6 ⎯⎯ Symposium: Aerial Robotics: Control and Planning<br />
Session WeBT7 ⎯⎯ Passive Walking & Leg-Wheeled Robots<br />
Session WeBT8 ⎯⎯ Multirobot Systems: Rendezvous & Task Switching<br />
Session WeBT9 ⎯⎯ Visual Servoing<br />
(continues on next page)
14:00-15:30<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–94–<br />
Wednesday<br />
September 28, <strong>2011</strong><br />
(continued)<br />
Session WeCT1 ⎯⎯ Human-Robot Interaction and Cooperation<br />
Session WeCT2 ⎯⎯ Pose Estimation & Visual Tracking<br />
Session WeCT3 ⎯⎯ Robot Safety<br />
Session WeCT4 ⎯⎯ Symposium: Haptic Feedback and System Evaluation<br />
Session WeCT5 ⎯⎯ Symposium: Symbolic Approaches to Motion Planning and<br />
Control<br />
Session WeCT6 ⎯⎯ Symposium: Marine Robotics: Platforms and Applications<br />
Session WeCT7 ⎯⎯ Humanoid Control<br />
Session WeCT8 ⎯⎯ Multirobot Planning<br />
Session WeCT9 ⎯⎯ Visual & Multi-Sensor Calibration<br />
16:00-17:30<br />
Session WeDT1 ⎯⎯ Human-Robot Collaboration<br />
Session WeDT2 ⎯⎯ Recognition & Prediction of Motion<br />
Session WeDT3 ⎯⎯ Haptic Rendering & Object Recognition<br />
Session WeDT4 ⎯⎯ Industrial Forum: Medical Robotics<br />
Session WeDT5 ⎯⎯ Symposium: Robot Motion Planning: New Frameworks and High<br />
Performance<br />
Session WeDT6 ⎯⎯ Symposium: Marine Robotics: Control and Planning<br />
Session WeDT7 ⎯⎯ Tracking & Gait Analysis<br />
Session WeDT8 ⎯⎯ Multirobot Coordination & Modular Robots<br />
Session WeDT9 ⎯⎯ Calibration & Identification
Session WeAT1 Continental Parlor 1 Wednesday, September 28, <strong>2011</strong>, 08:00–09:30<br />
HRI: Modeling Human Behavior<br />
Chair Kai Oliver Arras, Univ. of Freiburg<br />
Co-Chair Mario F. Montenegro Campos, Univ. Federal de Minas Gerais<br />
08:00–08:15 WeAT1.1<br />
Please do not disturb! Minimum Interference<br />
Coverage for Social Robots<br />
Gian Diego Tipaldi and Kai O. Arras<br />
Social Robotics Lab, University of Freiburg, Germany<br />
• We address the novel problem<br />
of human-aware coverage<br />
• We learn spatio-temporal<br />
distributions of human activities<br />
with spatial Poisson processes<br />
• We use a time dependent TSP to plan<br />
paths that minimize the interference<br />
probability with humans<br />
• Experiments show that the proposed<br />
coverage planner significantly<br />
reduces interference in terms of<br />
durations and numbers of disturbances<br />
• Applications e.g. for robotic vacuum<br />
cleaners to learn how to avoid busy<br />
places at certain times of the day<br />
08:30–08:45 WeAT1.3<br />
Generalising Human Demonstration Data by Identifying<br />
Affordance Symmetries in Object Interaction Trajectories<br />
Jonathan Claassens<br />
Council of Industrial and Scientific Research (CSIR), South Africa<br />
Yiannis Demiris<br />
Intelligent Systems and Networks, Imperial College London, South Africa<br />
• When humans interact with objects, their<br />
hand trajectories often exhibit symmetries.<br />
• These symmetries may exist along edges<br />
of tables, around a cup or along the<br />
handle of an oven.<br />
• They typically arise from the affordance of<br />
the affected object.<br />
• A means to formally specify such<br />
symmetries and an algorithm to identify<br />
potential symmetries is proposed.<br />
• The performance of the algorithm is<br />
illustrated on data captured from a mocap<br />
system.<br />
A Cylindrical Affordance<br />
Symmetry<br />
08:15–08:30 WeAT1.2<br />
Shall We Dance? A Music-driven Approach for<br />
Mobile Robots Choreography<br />
Samuel de Sousa Jr and Mario Montenegro Campos<br />
Departamento de Ciência da Computação, UFMG, Brazil<br />
• We describe an approach that endows<br />
a robot to autonomously dance based<br />
on music.<br />
•A variability function for estimating the<br />
turbulence of a block of notes is<br />
defined which generates specific<br />
choreographic patterns.<br />
• Three dancing behaviors: phlegmatic,<br />
melancholic, and choleric are defined<br />
and implemented according to the<br />
turbulence of a block of notes.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–95–<br />
Methodology<br />
08:45–08:50 WeAT1.4<br />
Human Preferences for<br />
Robot-Human Hand-over Configurations<br />
Maya Cakmak<br />
School of Interactive Computing, Georgia Institute of Technology, USA<br />
Siddhartha S.Srinivasa, Min Kyung Lee, Jodi Forlizzi, Sara Kiesler<br />
Robotics Institute & Human-Computer Interaction Institute,<br />
Carnegie Mellon University, USA<br />
• Hand-over configuration: Robot and object<br />
configuration at which the object is<br />
transferred from the robot to the human.<br />
• We present a user study that compares<br />
configurations planned using a kinematic<br />
model of the human and configurations<br />
learned from human examples.<br />
• We find that learned configurations are<br />
preferred (found to be more natural and<br />
appropriate), while planned configurations are<br />
more practical and reachable.<br />
• We identify three latent variables of preferred<br />
configurations: arm extension, consistency<br />
and naturalness. These can be used to plan<br />
better hand-over configurations.<br />
Planned (top) and learned (bottom)<br />
hand-over configurations
Session WeAT1 Continental Parlor 1 Wednesday, September 28, <strong>2011</strong>, 08:00–09:30<br />
HRI: Modeling Human Behavior<br />
Chair Kai Oliver Arras, Univ. of Freiburg<br />
Co-Chair Mario F. Montenegro Campos, Univ. Federal de Minas Gerais<br />
08:50–08:55 WeAT1.5<br />
Did you see it hesitate? – Empirically Grounded<br />
Design of Hesitation Trajectories for<br />
Collaborative Robots<br />
AJung Moon, Chris A. C. Parker, Elizabeth A. Croft<br />
H. F. Machiel Van der Loos<br />
Mechanical Engineering, University of British Columbia, Canada<br />
• Humans hesitate when unexpected<br />
conflict of resource occurs.<br />
• Based on survey validated human<br />
hesitation gestures, we developed<br />
characteristic hesitation gesture<br />
trajectories for a robot.<br />
• Our study empirically demonstrates that<br />
the communicative content of human<br />
hesitation gestures can be transferred<br />
onto a robot arm.<br />
• We present the characteristic acceleration<br />
profile of robotically generated hesitation<br />
gestures.<br />
Figure caption is optional,<br />
use Arial Narrow 20pt<br />
09:00–09:15 WeAT1.7<br />
Towards an Understanding of Dancers'<br />
Coupled Body Dynamics for Waltz<br />
Hongbo Wang and Kazuhiro Kosuge<br />
Department of Bioengineering and Robotics, Tohoku University, Japan<br />
• To improve a dance partner robot, two<br />
waltz dancers’ coupled body dynamics are<br />
studied<br />
• Dancers are modeled as linear inverted<br />
pendulums, connected by a spring and a<br />
damper<br />
• Stability and optimal interaction are<br />
analyzed, under assumptions on<br />
synchronized, periodic motions<br />
• Simulations and human-robot experiments<br />
are conducted to validate our approach<br />
08:55–09:00 WeAT1.6<br />
VocaWatcher: Natural Singing Motion Generator<br />
for a Humanoid Robot<br />
Shuuji Kajita*, Tomoyasu Nakano**, Masataka Goto**,<br />
Yosuke Matsusaka*, Shin'ichiro Nakaoka*, and Kazuhito Yokoi*<br />
*Intelligent Systems Research Institute, AIST, Japan<br />
**Information Technology Research Institute, AIST, Japan<br />
• We propose a novel robot motion generator<br />
that enables a humanoid robot to sing with<br />
realistic facial expressions and naturally<br />
synthesized singing voices<br />
• Our generator uses the HRP-4C and<br />
consists of two subsystems: VocaWatcher<br />
for generating singing motions and<br />
VocaListener for synthesizing singing<br />
voices<br />
• VocaWatcher imitates a human singer by<br />
analyzing a video clip of a human singing,<br />
recorded by a single video camera<br />
• VocaListener synthesizes singing voices by<br />
imitating the pitch and dynamics of the The original human singer and<br />
human singing<br />
the robot singer, HRP-4C<br />
Video clips: http://staff.aist.go.jp/t.nakano/VocaWatcher/<br />
09:15–09:30 WeAT1.8<br />
Understanding human interaction<br />
for probabilistic autonomous navigation<br />
using Risk-RRT approach<br />
Jorge Rios-Martinez 1 , Anne Spalanzani 2 , Christian Laugier 1<br />
1 INRIA Rhone-Alpes, Grenoble, France<br />
2 UPMF-Grenoble 2 - INRIA - LIG, France<br />
• Robot navigation systems must be<br />
“aware” of the social conventions<br />
followed by people like respecting<br />
personal space and o-space<br />
• This paper proposes a risk-based<br />
navigation method including both the<br />
traditional notion of risk of collision and<br />
the notion of risk of disturbance<br />
• Results exhibit new emerging behavior<br />
showing how a robot takes into account<br />
social conventions in its navigation<br />
strategy<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–96–
Session WeAT2 Continental Parlor 2 Wednesday, September 28, <strong>2011</strong>, 08:00–09:30<br />
Models and Representation<br />
Chair Lynne Parker, Univ. of Tennessee<br />
Co-Chair Christian Schlegel, Univ. of Applied Sciences Ulm<br />
08:00–08:15 WeAT2.1<br />
Denoising of Range Images<br />
using a Trilateral filter and Belief Propagation<br />
Shuji Oishi, Ryo Kurazume,<br />
Yumi Iwashita, and Tsutomu Hasegawa<br />
Kyushu University, Japan<br />
• Two denoising techniques are<br />
proposed for a 3D laser scanner<br />
• We focus on laser reflectance<br />
obtained as a by-product of range<br />
image<br />
• New trilateral filter is proposed for<br />
suppressing noises in range images<br />
while preserving geometric features<br />
• New inpainting technique using belief<br />
propagation is also proposed to<br />
recover missing regions in range<br />
images<br />
08:30–08:45 WeAT2.3<br />
3D Crowd Surveillance and Analysis using<br />
Laser Range Scanners<br />
Xiaowei Shao, Ryosuke Shibasaki and Yun Shi<br />
Center for Spatial Information Science, University of Tokyo, Japan<br />
Huijing Zhao<br />
State Key Lab of Machine Perception, Peking University, China<br />
Kiyoshi Sakamoto<br />
Research and Development Center, East Japan Railway Company, Japan<br />
• 3D crowd surveillance by using<br />
swinging laser range scanner<br />
• Multiple clients are integrated by semiauto<br />
calibration<br />
• Automatic and efficient people<br />
extraction based on improved meanshift<br />
technique<br />
• Quantified crowdness analysis in ROIs<br />
is performed.<br />
Top to bottom: swinging laser scanner;<br />
integrated 3D data; people extraction<br />
08:15–08:30 WeAT2.2<br />
Representing Actions with Kernels<br />
Guoliang Luo, Niklas Bergström,<br />
Carl Henrik Ek and Danica Kragic<br />
Computer Vision and Active Perception Lab<br />
Royal Institute of Technology (KTH), Sweden<br />
Approach<br />
• Actions as interaction<br />
• Structural representation<br />
• Define kernel induced feature<br />
representation<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–97–<br />
Benefits<br />
• Robust to noise<br />
• Allows for principled inference<br />
08:45–08:50 WeAT2.4<br />
4-Dimensional Local Spatio-Temporal Features<br />
for Human Activity Recognition<br />
Hao Zhang and Lynne E. Parker<br />
Department of Electrical Engineering and Computer Science,<br />
University of Tennessee, Knoxville, USA<br />
• A 4-dimensional local spatio-temporal<br />
feature is proposed that combines both<br />
intensity and depth information<br />
• The proposed feature is used for human<br />
activity recognition with the latent Dirichlet<br />
allocation (LDA) model as a classifier<br />
• A new human activity database is created<br />
using a Kinect installed on a Pioneer robot<br />
• An average accuracy of 91.50% is<br />
achieved using the LDA model with the<br />
proposed feature<br />
Confusion matrix of human<br />
activity recognition using Kinect
Session WeAT2 Continental Parlor 2 Wednesday, September 28, <strong>2011</strong>, 08:00–09:30<br />
Models and Representation<br />
Chair Lynne Parker, Univ. of Tennessee<br />
Co-Chair Christian Schlegel, Univ. of Applied Sciences Ulm<br />
08:50–08:55 WeAT2.5<br />
Conic Fitting based on Stochastic Linearization<br />
Marcus Baum and Uwe D. Hanebeck<br />
Intelligent Sensor-Actuator-Systems Lab (ISAS)<br />
Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany<br />
• Fitting conics, e.g., ellipses, to noisy measurements<br />
• Approximation of the implicit measurement eqation with<br />
an explicit measurement equation<br />
• Gaussian filter<br />
09:00–09:15 WeAT2.7<br />
Managing Execution Variants in Task Coordination<br />
by Exploiting Design-Time Models at Run-Time<br />
Andreas Steck and Christian Schlegel<br />
Department of Computer Science,<br />
University of Applied Sciences Ulm, Germany<br />
• Models created at design-<br />
time are used by the<br />
robot at run-time to<br />
support the decision<br />
making process.<br />
• At design-time purpose-<br />
fully left open variation<br />
points are bound at run-<br />
time.<br />
• Real-world experiments<br />
http://youtu.be/xtLK-655v7k<br />
SmartTCL coordinates the whole<br />
system<br />
08:55–09:00 WeAT2.6<br />
Bootstrapping sensorimotor cascades:<br />
a group-theoretic perspective<br />
Andrea Censi and Richard M. Murray<br />
Control & Dynamical Systems<br />
California Institute of Technology, USA<br />
• In the bootstrapping problem we study<br />
agents that can learn to use unknown<br />
actuators and unknown sensors, starting<br />
from zero prior information.<br />
• For example, the figure shows the raw<br />
intensity values returned by a subset of<br />
the sensels in a range-finder and a<br />
camera (can you tell which is which?)<br />
• In this paper, we formulate the problem as<br />
a problem of group nuisance rejection.<br />
Invariance to group nuisances is our<br />
guiding principle for design.<br />
• Models are demonstrated on real-world<br />
data of cameras and range-finders.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–98–<br />
Sensels �<br />
Time �<br />
09:15–09:30 WeAT2.8<br />
The Mathematical Model and Control of Human-<br />
Machine Perceptual Feedback System<br />
Han Yoon and Seth Hutchinson<br />
Department of Electrical and Computer Engineering,<br />
University of Illinois, USA<br />
• Create interfaces that enable perceptual<br />
feedback control between a human user<br />
and a mobile robot.<br />
• Enhance the perception-to-behavior<br />
performance of an unskilled human user<br />
by warping the perceptual display<br />
• Improve the wellbeing of people suffering<br />
from the lack of manipulability<br />
• Protect people like drivers or pilots who<br />
are impaired by fatigue, boredom, or<br />
possible substance abuse
Session WeAT3 Continental Parlor 3 Wednesday, September 28, <strong>2011</strong>, 08:00–09:30<br />
Medical Robotics: Tracking & Detection<br />
Chair Stefano Stramigioli, Univ. of Twente<br />
Co-Chair Erion Plaku, Catholic Univ. of America<br />
08:00–08:15 WeAT3.1<br />
Three-Dimensional Pose Reconstruction of<br />
Flexible Instruments from Endoscopic Images<br />
Rob Reilink, Stefano Stramigioli, and Sarthak Misra<br />
University of Twente, The Netherlands<br />
• Position and orientation of flexible<br />
endoscopic instruments are measured<br />
without adding sensors<br />
• Feature points of endoscopic instrument<br />
are detected within the endoscopic<br />
image<br />
• The state of a kinematic model is<br />
adapted in order to match the feature<br />
points of the model to observed points in<br />
the image<br />
• RMS error of 1.7, 1.2, and 3.6mm in<br />
resp. horizontal (x), vertical (y) and<br />
longitudinal (z) directions in colon<br />
anatomical model.<br />
08:30–08:45 WeAT3.3<br />
Comparison of Several Image Features<br />
for WCE Video Abstract<br />
Baopu Li1,2 , Max Q.-H. Meng1,2 1. Department of Electronic Engineering, The Chinese University of Hong Kong<br />
2. Shenzhen Institute of Advanced Technology,<br />
The Chinese Academy of Science, China<br />
• Wireless Capsule endoscopy (WCE) is a revolutionary technique to<br />
examine the diseases in small intestine<br />
• Deduction of the review time for a WCE video is needed<br />
• Several image features are compared to summarize a WCE<br />
• Preliminary experiments show that textural and motion features maybe<br />
suitable for a WCE video abstract<br />
• Clinical validation is a part of future work<br />
08:15–08:30 WeAT3.2<br />
Detection of Curved Robots using 3D<br />
Ultrasound<br />
Hongliang Ren, Nikolay V. Vasilyev and Pierre E. Dupont<br />
Department of Cardiac Surgery, Children's Hospital Boston<br />
Harvard Medical School, USA<br />
• Goal is image-based continuum robot<br />
tracking and servoing using real-time 3D<br />
ultrasound<br />
• Existing detection algorithms apply to<br />
robotic instruments with straight shafts,<br />
but do not extend to continuum robots that<br />
curve along their length<br />
• Problem complexity is reduced by<br />
decomposing arc detection into sequence<br />
of plane and circle detection problems.<br />
• Proposed approach is parallelizable.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–99–<br />
3D Ultrasound volume and<br />
detected curved robot<br />
08:45–08:50 WeAT3.4<br />
Toward Development of 3D Surgical Mouse<br />
Paradigm<br />
Xiaochuan Sun and Shahram Payandeh<br />
Experimental Robotics and Imaging Laboratory<br />
School of Engineering Science<br />
Simon Fraser University, Canada<br />
• Monocular systems imaging systems are<br />
commonly utilized in In minimally invasive<br />
surgery.<br />
• The lack of 3D visual perception offers a<br />
natural challenge for innovation design<br />
opportunities developing in surgeoncomputer<br />
interface.<br />
• Design of a robust monocular-based<br />
image tracking of the surgical instruments<br />
is an essential first step.<br />
• We introduce a novel 3D mouse paradigm<br />
and compare four different image tracking<br />
schemes.<br />
3D mouse conceptual diagram
Session WeAT3 Continental Parlor 3 Wednesday, September 28, <strong>2011</strong>, 08:00–09:30<br />
Medical Robotics: Tracking & Detection<br />
Chair Stefano Stramigioli, Univ. of Twente<br />
Co-Chair Erion Plaku, Catholic Univ. of America<br />
08:50–08:55 WeAT3.5<br />
3D Thread Tracking for Robotic Assistance in<br />
Tele-surgery<br />
Nicolas Padoy, Gregory D. Hager<br />
The Johns Hopkins University, Baltimore, MD, USA<br />
{padoy,hager}@jhu.edu<br />
• Automatic third arm assistance<br />
• Thread modeling and tracking with nonuniform<br />
rationale B-splines<br />
• Energy approximation and minimization<br />
using discrete MRF optimization<br />
• Automatic scissors command for the da<br />
Vinci robot<br />
09:00–09:15 WeAT3.7<br />
In-vitro Three Dimensional Vasculature Modeling<br />
Based on Sensor Fusion between Intravascular<br />
Ultrasound and Magnetic Tracker<br />
Chaoyang Shi, Carlos Tercero, Seiichi Ikeda, Toshio Fukuda<br />
Micro-Nano Systems Engineering, Nagoya University, Japan<br />
Kimihiro Komori and Kiyohito Yamamoto<br />
Graduate School of Medicine, Nagoya University, Japan<br />
• Proposed the new sensor fusion<br />
technology between intravascular<br />
ultrasound (IVUS) and magnetic<br />
trackers for intravascular surgery<br />
• Integrated the hybrid probe and<br />
measured the disturbances between<br />
two sensors<br />
• Performed in-vitro experiments by<br />
adopting a silicone model of<br />
descending aorta, and reconstructed<br />
the corresponding 3D model<br />
Fig. a) Hybrid probe consisting of magnetic<br />
tracker and IVUS probe; b) Silicone model<br />
for in-vitro experiments; c) Reconstructed<br />
model.<br />
08:55–09:00 WeAT3.6<br />
Ultrasound image features of the wrist<br />
are linearly related to finger positions<br />
Claudio Castellini and Georg Passig<br />
Institute of Robotics and Mechatronics<br />
DLR - German Aerospace Research Center<br />
Oberpfaffenhofen, Germany<br />
• ultrasound images of the human wrist<br />
and finger positions are recorded using<br />
a commercial ultrasound machine and<br />
a dataglove<br />
• first-order local linear approximations of<br />
the grey levels are extracted from the<br />
images, and<br />
• a strong linear relationship is detected<br />
between them and finger positions<br />
• finger positons are then predicted in<br />
real time<br />
• this includes fingers flexion/extension<br />
and thumb adduction/rotation<br />
• the system is robust to downsampling<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–100–<br />
typical values of true and<br />
predicted pinkie position<br />
• main forseen application is<br />
on transradial amputees:<br />
1.treatment of phantom limb pain<br />
2.visualisation of the imaginary limb<br />
3.control of a hand prosthesis<br />
09:15–09:30 WeAT3.8<br />
Surgical Tools Pose Estimation for a Multimodal<br />
HMI of a Surgical Robotic Assistant<br />
Belén Estebanez, Enrique Bauzano and Víctor F. Muñoz<br />
System Engineering and Automation, University of Malaga, Spain<br />
• The recognition of a surgeon’s maneuver<br />
through Hidden Markov Models (HMM)<br />
requires the tracking of the surgical tools.<br />
• Minimization of the measured occluded<br />
areas is needed to control autonomous<br />
movements of a robotic assistant through<br />
gestures.<br />
• Location of each surgeon’s tool may be<br />
estimated with Multiple Extended Kalman<br />
Filters (MEKF), one for each gesture the<br />
maneuver is composed of.<br />
• Estimated location depends on the most<br />
probable maneuver the surgeon may be<br />
carrying out.
Session WeAT4 Continental Ballroom 4 Wednesday, September 28, <strong>2011</strong>, 08:00–09:30<br />
Symposium: Haptics Interfaces for the Fingertip, Hand, and Arm<br />
Chair Katherine J. Kuchenbecker, Univ. of Pennsylvania<br />
Co-Chair Marcia O'Malley, Rice Univ.<br />
08:00–08:15 WeAT4.1*<br />
Semi-Plenary Invited Talk: Surface Haptics: Virtual<br />
Touch on Physical Surfaces<br />
Edward Colgate, Northwestern University<br />
08:30–08:45 WeAT4.3<br />
Weight and Friction Display Device<br />
by Controlling the Slip Condition of a Fingertip<br />
Yuichi Kurita<br />
Graduate School of Engineering, Hiroshima University, Japan<br />
Satoshi Yonezawa, Atsutoshi Ikeda and Tsukasa Ogasawara<br />
Graduate School of Information Science,<br />
Nara Institute of Technology, Japan<br />
• weight and friction illusions are generated<br />
by controlling ``eccentricity’’ of the contact<br />
surface between a fingertip and a rigid<br />
plate.<br />
• The eccentricity is a quantitative index of<br />
the slip condition calculated from image<br />
processing of captured images.<br />
• The desired eccentricity profile for the<br />
target weight and friction was obtained by<br />
modeling human’s grip/load force profiles.<br />
• Human experiments showed that the<br />
proposed device successfully presents the<br />
weight/friction illusions.<br />
08:15–08:30 WeAT4.2<br />
Semi-Plenary Invited Talk: Surface Haptics: Virtual<br />
Touch on Physical Surfaces<br />
Edward Colgate, Northwestern University<br />
08:45–08:50 WeAT4.4<br />
On-line Bio-impedance Identification of Fingertip<br />
Skin for Enhancement of Electrotactile Based Haptic<br />
Rendering<br />
John Gregory*, Yantao Shen**, and Ning Xi †<br />
*Wintek Electro-Optics Co., Ann Arbor, Michigan, USA<br />
** Dept. of Electrical and Biomedical Engr., University of Nevada, Reno, USA<br />
† Dept. of Electrical and Computer Engr., Michigan State University, USA &<br />
Dept. of Manufacturing Engineering and Engineering Management, The City<br />
University of Hong Kong, China<br />
• A new constant-voltage-driver<br />
(CVD) based electrotactile haptic<br />
rendering system is developed<br />
• The system owns the feature of<br />
on-line identifying bio-impedance<br />
of fingertip skin for haptic/tactile<br />
preference tuning<br />
• The identification method is<br />
based on a discrete-time<br />
extended least squares iterative<br />
approach with forgetting factor<br />
• Experimental results<br />
demonstrate the performance of<br />
both the system and the<br />
identification method<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–101–<br />
Adaptive fingertip skin bio-impedance<br />
identification in z-domain
Session WeAT4 Continental Ballroom 4 Wednesday, September 28, <strong>2011</strong>, 08:00–09:30<br />
Symposium: Haptics Interfaces for the Fingertip, Hand, and Arm<br />
Chair Katherine J. Kuchenbecker, Univ. of Pennsylvania<br />
Co-Chair Marcia O'Malley, Rice Univ.<br />
08:50–08:55 WeAT4.5<br />
Design of an MRI Compatible Haptic Interface<br />
• A plastic, MRI Compatible, 1-axis, fiber<br />
optic force sensor for robotic<br />
applications in MRI environment<br />
• Its compact design employs a<br />
displacement amplifying compliant<br />
mechanism for more accurate sensing<br />
• Samples made of delrin and ABSplastic<br />
are tested, both samples<br />
confirmed MRI safety requirements<br />
• Delrin sample is superior to ABSplastic<br />
in accuracy and uniaxial<br />
sensitivity<br />
Melih Turkseven and Jun Ueda<br />
George W. Woodruff School of Mechanical Engineering,<br />
Georgia Institute of Technology, USA<br />
The proposed MRI compatible<br />
force sensor<br />
09:00–09:15 WeAT4.7<br />
Wide-bandwidth Bilateral Control<br />
Using Two Stage Actuator Systems:<br />
Evaluation Results of a Prototype<br />
Saori Kokuryu, Masaki Izutsu, Norihiro Kamamichi and<br />
Jun Ishikawa<br />
Department of Robotics and Mechatoronics, Tokyo Denki University, Japan<br />
• Developed a prototype of the proposed<br />
two stage actuator system for widebandwidth<br />
bilateral control<br />
• Proposed a coordinated motion control via<br />
control bandwidth separation method<br />
• Successfully Implemented a mechanical<br />
impedance control, the mass of which was<br />
0.01 kg, the damping 1.6 N/(m/s) without<br />
stiffness<br />
• Established a basis for wide-bandwidth<br />
bilateral control using lightly-moving<br />
actuator system<br />
Second Seco S<br />
stage<br />
First stage<br />
Ball screw<br />
AC<br />
motor<br />
Encoder<br />
Laser displacement sensor<br />
Prototype using two inexpensive<br />
actuators, i.e., voice coil motor<br />
and AC motor with ball screw<br />
08:55–09:00 WeAT4.6<br />
Force Producibility Improvement of Redundant<br />
Parallel Mechanism for Haptic Applications<br />
Jumpei Arata, Norio Ikedo and Hideo Fujimoto<br />
Nagoya Institute of Technology, Japan<br />
• Parallel mechanism is widely applied to<br />
haptic applications for advantageous<br />
benefits such as high rigidity, output force<br />
and backdrivability .<br />
• In our past study, we proposed a new<br />
redundant parallel mechanism “DELTA-R”<br />
for a haptic device.<br />
• In this paper, we propose an improvement<br />
method of force producibility by using the<br />
redundant DOF.<br />
• The forward kinematic model was<br />
modified by taking into account the<br />
geometric conditions of the mechanism.<br />
• Extremal Force Diagram (EFD) and<br />
experimental results using prototype<br />
clearly showed the effectiveness of the<br />
proposed method.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–102–<br />
Redundant PM DELTA-R<br />
and its improved force producibility<br />
09:15–09:30 WeAT4.8<br />
A New Generation of Ergonomic Exoskeletons –<br />
The High-Performance X-Arm-2<br />
André Schiele<br />
Telerobotics & Haptics Laboratory, ESA, Netherlands<br />
Gerd Hirzinger<br />
Institute of Robotics & Mechatronics, DLR, Germany<br />
• New, fully actuated and ergonomic<br />
haptic exoskeleton for space robotics<br />
Telepresence<br />
• Human-centered design<br />
• Low mass and high-power density<br />
implementation of actuators through<br />
combination of directly integrated and<br />
Bowden Cable relocated drives<br />
• Detailed performance characterization of<br />
mechatronic implementation through<br />
Bode plots and contact experiments<br />
X-Arm-2 Exoskeleton front (top) and<br />
back-side view (bottom)
Session WeAT5 Continental Ballroom 5 Wednesday, September 28, <strong>2011</strong>, 08:00–09:30<br />
Symposium: Robot Motion Planning: Achievements and Emerging Approaches<br />
Chair Ron Alterovitz, Univ. of North Carolina at Chapel Hill<br />
Co-Chair Maxim Likhachev, Carnegie Mellon Univ.<br />
08:00–08:15 WeAT5.1*<br />
Semi-Plenary Invited Talk: 50 Years of Robotics:<br />
Motion Planning<br />
Tomas Lozano-Perez, MIT<br />
08:30–08:45 WeAT5.3<br />
Conflict-Free Route Planning<br />
in Dynamic Environments<br />
Adriaan W. ter Mors<br />
Delft University of Technology, The Netherlands<br />
• Optimal route planning on a roadmap for a single robot, in<br />
polynomial time<br />
• Prioritized approach: a robot plans around the routes of higherpriority<br />
robots<br />
• Unexpected delays for one robot can propagate to others<br />
• Pareto- and non-Pareto optimal plan repair mechanisms are<br />
compared<br />
Two robots traverse a shared roadmap<br />
of limited-capacity resources<br />
08:15–08:30 WeAT5.2<br />
Semi-Plenary Invited Talk: 50 Years of Robotics:<br />
Motion Planning<br />
Tomas Lozano-Perez, MIT<br />
08:45–08:50 WeAT5.4<br />
Kinodynamic Motion Planning with<br />
State Lattice Motion Primitives<br />
Mihail Pivtoraiko and Alonzo Kelly<br />
Robotics Institute, Carnegie Mellon University, USA<br />
• We present an approach to automatic<br />
generation of mo-tion primitives for<br />
kinodyna-mic motion planning;<br />
• State lattice motion primitives feature a<br />
number of advanta-ges, including enabling<br />
reuse of computation (e.g. D*) in differentially<br />
constrained motion planning;<br />
• We demonstrate incremental planning for<br />
a dynamics sys-tem: a mobile robot<br />
moving on slippery surface with significant<br />
drift.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–103–
Session WeAT5 Continental Ballroom 5 Wednesday, September 28, <strong>2011</strong>, 08:00–09:30<br />
Symposium: Robot Motion Planning: Achievements and Emerging Approaches<br />
Chair Ron Alterovitz, Univ. of North Carolina at Chapel Hill<br />
Co-Chair Maxim Likhachev, Carnegie Mellon Univ.<br />
08:50–08:55 WeAT5.5<br />
Efficient Motion Planning for Manipulation Robots<br />
in Environments with Deformable Objects<br />
Barbara Frank, Cyrill Stachniss,<br />
Nichola Abdo, and Wolfram Burgard<br />
Department of Computer Science, University of Freiburg, Germany<br />
• Approach to online manipulator motion<br />
planning considering deformable objects<br />
• Efficient estimation of deformation cost<br />
along a trajectory using Gaussian process<br />
(GP) regression<br />
• GP training using a deformation simulation<br />
based on finite element methods<br />
• Experiments illustrate an accurate cost<br />
estimation and online planning capabilities<br />
Our robot deforming the leaves of<br />
a plant along a trajectory<br />
09:00–09:15 WeAT5.7<br />
A Simplified Model of RRT Coverage for<br />
Kinematic Systems<br />
Joel M Esposito<br />
Systems Engineering, United States Naval Academy<br />
• Rapidly Exploring Random Trees explore<br />
the state space..but how quickly?<br />
• We model state space coverage as a<br />
random discrete time process<br />
• Under two simplifying assumptions we<br />
obtain a closed form solution for expected<br />
coverage as a function of number of<br />
nodes<br />
• Experimental results suggest model is<br />
valid for holonomic systems in expansive<br />
configuration spaces.<br />
• Model can be used as termination criteria,<br />
or as a measure of problem difficulty<br />
08:55–09:00 WeAT5.6<br />
Learning Dimensional Descent planning for a<br />
highly-articulated robot arm<br />
Paul Vernaza and Daniel D. Lee<br />
GRASP Laboratory, University of Pennsylvania, USA<br />
• Learning Dimensional Descent (LDD)<br />
solves high-dimensional planning<br />
problems by solving a sequence of<br />
low-dimensional DP problems<br />
• Learning-based technique<br />
automatically identifies and exploits<br />
low-dimensional structure<br />
• Experiments with PR2 robot<br />
demonstrate much higher-quality<br />
solutions obtained than with samplingbased<br />
planners and smoothing<br />
• Open-source implementation available<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–104–<br />
Illustration of one step of LDD algorithm.<br />
Solution generated in next step is<br />
minimum-cost path restricted to manifold<br />
of previous path swept along learned<br />
basis directions.<br />
09:15–09:30 WeAT5.8<br />
Space-Filling Trees: A New Perspective on<br />
Incremental Search for Motion Planning<br />
James J. Kuffner<br />
The Robotics Institute, Carnegie Mellon University, USA<br />
& Google Research, Google Inc., USA<br />
Steven M. LaValle<br />
Dept. of Computer Science, Univ. of Illinois at Urbana-Champaign, USA<br />
• The concept of “Space-Filling Trees” is<br />
introduced and analyzed in the context of<br />
sampling-based motion planning.<br />
• Space-filling trees are analogous to<br />
space-filling curves, but with a branching,<br />
tree-like structure that connects a single<br />
point in a continuous space to every other<br />
point by a path of finite length.<br />
• Rapidly-exploring Random Trees (RRTs)<br />
can be considered stochastic variants of<br />
space-filling trees, and compared in terms<br />
of overall efficiency of exploration.<br />
• Key open problems remain, such as<br />
applying space-filling trees to the design<br />
and analysis of sampling-based planners.<br />
Three Iterations of the triangle<br />
and square space-filling trees.
Session WeAT6 Continental Ballroom 6 Wednesday, September 28, <strong>2011</strong>, 08:00–09:30<br />
Symposium: Aerial Robotics: Estimation, Perception and Control<br />
Chair Nathan Michael, Univ. of Pennsylvania<br />
Co-Chair Pieter Abbeel, UC Berkeley<br />
08:00–08:15 WeAT6.1*<br />
Semi-Plenary Invited Talk: Do It Yourself Drones<br />
Chris Anderson, Wired Magazine<br />
08:30–08:45 WeAT6.3<br />
Multiple-Objective Motion Planning<br />
for Unmanned Aerial Vehicles (UAVs)<br />
Sebastian Scherer and Sanjiv Singh<br />
Robotics Institute, Carnegie Mellon University, USA<br />
• Want to perform various missions (reach a goal, search for landing<br />
sites, approach LZs), however mission cannot be expressed as goal<br />
seeking behavior<br />
• Propose a consistent framework and algorithm to plan with a<br />
combination of multiple objectives<br />
• Show results from performing realistic missions in simulation<br />
08:15–08:30 WeAT6.2<br />
Semi-Plenary Invited Talk: Do It Yourself Drones<br />
Chris Anderson, Wired Magazine<br />
08:45–08:50 WeAT6.4<br />
Bilateral Teleoperation of Multiple UAVs with<br />
Decentralized Bearing-only Formation Control<br />
Antonio Franchi 1 , Carlo Masone 1<br />
Heinrich H. Bülthoff 1,2 , and Paolo Robuffo Giordano 1<br />
1 Max Plank Institute for Biological Cybernetics, Germany<br />
2 Department of Brain and Cognitive Engineering, Korea University, Korea<br />
• Decentralized<br />
multi-master/multi-slave<br />
bilateral teleoperation of<br />
groups of UAVs:<br />
1. Human controls collective motion<br />
2. UAVs autonomously control shape<br />
• Only relative bearing measures<br />
used (no global localization, no<br />
distances)<br />
• Rigorous analysis of minimally-rigid sets<br />
of 3D bearing-formations and associated<br />
decentralized formation control<br />
• Stable design of force feedback for the human operator in<br />
order to increase telepresence<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–105–<br />
Human-hardware-in-the-loop<br />
simulations and experiments<br />
with quadrotors<br />
and 2 haptic devices
Session WeAT6 Continental Ballroom 6 Wednesday, September 28, <strong>2011</strong>, 08:00–09:30<br />
Symposium: Aerial Robotics: Estimation, Perception and Control<br />
Chair Nathan Michael, Univ. of Pennsylvania<br />
Co-Chair Pieter Abbeel, UC Berkeley<br />
08:50–08:55 WeAT6.5<br />
Modeling and Decoupling Control<br />
of the CoaX Micro Helicopter<br />
Péter Fankhauser, Samir Bouabdallah,<br />
Stefan Leutenegger, Roland Siegwart<br />
Autonomous Systems Lab, ETH Zurich, Switzerland<br />
• A nonlinear model for a coaxial micro<br />
helicopter based on gray-box modeling<br />
and parameter identification is presented<br />
• The model accounts for hover and<br />
cruise flight situations and captures the<br />
off-axis dynamics and the dynamics of<br />
the stabilizer bar<br />
• A test bench and a vision based tracking<br />
system are used for parameter<br />
identification and model validation<br />
• A decoupling controller is developed and<br />
implemented which shows to increase<br />
the accuracy of flight trajectories<br />
09:00–09:15 WeAT6.7<br />
Deterministic Initialization of Metric<br />
State Estimation Filters for Loosely-Coupled<br />
Monocular Vision-Inertial Systems<br />
Laurent Kneip, Stephan Weiss, and Roland Siegwart<br />
Autonomous Systems Lab, ETH Zurich<br />
• Deterministic scale factor computation of monocular visual SLAM<br />
• Deterministic computation of orientation with respect to gravity direction<br />
• Performed via a derivation of delta-velocities from<br />
• Numerical differentiation of visual pose<br />
• Integration of inertial data<br />
• Robustness analysis for noise<br />
and degenerate motions<br />
• Successful results on simulated<br />
and real data<br />
• Performance tests on filter initialization<br />
08:55–09:00 WeAT6.6<br />
On Active Target Tracking and Cooperative<br />
Localization for Multiple Aerial Vehicles<br />
Fabio Morbidi, Gian Luca Mariottini<br />
ASTRA Robotics Laboratory<br />
Department of Computer Science & Engineering<br />
University of Texas at Arlington, USA<br />
• Cooperative active target-tracking<br />
for a team of aerial vehicles<br />
equipped with 3-D range-finding<br />
sensors<br />
• Gradient-based controllers are<br />
designed for each vehicle<br />
• The Kalman-Bucy filter is used for<br />
estimation fusion<br />
• Analytical derivation of lower and<br />
upper bounds on the target’s<br />
position uncertainty<br />
• Active Cooperative Localization<br />
and Multi-target Tracking (ACLMT)<br />
minimizes both targets’- and<br />
robots’- position uncertainty<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–106–<br />
Four aerial vehicles cooperatively<br />
track a target to estimate its position<br />
with minimum uncertainty<br />
09:15–09:30 WeAT6.8<br />
Collaborative Stereo<br />
Markus W. Achtelik*, Stephan Weiss*, Margarita Chli*,<br />
Frank Dellaert † , and Roland Siegwart*<br />
*ETH Zurich, Autonomous Systems Lab, Switzerland<br />
† Georgia Institute of Technology, School of Interactive Computing, USA<br />
• Two MAVs, each equipped with a monocular camera and an IMU,<br />
form a variable baseline stereo rig<br />
• Extrinsic calibration estimation with absolute scale baseline by<br />
incorporating IMU information from both MAVs<br />
• Vision / IMU data fusion with EKF framework – only dependent on<br />
IMU and relative vision measurements
Session WeAT7 Continental Parlor 7 Wednesday, September 28, <strong>2011</strong>, 08:00–09:30<br />
Robot Walking<br />
Chair Luis Sentis, The Univ. of Texas at Austin<br />
Co-Chair Shinya Aoi, Kyoto Univ.<br />
08:00–08:15 WeAT7.1<br />
Self-stabilization Principle of Mechanical Energy<br />
Inherent in Passive Compass Gait<br />
Fumihiko Asano<br />
School of Information Science,<br />
Japan Advanced Institute of Science and Technology, Japan<br />
• The self-stabilization principle<br />
underlying passive compass gait is<br />
investigated from the mechanical<br />
energy point of view.<br />
• It is numerically shown that a passive<br />
compass gait is monotonically<br />
stabilized not by means of the state<br />
variables but by means of the total<br />
mechanical energy.<br />
• The linearized mechanical energy<br />
(LME) is defined and the mechanism<br />
of monotonic convergence is<br />
mathematically investigated.<br />
08:30–08:45 WeAT7.3<br />
A Walking Stability Controller with Disturbance<br />
Rejection Based on CMP Criterion and Ground<br />
Reaction Force Feedback<br />
Richard Beranek, Henry Fung and Mojtaba Ahmadi<br />
Department of Mechanical and Aerospace Engineering, Carleton University,<br />
Canada<br />
• Robustness to external disturbances in<br />
bipedal control remains limited<br />
• Centroidal Moment Pivot (CMP) with<br />
ground reaction force feedback is used to<br />
modify the reference COG trajectory to<br />
increase stability<br />
• Simulation results results show the<br />
controller is more robust to a sagital<br />
disturbance than a ZMP based controller<br />
With ground reaction force<br />
feedback, CMP can be used to<br />
compensate for unbalanced<br />
moments acting on the system<br />
08:15–08:30 WeAT7.2<br />
Model-Based Velocity Control<br />
for Limit Cycle Walking<br />
Tuomas Haarnoja<br />
Smart Machines, VTT Technical Research Centre of Finland, Finland<br />
José-Luis Peralta-Cabezas and Aarne Halme<br />
Department of Automation and Systems Technology, Aalto University, Finland<br />
• Limit Cycle Walking (LCW) is an energy efficient but restrictive method<br />
of locomotion for legged robots<br />
• The paper proposes several ideas for controlling the velocity of a LCW<br />
robot<br />
• The methods are tested in simulations and the results are compared in<br />
the sense of their energy efficiency and achievable velocity range<br />
08:45–08:50 WeAT7.4<br />
Perturbation Theory to Plan Dynamic Locomotion<br />
in Very Rough Terrain<br />
• Motivation<br />
• Planning<br />
Luis Sentis and Benito Fernandez<br />
The University of Texas at Austin, USA<br />
• Prediction<br />
• Results<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–107–
Session WeAT7 Continental Parlor 7 Wednesday, September 28, <strong>2011</strong>, 08:00–09:30<br />
Robot Walking<br />
Chair Luis Sentis, The Univ. of Texas at Austin<br />
Co-Chair Shinya Aoi, Kyoto Univ.<br />
08:50–08:55 WeAT7.5<br />
Generation of adaptive splitbelt treadmill<br />
walking by a biped robot using nonlinear<br />
oscillators with phase resetting<br />
Shinya Aoi 1 , Soichiro Fujiki 1 , Tsuyoshi Yamashita 1<br />
Takehisa Kohda 1 , Kei Senda 1 , and Kazuo Tsuchiya 2<br />
1 Kyoto University, Japan, 2 Doshisha University, Japan<br />
• We designed a biped robot, whose<br />
joints are controlled by nonlinear<br />
oscillators with phase resetting<br />
• Stable walking on splitbelt treadmill<br />
was generated despite various<br />
speed conditions of belts<br />
• Speed discrepancy caused shift of<br />
phase differences between leg<br />
movements and modulation of duty<br />
factors<br />
• This adaptability was due to<br />
modulation of locomotion rhythm and<br />
phase by phase resetting<br />
Biped robot walking on<br />
splitbelt treadmill<br />
09:00–09:15 WeAT7.7<br />
Multi-objective Parameter CPG Optimization for<br />
Gait Generation of a Quadruped Robot<br />
Considering Behavioral Diversity<br />
Miguel Oliveira, Cristina P. Santos,<br />
Vítor Matos, and Manuel Ferreira<br />
Industrial Electronics Department, Minho University , Portugal<br />
Lino Costa<br />
Production Systems Department, Minho University, Portugal<br />
• Multi-objective optimization system<br />
that combines bio-inspired Central<br />
Patterns Generators (CPGs) and a<br />
multi-objective evolutionary<br />
algorithm(NSGAII)<br />
• CPGs are modeled as autonomous<br />
differential equations that generate the<br />
necessary limb movement<br />
• four conflicting objectives are<br />
considered: vibration, velocity, wide<br />
stability margin and behavioral<br />
diversity<br />
08:55–09:00 WeAT7.6<br />
Experimental verification of hysteresis in gait<br />
transition of a quadruped robot driven by<br />
nonlinear oscillators with phase resetting<br />
Shinya Aoi 1 , Soichiro Fujiki 1 , Daiki Katayama 1 , Tsuyoshi<br />
Yamashita 1 , Takehisa Kohda 1 , Kei Senda 1 , Kazuo Tsuchiya 2<br />
1 Kyoto University, Japan, 2 Doshisha University, Japan<br />
• We designed a quadruped robot,<br />
whose legs are controlled by<br />
nonlinear oscillators with phase<br />
resetting<br />
• Walk and trot patterns were generated<br />
through interactions among robot<br />
dynamics, oscillator dynamics, and<br />
environment<br />
• Walk-trot transition is induced by<br />
changing the walking speed<br />
• Hysteresis appears in the gait<br />
transition<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–108–<br />
Quadruped robot and<br />
hysteresis in walk-trot transition<br />
09:15–09:30 WeAT7.8<br />
A sparse model predictive control formulation<br />
for walking motion generation<br />
Dimitar Dimitrov and Alexander Sherikov<br />
Orebro University, Sweden<br />
Pierre-Brice Wieber<br />
INRIA-Grenoble, France<br />
• Walking motion generation with variable<br />
sampling time and CoM height<br />
• MPC formulation that does not depend<br />
on forming an objective offline<br />
• Faster QP solution
Session WeAT8 Continental Parlor 8 Wednesday, September 28, <strong>2011</strong>, 08:00–09:30<br />
Networked Robots<br />
Chair C. S. George Lee, Purdue Univ.<br />
Co-Chair Ron Lumia, Univ. of New Mexico<br />
08:00–08:15 WeAT8.1<br />
Mobility and Routing Joint Design for Lifetime<br />
Maximization in Mobile Sensor Networks<br />
Shengwei Yu and C. S. George Lee<br />
School of Electrical and Computer Engineering<br />
Purdue University, USA<br />
• Simultaneously design mobility and<br />
routing strategies of robotic sensor nodes<br />
to improve the lifetime of a mobile sensor<br />
network.<br />
• The non-convex problem was solved<br />
distributedly by a series of convex<br />
approximations and a novel algorithm.<br />
• Computer simulations demonstrated quick<br />
convergence to the solution and the<br />
lifetime of the network was improved<br />
tremendously.<br />
• The proposed joint design method has an<br />
edge over other methods in lifetime<br />
maximization of mobile sensor networks.<br />
08:30–08:45 WeAT8.3<br />
Optimal Maintenance Strategy<br />
in<br />
Fault-Tolerant Multi-Robot Systems<br />
Satoshi Hoshino and Hiroya Seki<br />
Chemical Resources Laboratory, Tokyo Institute of Technology, Japan<br />
Jun Ota<br />
Research into Artifacts, Center for Engineering (RACE), The University of<br />
Tokyo, Japan<br />
• Optimal maintenance strategy for multirobot<br />
systems on the basis of reliability<br />
engineering is proposed<br />
• Robots are allowed to undergo preventive<br />
maintenance at optimal intervals<br />
• Fault tolerance of the system is ensured<br />
as the number of robots is increased<br />
• Highest performance of the system with<br />
many robots is successfully maintained<br />
Robot’s availability and failure<br />
rate curves based on shape<br />
parameters<br />
08:15–08:30 WeAT8.2<br />
Decentralized Multi-Vehicle Path Coordination<br />
under Communication Constraints<br />
Pramod Abichandani and Moshe Kam<br />
Electrical and Computer Engineering Department, Drexel University, USA<br />
Hande Benson<br />
Department of Decision Sciences, Drexel University, USA<br />
• Decentralized framework to generate time<br />
optimal speed profiles for a group of path<br />
constrained vehicle robots<br />
• Paths modeled as cubic splines<br />
• Problem modeled as a Receding Horizon<br />
Mixed Integer Nonlinear Programming<br />
problem (RH-MINLP)<br />
• Constraints on kinematics, dynamics,<br />
collision avoidance and communication<br />
connectivity<br />
• Results for up to ten (10) robots<br />
08:45–08:50 WeAT8.4<br />
Distributed Control of Multi–Robot Systems with<br />
Global Connectivity Maintenance<br />
Lorenzo Sabattini<br />
Department of Electronics, Computer Sciences and Systems<br />
University of Bologna, Italy<br />
Nikhil Chopra<br />
Department of Mechanical Engineering and Institute for Systems Research<br />
University of Maryland, College Park, MD, USA<br />
Cristian Secchi<br />
Department of Sciences and Methods of Engineering<br />
University of Modena and Reggio Emilia, Italy<br />
• Decentralized estimation of the algebraic c<br />
connectivity<br />
• Connectivity maintenance is formally<br />
guaranteed<br />
• Formation control and rendezvous<br />
applications<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–109–
Session WeAT8 Continental Parlor 8 Wednesday, September 28, <strong>2011</strong>, 08:00–09:30<br />
Networked Robots<br />
Chair C. S. George Lee, Purdue Univ.<br />
Co-Chair Ron Lumia, Univ. of New Mexico<br />
08:50–08:55 WeAT8.5<br />
RSSI-based Physical Layout Classification and<br />
Target Tethering in Mobile Ad-hoc Networks<br />
Prashant Reddy<br />
Machine Learning Department, Carnegie Mellon University, USA<br />
Manuela Veloso<br />
Computer Science Department, Carnegie Mellon University, USA<br />
• LANdroids are simple inexpensive robots<br />
equipped only with a Wi-Fi radio sensor.<br />
• Problem: Using LANdroids to maintain<br />
connectivity between a static Gateway<br />
and mobile Targets (a.k.a. Target<br />
Tethering).<br />
• Challenge: The Wi-Fi RSSI signal is a<br />
very poor indicator of physical distance;<br />
how can we better use the RSSI signal?<br />
• Approach: Use SVM-based classification<br />
of RSSI signals amongst multiple<br />
LANdroids to categorize their physical<br />
layout into known Cluster Geometries.<br />
Then, use Q-learning to learn tethering<br />
policies based on those Geometries.<br />
09:00–09:15 WeAT8.7<br />
Leader-Follower Formation Control of<br />
Nonholonomic Robots with Fuzzy Logic Based<br />
Approach for Obstacle Avoidance<br />
Jawhar Ghommam1, Hasan Mehrjerdi 2 and Maarouf Saad2 1Research Unit on Mechatronics and Autonomous Systems<br />
ENIS-Sfax-Tunisia<br />
2Ecole de technologie supérieure<br />
1100, rue Notre-Dame Ouest Montreal, Quebec<br />
• A combination of the virtual vehicle and<br />
trajectory tracking approach is used.<br />
• A virtual vehicle is steered in such a way it<br />
stabilizes to a shifted reference<br />
position/heading defined by the leader<br />
• Velocity of the virtual vehicle is then used<br />
for further use in designing control law for<br />
the follower independent from the<br />
measurement of leader's velocity.<br />
• Ensuring the safety of robots while moving<br />
in a dynamic environment.<br />
• Obstacle avoidance scheme is introduced<br />
using fuzzy logic.<br />
Leader follower based virtual<br />
vehicle approach<br />
08:55–09:00 WeAT8.6<br />
Heterogeneous Sensor Network<br />
for Prioritized Sensing<br />
R. Andres Cortez and John Wood<br />
Department of Mechanical Engineering, University of New Mexico, USA<br />
Rafael Fierro<br />
Department of Electrical & Computer Eng., University of New Mexico, USA<br />
• Heterogeneous sensor network:<br />
• sensing agents<br />
• communication relays<br />
• Derive communication constraints within<br />
the network that guarantee network<br />
connectivity<br />
• Develop an algorithm that allows for<br />
adding communication links to the minimal<br />
spanning tree of the heterogeneous<br />
proximity graph<br />
• Combine a prioritized search algorithm<br />
and the communication constraints to<br />
provide a decentralized prioritized sensing<br />
control algorithm<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–110–<br />
Motion constraint set for a relay<br />
agent w.r.t. the network<br />
09:15–09:30 WeAT8.8<br />
On the Convergence of Braitenberg vehicle 3a<br />
Immersed in Parabolic Stimuli<br />
Iñaki Rañó<br />
Institut für Neuroinformatik, RUB, Germany<br />
• Braitenberg vehicle 3a models target<br />
seeking in animals.<br />
• Two sensors are directly connected to the<br />
wheels in a decreasing (-) way.<br />
• We perform the first theoretical analysis of<br />
the behavior.<br />
• The trajectory is described by non-linear<br />
differential equations.<br />
• For circular symmetry we identify the<br />
conditions when the trajectory; moves away<br />
from, approaches or circles around the<br />
target.<br />
• For parabolic situations the vehicle<br />
approaches the target along a principal<br />
direction.<br />
Braitenberg vehicle 3a
Session WeAT9 Continental Parlor 9 Wednesday, September 28, <strong>2011</strong>, 08:00–09:30<br />
Vision: From Features to Applications<br />
Chair Jana Kosecka, George Mason Univ.<br />
Co-Chair Mohammad Mahoor, Univ. of Denver<br />
08:00–08:15 WeAT9.1<br />
Yield Estimation in Vineyards<br />
by Visual Grape Detection<br />
Stephen Nuske, Supreeth Achar,<br />
Srinivas Narasimhan, Sanjiv Singh<br />
Robotics Institute, Carnegie Mellon University, USA<br />
Terry Bates<br />
Lake Eerie Research and Extension Laboratory, Cornell University, USA<br />
• Fine grained knowledge of<br />
yields can allow better<br />
management of vineyards<br />
• Current industry practice for<br />
yield estimation is destructive<br />
and spatially sparse<br />
• We use computer vision to<br />
count grapes and predict yield<br />
• Our method can be scaled to<br />
provide non-destructive, highresolution,<br />
yield estimates<br />
across large vineyards<br />
• We can predict yield of<br />
individual vineyard rows to<br />
within 9.8% of actual harvest<br />
08:30–08:45 WeAT9.3<br />
A Rotation Invariant Feature Descriptor O-DAISY<br />
and its FPGA Implementation<br />
Jan Fischer, Alexander Ruppel, Florian Weißhardt<br />
and Alexander Verl<br />
Robot Systems Department, Fraunhofer Institute for<br />
Manufacturing Engineering and Automation (IPA), Germany<br />
• O-DAISY, a rotational invariant<br />
extension of the DAISY feature<br />
descriptor<br />
• Outperforms other fast-to-compute<br />
descriptors like SURF and BRIEF<br />
• Description of FPGA<br />
implementation to achieve realtime<br />
performance of the descriptor<br />
calculation<br />
• Pipelined FPGA architecture<br />
enabling the processing of one<br />
descriptor per clock at 125 MHz<br />
Matching accuracy as a function of the<br />
rotation angle for an artificially rotated<br />
wall image using the rotation variant<br />
original DAISY (black), and O-DAISY<br />
variants (red and magenta)<br />
08:15–08:30 WeAT9.2<br />
A Novel Learning-based Approach for Local<br />
Patch Recognition<br />
Ce Gao, Yixu Song, Peifa Jia<br />
Department of Computer Science & Technology<br />
Tsinghua University, China<br />
• We propose a novel learningbased<br />
feature matching<br />
approach for Robot Visual<br />
Learning.<br />
• An improved version of FAST-9<br />
is adopted to extract keypoints<br />
quickly.<br />
• It identifies keypoints belong to<br />
different objects or background<br />
by color and texture<br />
representation.<br />
• 3a two-stage multilayer ferns<br />
classifier is trained to recognize<br />
the local patches.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–111–<br />
Matching result for BSD and PASCAL. The color<br />
points represent correct matching, and different<br />
colors represent different objects.<br />
08:45–08:50 WeAT9.4<br />
Adaptive Multi-Affine (AMA) Feature-Matching Algorithm<br />
and its Application to Minimally-Invasive Surgery Images<br />
G. Puerto1 , M. Adibi2 , J.A. Cadeddu2 , G.L. Mariottini1 1Dept. of Comp. Science and Eng., Univ. of Texas at Arlington, USA<br />
2Dept. of Urology, Univ. of Texas Southwestern Med. Center, USA<br />
• Existing methods match only few points btw. images of non-planar object<br />
• Our algorithm (AMA) can match increased number of image features<br />
on the entire 3-D object surface<br />
• Multi-Affine transformations used<br />
to recover from any feature-tracking<br />
loss (e.g., occlusion)<br />
• Affine transformations are found<br />
adaptively to maximize no. of<br />
matched features.<br />
• Extensive experimental validation<br />
with Minimally-Invasive Surgery<br />
videos.<br />
Above, state-of-art method matches few features (dots) and one<br />
affine transformation (indicated w/ arrow). Below, our method<br />
matches more features distributed over the entire object surface.
Session WeAT9 Continental Parlor 9 Wednesday, September 28, <strong>2011</strong>, 08:00–09:30<br />
Vision: From Features to Applications<br />
Chair Jana Kosecka, George Mason Univ.<br />
Co-Chair Mohammad Mahoor, Univ. of Denver<br />
08:50–08:55 WeAT9.5<br />
Video Stabilization Using SIFT-ME Features and<br />
Fuzzy Clustering<br />
Kevin L. Veon, Mohammad H. Mahoor, and Richard M. Voyles<br />
Department of Electrical and Computer Engineering<br />
University of Denver, Denver, CO, USA<br />
• The orientation of SIFT features is used to<br />
estimate rotation.<br />
• Fuzzy set theory is used to separate local<br />
motion and global motion.<br />
• Kalman filtering is used to estimate<br />
desired motion.<br />
• Scenarios with combinations of<br />
translation, rotation, local motion, and<br />
desired motion are presented.<br />
• Qualitative and quantitative measures are<br />
used to evaluate stabilization<br />
performance.<br />
Example Unstable (top) and<br />
stabilized (bottom) video frames.<br />
09:00–09:15 WeAT9.7<br />
A Learning Algorithm for Visual Pose Estimation<br />
of Continuum Robots<br />
Austin Reiter, Peter K. Allen, and Konstantinos Iliopoulos<br />
Dept. of Computer Science, Columbia University, USA<br />
Roger E. Goldman<br />
Dept. of Biomedical Engineering, Columbia University, USA<br />
Andrea Bajo and Nabil Simaan<br />
Dept. of Mechanical Engineering, Vanderbilt University, USA<br />
• Estimate the configuration of a continuum<br />
robot using vision<br />
• Learn a mapping of visual feature<br />
descriptors to ground truth configuration<br />
space variables<br />
• Interpolate a parametric manifold and<br />
estimate unknown poses using only<br />
feature descriptors<br />
• Rotational accuracy in the range of 1<br />
degree<br />
• Robust to occlusions and small training<br />
set size<br />
• Applicable to closed-loop control as<br />
accurate feedback sensory information<br />
[Top] Sample images of a single<br />
segment of a continuum robot<br />
with an occluder. [Bottom]<br />
Algorithm flow<br />
08:55–09:00 WeAT9.6<br />
Label propagation in videos indoors with an<br />
incremental non-parametric model update<br />
Jorge Rituerto, Ana Cristina Murillo<br />
DIIS – i3A, Universidad de Zaragoza, Spain<br />
Jana Košecká<br />
Computer Science Department, George Mason University, USA<br />
• Towards semantic interpretation of the robot environment<br />
• Goal: label background regions and separate them from the<br />
“foreground” along robot acquired sequences.<br />
• Learning simple models from interest regions in first frame, then<br />
update and propagate along the sequence.<br />
• Ingredients: superpixel segmentation, description and EMD based<br />
correspondences.<br />
Summary of presented propagation process<br />
09:15–09:30 WeAT9.8<br />
Multilayer real-time video image stabilization<br />
• Real-time video image<br />
stabilization system (VISS)<br />
primarily developed for aerial<br />
robots<br />
• Four independent stabilization<br />
layers<br />
• Low-cost and robust<br />
• VISS significantly improves the<br />
stability of shaky video images<br />
Jens Windau, Laurent Itti<br />
University of Southern California, USA<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–112–
Session WeBT1 Continental Parlor 1 Wednesday, September 28, <strong>2011</strong>, 10:00–11:30<br />
Socially Assistive Robots<br />
Chair Urbano Nunes, Univ. de Coimbra<br />
Co-Chair Eric Wade, Univ. of Southern California<br />
10:00–10:15 WeBT1.1<br />
Using Socially Assistive Robotics<br />
to Augment Motor Task Performance<br />
in Individuals Post-Stroke<br />
Eric Wade1 , Avinash Parnandi2 , and Maja J. Matarić1 1University of Southern California, Los Angeles, CA, USA<br />
2Texas A&M University, College Station, TX, USA<br />
• How can we influence performance measures using socially assistive<br />
robotics?<br />
10:30–10:45 WeBT1.3<br />
An Experience-Driven Robotic Assistant<br />
Acquiring Human Knowledge<br />
to Improve Haptic Cooperation<br />
Jose Ramon Medina, Martin Lawitzky, Alexander Mörtl,<br />
Dongheui Lee and Sandra Hirche<br />
Institute of Automatic Control Engineering,<br />
Technische Universität München, Germany<br />
Goal:<br />
Incremental learning of haptic<br />
primitives in complex scenarios<br />
during execution<br />
Proposed approach:<br />
• Combine incremental learning<br />
techniques with feedback<br />
control<br />
• Enhancement of autonomous<br />
segmentation, clustering,<br />
learning and execution<br />
performance by acquisition of<br />
meaningful semantic labels<br />
10:15–10:30 WeBT1.2<br />
The Impact of Different Competence Levels of<br />
Care-Receiving Robot on Children<br />
Madhumita Ghosh and Fumihide Tanaka<br />
Department of Intelligent Interaction Technologies,<br />
University of Tsukuba, Japan<br />
• New concept of robot use for education<br />
• Not like a teacher robot<br />
•But “Care-Receiving Robot (CRR)” which is<br />
taken care of (taught) by children<br />
• The goal is to achieve children’s<br />
spontaneous learning by teaching<br />
• Field trials at an English learning school<br />
for children were conducted<br />
• Investigation on two different types of CRR<br />
with presenting numerical data<br />
• CRR induced children’s behaviors which<br />
would lead to their learning reinforcement<br />
10:45–10:50 WeBT1.4<br />
Study on a Practical Robotic Follower<br />
to Support Home Oxygen Therapy Patients<br />
-Development and Control of a Mobile Platform-<br />
Atsushi Tani, Gen Endo, Edwardo F. Fukushima and Shigeo Hirose<br />
Dept. of Mechanical and Aerospace Engineering, Tokyo Institute of Technology, Japan<br />
Masatsugu Iribe<br />
The Faculty of Engineering, Osaka Electro-Communication University, Japan<br />
Toshio Takubo<br />
The First Department of Medicine, Tokyo Women’s Medical University, Japan<br />
• The patients suffering from severe lung<br />
diseases have to carry an oxygen tank<br />
when they go out.<br />
• We developed a low-cost mobile robot to<br />
carry the oxygen tank in an urban<br />
environment.<br />
• A tether interface is utilized to detect the<br />
relative position of the foregoing patient.<br />
• The robot can negotiate a curb separating a<br />
sidewalk from a street.<br />
• The following algorithm is also improved to<br />
track the patient’s trajectory.<br />
• We successfully demonstrated the following<br />
experiment in an outdoor environment.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–113–<br />
Developed mobile robot to carry<br />
an oxygen tank
Session WeBT1 Continental Parlor 1 Wednesday, September 28, <strong>2011</strong>, 10:00–11:30<br />
Socially Assistive Robots<br />
Chair Urbano Nunes, Univ. de Coimbra<br />
Co-Chair Eric Wade, Univ. of Southern California<br />
10:50–10:55 WeBT1.5<br />
Progress in Developing a Socially Assistive<br />
Mobile Home Robot Companion<br />
for Elderly with Mild Cognitive Impairment<br />
H.-M. Gross, C. Schroeter, S. Mueller, M. Volkhardt, E. Einhorn<br />
Neuroinformatics and Cognitive Robotics<br />
Ilmenau University of Technology, Germany<br />
A. Bley, Ch. Martin, T. Langner, M. Merten<br />
MetraLabs GmbH Ilmenau, Germany<br />
The paper presents<br />
� main system requirements derived from<br />
studies with the end-user target groups<br />
(elderly, relatives, caregivers)<br />
� consequences for the hardware design<br />
and functionality of the robot companion<br />
and its system architecture<br />
� a key technology for HRI in home<br />
environments – the autonomous user<br />
tracking and searching<br />
� results of already conducted and ongoing<br />
functionality tests with a particular focus<br />
on experimental results in autonomous<br />
user search<br />
89 years old lady in interaction with the<br />
mobile home robot companion developed in<br />
the European FP7 project CompanionAble<br />
11:00–11:15 WeBT1.7<br />
Whole-Body Contact Manipulation Using Tactile<br />
Information for the Nursing-Care Assistant Robot RIBA<br />
Toshiharu Mukai, Shinya Hirano, Morio Yoshida, and<br />
Hiromichi Nakashima<br />
RTC, RIKEN, Japan<br />
Shijie Guo<br />
SR Laboratorym Tokai Rubber Industries, Japan<br />
Yoshikazu Hayakawa<br />
Dept. of Mechanical Science and Engineering, Nagoya Univ., Japan<br />
• Whole-body contact manipulation<br />
method for nursing-care assistant robots<br />
in direct contact with patients is<br />
proposed.<br />
• This method preserves the conditions of<br />
surface contact, while manipulating the<br />
object’s position and orientation.<br />
• Information on the surface contact is<br />
obtained from tactile sensors mounted<br />
on the robot arms.<br />
• Results of basic experiments are also<br />
provided.<br />
10:55–11:00 WeBT1.6<br />
Wheelchair Navigation Assisted by Humanmachine<br />
Shared-control and P300-based Brain<br />
Computer Interface<br />
Ana C. Lopes, Gabriel Pires, Luís Vaz, and Urbano Nunes<br />
Institute for Systems and Robotics, University of Coimbra, Portugal<br />
• A Two-layer shared-control approach for<br />
assistive mobile robots, using a brain<br />
computer interface (BCI) is presented;<br />
• A P300-based paradigm that allows the<br />
selection of brain-actuated commands to<br />
steer the robotic wheelchair is proposed;<br />
• A virtual-constraint layer is responsible for<br />
validating user commands, based on<br />
context aware restrictions;<br />
• An intent-matching layer is responsible for<br />
determining the suitable steering<br />
command.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–114–<br />
Overview of Robchair: ISR-UC<br />
robotic wheelchair platform<br />
11:15–11:30 WeBT1.8<br />
Should Robots or People Do These Jobs?<br />
A Survey of Robotics Experts and Non-Experts<br />
About Which Jobs Robots Should Do<br />
Wendy Ju and Leila Takayama<br />
Willow Garage, USA<br />
• This study identifies occupational predictors for which jobs robots could<br />
or should do.<br />
• Surveyed robot experts and non-experts (N=392, which includes 134<br />
robotics experts and 258 non-experts )<br />
• Experts have more nuanced and guardedly optimistic view of robot<br />
capabilities.<br />
• Experts are more likely to focus on an occupation’s perceptual<br />
requirements in determining robot capability.<br />
• Non-experts are more skeptical about the robot’s ability to memorize or<br />
interact sociably.
Session WeBT2 Continental Parlor 2 Wednesday, September 28, <strong>2011</strong>, 10:00–11:30<br />
Estimation & Sensor Fusion<br />
Chair Thierry Peynot, The Univ. of Sydney<br />
Co-Chair Agostino Martinelli, INRIA Grenoble-Rhone-Alpes<br />
10:00–10:15 WeBT2.1<br />
Vision-Aided Inertial Navigation: Closed-Form<br />
Determination of Scale, Speed and Attitude<br />
Agostino Martinelli, Chiara Troiani and Alessandro Renzaglia<br />
INRIA, Rhone Alpes, France<br />
• Algorithm based on a<br />
closed-form solution able to<br />
determine speed, attitude<br />
and absolute scale in a<br />
short time interval.<br />
• No prior knowledge or<br />
initialization required.<br />
• Only 4 camera poses and<br />
one single feature are<br />
necessary for the previous<br />
determination (or 2 features<br />
and 3 poses)<br />
• Implementation on a real<br />
quadrotor<br />
10:30–10:45 WeBT2.3<br />
Time-Varying Complementary Filtering for<br />
Attitude Estimation<br />
Evan Chang-Siu , Masayoshi Tomizuka<br />
Dept. of Mechanical Engineering, University of California Berkeley, USA<br />
Kyoungchul Kong<br />
Dept. of Mechanical Engineering, Sogang University, Korea<br />
• Robust method to estimate posture angle<br />
in a single degree of freedom rigid body.<br />
• Applies a complementary filter to a<br />
gyroscope and accelerometer for attitude<br />
estimation.<br />
• Adapts cut off frequency of<br />
complementary filter to achieve improved<br />
performance over a standard<br />
complementary filter.<br />
• Discusses stability and provides<br />
experimental results.<br />
Diagram of TVCF method<br />
10:15–10:30 WeBT2.2<br />
Attitude Determination Framework<br />
by Globally and Asymptotically Stable Bias Error Estimation<br />
with Disturbance Attenuation and Rejection<br />
Hideaki YAMATO and Takayuki FURUTA<br />
Future Robotics Technology Center, Chiba Institute of Technology, Japan<br />
Ken TOMIYAMA<br />
The Department of Advanced Robotics, Chiba Institute of Technology, Japan<br />
Attitude determination framework for costeffective<br />
and small-size attitude sensor<br />
units proposed, featuring<br />
• globally and asymptotically stable<br />
estimation of constant bias error of rate<br />
sensors by a quaternion feedback<br />
• disturbance attenuation as well as<br />
rejection by a disturbance evaluation<br />
scheme (existence and strength) and the<br />
conventional vector matching method<br />
• low computational effort suitable for costeffective<br />
motion sensor units<br />
• fundamental improvement of attitude<br />
error property as shown in the figure.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–115–<br />
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Comparison of error properties: (a) by<br />
gyroscope sensors, (b) by accelerometers<br />
& magnetometers, and (c) by<br />
algorithm estimation proposed<br />
10:45–10:50 WeBT2.4<br />
A Sensor Fusion Approach to Angle and Angular<br />
Rate Estimation<br />
Daniel Kubus and Friedrich M. Wahl<br />
Institut fuer Robotik und Prozessinformatik, TU Braunschweig, Germany<br />
• Fusion of low-cost encoder and MEMS<br />
angular rate sensor measurements<br />
• Virtual increase of encoder resolution and<br />
elimination of rate sensor drift<br />
• Resolution gain of up to factor 10;<br />
outperforms standard Kalman filter<br />
• Easy to use, low computational<br />
requirements<br />
Sinusoidal trajectory. True angle,<br />
encoder output(dotted), and filter<br />
output(dashed).
Session WeBT2 Continental Parlor 2 Wednesday, September 28, <strong>2011</strong>, 10:00–11:30<br />
Estimation & Sensor Fusion<br />
Chair Thierry Peynot, The Univ. of Sydney<br />
Co-Chair Agostino Martinelli, INRIA Grenoble-Rhone-Alpes<br />
10:50–10:55 WeBT2.5<br />
Combining Multiple Sensor Modalities for a<br />
Localisation Robust to Smoke<br />
Christopher Brunner, Thierry Peynot and Teresa Vidal-Calleja<br />
ACFR, The University of Sydney, Australia<br />
• Camera localisation robust to smoke/fog<br />
• Visible and IR cameras used within a<br />
SLAM framework<br />
• Robust localisation in smoke is shown:<br />
• using IR camera only<br />
• with Visual and IR cameras<br />
• with Visual and IR cameras plus<br />
visual data quality evaluation (visual<br />
images are discarded when most<br />
affected by smoke, mitigating errors)<br />
• Experimental validation with outdoor<br />
ground robot<br />
• Results show accuracy of proposed<br />
methods by comparing multiple<br />
trajectories with dGPS/INS ground truth<br />
11:00–11:15 WeBT2.7<br />
An Improved Pedestrian Inertial Navigation<br />
System for Indoor Environments<br />
Sylvie Lamy-Perbal, Mehdi Boukallel and Nadir Castañeda<br />
CEA List, Sensory and Ambient Interfaces Laboratory, France<br />
• An improved shoe-mounted inertial<br />
navigation system with submeter accuracy<br />
• Zero Velocity Update (ZUPT) and Angular<br />
update algorithms (AUPT) for drift<br />
compensation<br />
• Efficient foot stance phase detection<br />
system without additional sensors<br />
• In situ experimentation and validation<br />
10:55–11:00 WeBT2.6<br />
Multisensor Data Fusion for Robust Pose<br />
Estimation of a Six-Legged Walking Robot<br />
Annett Chilian, Heiko Hirschmüller, Martin Görner<br />
DLR German Aerospace Center, Germany<br />
• Fusion of IMU data, 3D visual odometry<br />
and 3D leg odometry using an indirect<br />
feedback information filter<br />
• Error estimate of visual odometry<br />
measurements is computed and<br />
considered in the filtering process<br />
• State cloning is applied to correctly fuse<br />
relative odometry measurements<br />
• Fusion result is robust against failures of<br />
the visual odometry caused by poor<br />
visual conditions<br />
• Experimental results prove the<br />
robustness and accuracy of the data<br />
fusion process<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–116–<br />
Fusion result compared to ground<br />
truth, visual and leg odometry under<br />
poor visual conditions<br />
11:15–11:30 WeBT2.8<br />
Learning the Delaunay Triangulation of<br />
Landmarks From a Distance Ordering Sensor<br />
Max Katsev and Steven M. LaValle<br />
Department of Computer Science,<br />
University of Illinois at Urbana-Champaign, USA<br />
• Limited sensing: for any two landmarks,<br />
the robot can detect which one is closer<br />
• No other information: no exact distance,<br />
no landmark locations, no odometry<br />
• Algorithms for the robot to navigate to<br />
certain points and to learn global<br />
topological information about landmarks<br />
• Can be used to solve simple tasks, e.g.,<br />
convex hull computation<br />
The environment is split into<br />
regions uniquely identified by the<br />
distance order of landmarks
Session WeBT3 Continental Parlor 3 Wednesday, September 28, <strong>2011</strong>, 10:00–11:30<br />
Surgical Robotics<br />
Chair Wan Kyun Chung, POSTECH<br />
Co-Chair Sarthak Misra, Univ. of Twente<br />
10:00–10:15 WeBT3.1<br />
A Bimanual Teleoperated System for Endonasal<br />
Skull Base Surgery<br />
Jessica Burgner1 , Philip J. Swaney1 , D. Caleb Rucker1 ,<br />
Hunter B. Gilbert1 , Scott T. Nill1 , Paul T. Russell III2 ,<br />
Kyle D. Weaver3 , and Robert J. Webster III1,2 1 Department of Mechanical Engineering, Vanderbilt University, USA<br />
2 Department of Otolaryngology, Vanderbilt University Medical Center, USA<br />
3 Department of Neurosurgery, Vanderbilt University Medical Center, USA<br />
• Robotic surgery through a single<br />
nostril<br />
• “Tentacle-Like” concentric tube<br />
continuum robot manipulators<br />
• Presentation covers:<br />
- Surgical requirements<br />
- Workspace from patient data<br />
- Optimal end effector design<br />
- Actuation unit design<br />
- Jacobian-based teleoperation<br />
• Cadaver experiment results<br />
10:30–10:45 WeBT3.3<br />
Evaluation of Command Modes of an Assistance<br />
Robot for Middle Ear Surgery<br />
Guillaume Kazmitcheff1 , Mathieu Miroir1 , Yann Nguyen1 , Charlotte<br />
Célérier1 , Stéphane Mazalaigue3 , Evelyne Ferrary1,2 , Olivier<br />
Sterkers1,2 and Alexis Bozorg-Grayeli1,2 1 UMR-S 867 / Inserm / University Paris 7, France<br />
2 AP-HP Hôpital Beaujon, France<br />
3 Collin SA, France<br />
• Tele operated system : RobOtol<br />
• New prototype : improved controller system , mechanical enhancement<br />
and HMI concept<br />
• 2 command modes : Position-Velocity and Position-Position<br />
• Robot evaluation by tasks specific to middle ear surgery<br />
10:15–10:30 WeBT3.2<br />
Automated Surgical Planning and Evaluation<br />
Algorithm for Spinal Fusion Surgery with Three-<br />
Dimensional Pedicle Model<br />
Jongwon Lee1 , Sungmin Kim2 , Young Soo Kim3 ,<br />
Wan Kyun Chung1 , and Minjun Kim1 1Department of Mechanical Engineering, POSTECH, KOREA<br />
2Department of Biomedical Engineering, Hanyang Univ., KOREA<br />
3Department of Neurosurgery, School of Medicine, Hanyang Univ., KOREA<br />
• Autonomous preoperative planning<br />
framework for lumbar spinal fusion was<br />
proposed<br />
• It augmented a novel functionality for<br />
suggesting optimal insertion trajectories<br />
of pedicle screws and its size (w.r.t.<br />
operational safety and screw-vertebra<br />
interface strength)<br />
• It is based on 3-D accurate spinal pedicle<br />
data segmented from patient’s CT images<br />
• It has been tested on 68 spinal pedicles<br />
of 8 patients, and successfully applied<br />
resulting in final safety margin of<br />
2.11�0.17mm<br />
5<br />
mm<br />
65<br />
70<br />
75<br />
80<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–117–<br />
mm<br />
20<br />
15<br />
10<br />
45 50 55 60 65 70 75<br />
mm<br />
Insertion trajectories of the pedicle<br />
screws computed by the proposed<br />
surgical planner<br />
10:45–10:50 WeBT3.4<br />
Towards validation of robotic surgery training<br />
assessment across training platforms<br />
Yixin Gao, Mert Sedef, Amod Jog, Peter Peng,<br />
Michael Choti, Gregory Hager, Jeff Berkley, Rajesh Kumar<br />
Computer Science, Johns Hopkins University, USA<br />
MIMIC Technologies, Inc. USA<br />
• Automated skill assessment in robotic<br />
surgery required for unsupervised<br />
feedback and simulated task<br />
performance<br />
• First cross-platform analysis of motion<br />
data to distinguish novice and<br />
proficient user performances<br />
• Experiments with da Vinci dry-lab and<br />
dV-Trainer simulation environment with<br />
users of varying skills are reported<br />
The prototype cross-platform<br />
architecture
Session WeBT3 Continental Parlor 3 Wednesday, September 28, <strong>2011</strong>, 10:00–11:30<br />
Surgical Robotics<br />
Chair Wan Kyun Chung, POSTECH<br />
Co-Chair Sarthak Misra, Univ. of Twente<br />
10:50–10:55 WeBT3.5<br />
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11:00–11:15 WeBT3.7<br />
Mechanics of Needle-Tissue Interaction<br />
Roy J. Roesthuis, Youri R.J. van Veen,<br />
Alex Jahya and Sarthak Misra<br />
University of Twente, The Netherlands<br />
• Understanding the mechanics of<br />
needle-tissue interaction is required<br />
for pre-operative path planning and<br />
needle steering.<br />
• Needle-tissue interaction forces are<br />
identified for bevel-tipped needles<br />
inserted into a gelatin phantom.<br />
• The interaction forces are used to<br />
predict needle deflection using a<br />
mechanics-based model.<br />
• The average error between model<br />
and experiments for 100 mm<br />
insertion is 0.179 mm.<br />
Interaction forces on beveltipped<br />
needle during insertion<br />
Experimental setup to measure<br />
needle-tissue friction (top view)<br />
10:55–11:00 WeBT3.6<br />
An Analytical Model for Deflection of Flexible<br />
Needles During Needle Insertion<br />
Ali Asadian 1,2 , Mehrdad R. Kermani 1,2 , and Rajni V. Patel 1,2,3<br />
1 Canadian Surgical Technologies & Advanced Robotics (CSTAR),<br />
2 Dept. of Elect. & Comp. Eng., 3 Dept. of Surgery,<br />
University of Western Ontario, London, Ontario, Canada<br />
• The use of flexible needles in<br />
percutaneous interventions<br />
necessitates modeling of the<br />
generated curved trajectories.<br />
• A feasible model is essential<br />
for path planning (needle<br />
guidance) and simulation<br />
(clinician training).<br />
• Euler-Bernoulli beam theory<br />
and Green’s functions are<br />
used to obtain a needle’s<br />
planar deflection using.<br />
• The impact of tissue elasticity<br />
and needle-tissue friction is<br />
studied.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–118–<br />
Effective insertion forces acting on the<br />
curved axis and the 2D needle deflection<br />
with respect to its insertion depth<br />
11:15–11:30 WeBT3.8<br />
Feasibility Study of an Optically Actuated<br />
MR-compatible Active Needle<br />
Seok Chang Ryu1 , Pierre Renaud2 , Richard J. Black3 ,<br />
Bruce L. Daniel4 , and Mark R. Cutkosky1 1Center for Design Research, Stanford University, USA<br />
2LSIIT, Strasbourg University-CNRS-INSA, France<br />
3Intelligent Fiber Optic System Corporation, USA<br />
4Radiology, Stanford University, USA<br />
• We propose a novel MR-compatible,<br />
optically steerable active needle to<br />
improve needle maneuverability, which<br />
features an active flexure near tip for<br />
direct tip orientation control<br />
• The active flexure can be bent with an<br />
optically heated SMA wire by laser<br />
delivered through embedded optical<br />
fibers. It also includes optical curvature<br />
and temperature sensors<br />
Fig. Inner stylet of the proposed<br />
active biopsy needle<br />
• Mechanical behavior of the proposed needle was evaluated, and about<br />
10� of tip bending angle could be achieved<br />
• Optical heating requirements were investigated and side heating<br />
methods were proposed for better needle dynamics
Session WeBT4 Continental Ballroom 4 Wednesday, September 28, <strong>2011</strong>, 10:00–11:30<br />
Symposium: Hardware and Software Design for Haptic Systems<br />
Chair Yasuyoshi Yokokohji, Kobe Univ.<br />
Co-Chair Katherine J. Kuchenbecker, Univ. of Pennsylvania<br />
10:00–10:15 WeBT4.1<br />
A Friction Differential and Cable<br />
Transmission Design for a 3-DOF Haptic<br />
Device with Spherical Kinematics<br />
Reuben Brewer 1 , Adam Leeper 1 , and J. Kenneth Salisbury 1,2,3<br />
Departments of Mech. Engineering 1 , Computer Science 2 , and Surgery 3<br />
Stanford University, USA<br />
• New design for a 3-DOF haptic<br />
device with spherical kinematics<br />
(pitch, yaw, and prismatic radial).<br />
• All motors grounded to reduce<br />
inertia and increase compactness<br />
near user’s hand.<br />
• Aluminum-aluminum friction<br />
differential actuates pitch and<br />
yaw robustly.<br />
• Cable transmission routes<br />
through the center of the<br />
differential to actuate the<br />
prismatic, radial DOF.<br />
10:30–10:45 WeBT4.3<br />
Coaxial Needle Insertion Assistant<br />
for Epidural Puncture<br />
Yoshihiko Koseki and Kiyoyuki Chinzei<br />
Advanced Industrial Science and Technology (AIST), Japan<br />
Danilo De Lorenzo<br />
Politecnico di Milano, Bioengineering Department, Italy<br />
Allison M. Okamura<br />
Department of Mechanical Engineering, Stanford University, USA<br />
• The coaxial needle separates cutting force<br />
at the needle tip from shear friction on the<br />
needle shaft<br />
• A robotic assist presents the cutting force<br />
in order to enhance physician’s perception<br />
during epidural puncture<br />
• The ratio of successful to unsuccessful<br />
puncture detection was higher with the<br />
assistant than without<br />
• Users were more confident that they could<br />
perceive the moment of puncture<br />
Operator<br />
Operator<br />
Force<br />
Inner Needle<br />
Outer Needle<br />
Friction Force<br />
Cutting Force<br />
Actuator<br />
Assistant<br />
Force<br />
Epidural Space<br />
Spinal Cord<br />
Lumbar Spine<br />
10:15–10:30 WeBT4.2<br />
Hi5: a versatile dual-wrist device to study<br />
human-human interaction and bimanual control<br />
Alejandro Melendez-Calderon, Lorenzo Bagutti, Brice Pedrono<br />
and Etienne Burdet<br />
Department of Bioengineering, Imperial College London, United Kingdom<br />
• Human-human and bimanual haptic<br />
interactions involve complex control<br />
requiring coordination of redundant<br />
muscle systems.<br />
• Wrist flexion/extension movements<br />
simplify the study of muscle control and<br />
redundancy.<br />
• Hi5 allows to measure the kinematics,<br />
dynamics and muscular activity, allowing<br />
systematic analysis of redundant<br />
strategies.<br />
• The simple design enables quick and<br />
easy reconfiguration of the interface to<br />
investigate symmetric or asymmetric<br />
tasks on both human-human or<br />
bimanual interaction experiments.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–119–<br />
Removable carbon fiber tube<br />
Adjustable position<br />
Human-human Bimanual<br />
10:45–10:50 WeBT4.4<br />
Two-Hands Are Better Than One: Assisting<br />
Users with Multi-Robot Manipulation Tasks<br />
Bennie Lewis and Gita Sukthankar<br />
Department of EECS, University of Central Florida, USA<br />
• User interface for reducing operator<br />
workload while controlling a team of<br />
robots with manipulators.<br />
• Robots leverage information from<br />
commands that the user has executed in<br />
the past.<br />
• Two new modes which can be invoked by<br />
the user or autonomously by the interface.<br />
• Shows statistically-significant<br />
improvements in the time required to<br />
perform foraging scenarios
Session WeBT4 Continental Ballroom 4 Wednesday, September 28, <strong>2011</strong>, 10:00–11:30<br />
Symposium: Hardware and Software Design for Haptic Systems<br />
Chair Yasuyoshi Yokokohji, Kobe Univ.<br />
Co-Chair Katherine J. Kuchenbecker, Univ. of Pennsylvania<br />
10:50–10:55 WeBT4.5<br />
3-DOF Haptic Feedback for Assisted<br />
Driving of an Omnidirectional Wheelchair<br />
Quinton M. Christensen and Stephen Mascaro<br />
Department of Mechanical Engineering, University of Utah, USA<br />
• The mobility limitations of<br />
conventional wheelchairs have<br />
prompted development of<br />
omnidirectional wheelchairs.<br />
• The 3-DOF Hapic Joystick shown<br />
provides a natural and intuitive<br />
controller for these wheelchairs.<br />
• This joystick provides force feedback<br />
corresponding to all degrees of<br />
control input.<br />
• This controller can assist in obstacle<br />
avoidance and driver training and<br />
has other research applications.<br />
Functional Prototype of the<br />
3-DOF Haptic Joystick<br />
11:00–11:15 WeBT4.7<br />
Asynchronous Haptic Simulation of Contacting<br />
Deformable Objects with Variable Stiffness<br />
Igor Peterlik, Christian Duriez, Stephane Cotin<br />
INRIA Lille Nord Europe, France<br />
• In complex simulations rapid variation of<br />
force can be introduced due to stiff nonlinear<br />
deformations<br />
• We present asynchronous technique for<br />
constraint-based haptic rendering of<br />
deformable or rigid objects in contact<br />
• The method is demonstrated with two<br />
deformable objects (volumetric and<br />
curved), each having different stiffness,<br />
simulated at different frequencies<br />
• The experiments shows that our<br />
multirate method results in stable,<br />
coherent and precise haptic rendering of<br />
contacts between the objects simulated<br />
asynchronously<br />
Volumic object simulated at low<br />
frequency (50 Hz), curved<br />
object (snap, thread) simulated<br />
at high frequncy (1000 Hz)<br />
10:55–11:00 WeBT4.6<br />
Configuration-based Optimization for Six<br />
Degree-of-Freedom Haptic Rendering<br />
Using Sphere-trees<br />
Xin Zhang, Dangxiao Wang and Yuru Zhang<br />
the State Key Lab of Virtual Reality Technology and Systems<br />
Beihang University, China<br />
Jing Xiao<br />
The Department of Computer Science,<br />
University of North Carolina – Charlotte, USA<br />
• Treated the haptic interaction between a<br />
rigid tool and rigid objects in a virtual<br />
environment as a constrained optimization<br />
problem in the 6-D configuration space of<br />
the tool.<br />
• Modeled the tool and objects as sphere<br />
trees for fast detection of multiple<br />
collisions and formulation of contact<br />
constraints.<br />
• Applied the approach to dental surgery<br />
simulation. Results show stable interaction<br />
in the update rate of 1kHz.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–120–<br />
Six DoF haptic rendering for<br />
dental surgery simulation<br />
11:15–11:30 WeBT4.8<br />
Proxy Method for Fast Haptic Rendering from<br />
Time Varying Point Clouds<br />
Fredrik Rydén, Sina Nia Kosari and Howard Jay Chizeck<br />
Department of Electrical Engineering, University of Washington, USA.<br />
• A novel algorithm for haptic rendering from<br />
time varying point clouds in real time.<br />
• The algorithm can run on a regular laptop<br />
(Core 2 Duo or better) and successfully<br />
render haptic forces at 1000 Hz.<br />
• The user can easily feel features that are<br />
less than 5 mm.<br />
• Algorithm is based on the extension of the<br />
haptic proxy idea to dynamic point clouds.<br />
• Potential application in gaming and telerobotics<br />
applications including robotic<br />
assisted surgery.<br />
The algorithm running on a<br />
regular laptop. The size of the<br />
proxy is an important parameter<br />
in haptic interaction and can here<br />
be compared to an apple.
Session WeBT5 Continental Ballroom 5 Wednesday, September 28, <strong>2011</strong>, 10:00–11:30<br />
Symposium: Foundations and Future Prospects of Sampling-based Motion Planning<br />
Chair Kostas E. Bekris, Univ. of Nevada, Reno<br />
Co-Chair Steven M LaValle, Univ. of Illinois<br />
10:00–10:15 WeBT5.1*<br />
Semi-Plenary Invited Talk: Sampling-based Algorithms<br />
for General Motion Control Problems: Recent<br />
Advances and New Challenges<br />
Emilio Frazzoli, Massachusetts Institute of Technology<br />
10:30–10:45 WeBT5.3<br />
An Obstacle-responsive Technique<br />
for the Management and Distribution<br />
of Local Rapidly-exploring Random Trees<br />
Nathan A. Wedge and Michael S. Branicky<br />
Department of Electrical Engineering and Computer Science,<br />
Case Western Reserve University, Cleveland, Ohio, USA<br />
• Previously-introduced PART (Path-length<br />
Annexed Random Tree) builds on RRT by<br />
addressing pathologically-difficult cases<br />
• PART adds threshold on path length to<br />
partition search into local trees, providing<br />
multiple neighbors for diverse expansion<br />
• APART (Adaptive PART) supplements<br />
PART by adjusting thresholds for each<br />
local tree based on obstacles<br />
• Adaptive mechanism focuses effort near<br />
presumed difficult regions and improves<br />
performance over RRT and PART on hard<br />
problems via apt threshold values<br />
APART’s search focuses on tricky<br />
areas like narrow obstacle edges<br />
10:15–10:30 WeBT5.2<br />
Semi-Plenary Invited Talk: Sampling-based Algorithms<br />
for General Motion Control Problems: Recent<br />
Advances and New Challenges<br />
Emilio Frazzoli, Massachusetts Institute of Technology<br />
10:45–10:50 WeBT5.4<br />
Finding Critical Changes in<br />
Dynamic Configuration Spaces<br />
Yanyan Lu and Jyh-Ming Lien<br />
Department of Computer Science, George Mason University, USA<br />
• Probabilistic methods plan in<br />
configuration-time space and do not<br />
reuse computation between<br />
configuration space slices.<br />
• There exist methods reuse the valid<br />
portion of roadmap but only update<br />
the roadmap at fixed times.<br />
• Our method identifies critical changes<br />
in the configuration space and reuses<br />
valid edges and nodes, and updates<br />
roadmap only at the critical times.<br />
• Our planner provides better<br />
completeness and is at least 10 times<br />
faster than existing popular planners.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–121–<br />
Topological changes of configuration<br />
free space from t0 to t2
Session WeBT5 Continental Ballroom 5 Wednesday, September 28, <strong>2011</strong>, 10:00–11:30<br />
Symposium: Foundations and Future Prospects of Sampling-based Motion Planning<br />
Chair Kostas E. Bekris, Univ. of Nevada, Reno<br />
Co-Chair Steven M LaValle, Univ. of Illinois<br />
10:50–10:55 WeBT5.5<br />
Toggle PRM:<br />
A Simultaneous Mapping of C free and C obstacle<br />
-A Study in 2D-<br />
Jory Denny and Nancy M. Amato<br />
Computer Science and Engineering, Texas A&M University, USA<br />
• New paradigm for PRM samplingbased<br />
planner<br />
• A simultaneous, coordinated mapping<br />
of both C free and C obst<br />
• Theoretical and experimental<br />
improvement for sampling in narrow<br />
passages<br />
• Improved efficiency in terms of number<br />
of validity checks<br />
Uniform OBPRM<br />
Toggle PRM Free Toggle PRM Block<br />
-Example map constructions- Toggle PRM<br />
has highest density in the passage<br />
11:00–11:15 WeBT5.7<br />
EG-RRT: Environment-Guided Random Trees<br />
for Kinodynamic Motion Planning<br />
with Uncertainty and Obstacles<br />
Léonard Jaillet*, Judy Hoffman+, Jur van den Bergº,<br />
Pieter Abbeel+, Josep M. Porta* and Ken Goldberg+<br />
* Institut de Robòtica i Informàtica Industrial, Barcelona, Spain<br />
+ University of California, Berkeley, USA<br />
º University of North Carolina, Chapel Hill, USA<br />
• EG-RRT is an Environment-Guided<br />
variant of RRT designed for<br />
kinodynamic systems<br />
• This hybrid planner integrates the<br />
local limitations of the system<br />
dynamics as well as the risk of leading<br />
to unproductive expansions<br />
• The method may be enhanced by a<br />
cost model based on the LQG-MP<br />
framework<br />
• It produces paths faster and with a<br />
lower probability of collision under<br />
uncertainty in control and sensing<br />
Comparison of four RRT-based<br />
planners. EG-RRT in the lower-right<br />
outperforms the other variants<br />
10:55–11:00 WeBT5.6<br />
Sampling Heuristics for Optimal Motion<br />
Planning in High Dimensions<br />
Baris Akgun and Mike Stilmand<br />
The School of Interactive Computing, Georgia Institute of Technology, USA<br />
• Scenario: Allocated time for planning<br />
• Find an initial path quickly and iteratively<br />
decrease its cost fast<br />
• Introduce heuristics to RRT* algorithm to<br />
balance exploration and exploitation<br />
• Implement a bidirectional version for<br />
efficiency in high-dimensions<br />
• Local Biasing: Exploitation<br />
• Node rejection: Efficiency<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–122–<br />
Trees produced in same duration<br />
Top: With plain RRT*<br />
Bottom: With combined heuristics<br />
11:15–11:30 WeBT5.8<br />
Planning Humanlike Actions in Blending Spaces<br />
Yazhou Huang, Mentar Mahmudi<br />
and Marcelo Kallmann<br />
School of Engineering, University of California Merced, USA<br />
• We introduce an approach for enabling<br />
sampling-based planners to compute<br />
motions with humanlike appearance.<br />
• The search space of our planner is<br />
defined on a space of blendable motion<br />
capture examples, generating solutions<br />
similar to the original mocap examples.<br />
• The method is combined with a<br />
locomotion planner in order to generate<br />
whole-body motions for generic action<br />
execution among obstacles.
Session WeBT6 Continental Ballroom 6 Wednesday, September 28, <strong>2011</strong>, 10:00–11:30<br />
Symposium: Aerial Robotics: Control and Planning<br />
Chair Srikanth Saripalli, Arizona State Univ.<br />
Co-Chair Pieter Abbeel, UC Berkeley<br />
10:00–10:15 WeBT6.1<br />
UAV Rotorcraft in Compliant Contact:<br />
Stability Analysis and Simulation<br />
Paul Pounds and Aaron Dollar<br />
Department of Mechanical Engineering, Yale University, USA<br />
• End-effector compliance can allow for safe<br />
interactions of a hovering vehicle with<br />
objects, without structural contact<br />
knowledge<br />
• The 6-DOF dynamics of a helicopter with<br />
nonlinear PD control remain stable for a<br />
range of end-effector stiffnesses and<br />
displacements from aircraft center of<br />
gravity<br />
• Simulation of the system for archetypical<br />
end-effector configurations demonstrate<br />
stability performance predicted analytically<br />
�<br />
�<br />
F<br />
�<br />
Unforced position<br />
Helicopter coupled to ground<br />
via compliance<br />
10:30–10:45 WeBT6.3<br />
Flight Control for Target Seeking by<br />
13 gram Ornithopter<br />
Stanley S. Baek, Fernando L. Garcia Bermudez,<br />
and Ronald S. Fearing<br />
Dept. of EECS, University of California, Berkeley, United States<br />
• Autonomous flight control of a 13 gram<br />
ornithopter flying toward a target using<br />
onboard sensing and computation only<br />
• 1.0 gram control electronics including a<br />
microcontroller, inertial and visual<br />
sensors, wireless communication, and<br />
motor drivers.<br />
• Simplified aerodynamic model of<br />
ornithopter flight to reduce the order of<br />
the control system.<br />
• Dead-reckoning algorithm to recover<br />
from temporary loss of the target.<br />
• Landed within a radius of 0.5 m from the<br />
target with more than 85% success (N =<br />
20)<br />
target<br />
Figure caption is optional,<br />
use Arial Narrow 20pt<br />
10:15–10:30 WeBT6.2<br />
Design, Modeling, Estimation and Control<br />
for Aerial Grasping and Manipulation<br />
Daniel Mellinger, Quentin Lindsey,<br />
Michael Shomin, and Vijay Kumar<br />
GRASP Lab, University of Pennsylvania, USA<br />
• We present the design of several grippers<br />
that allow quadrotors to pick up and<br />
transport payloads<br />
• We describe methods for estimating and<br />
adapting to the changes in system<br />
dynamics due to the grasped payloads<br />
• We present experimental results for the<br />
estimation methods and show<br />
considerable improvement in tracking<br />
performance with the inclusion of the<br />
estimated parameters<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–123–<br />
Quadrotors Tranporting Payloads<br />
10:45–10:50 WeBT6.4<br />
Embedded robust nonlinear control for a four-rotor<br />
rotorcraft: Validation in real-time with wind gusts<br />
Laura E. Muñoz, Pedro Castillo, Guillaume Sanahuja<br />
Heudiasyc UMR CNRS 6599, UTC France<br />
Omar Santos<br />
CITIS - Universidad Autónoma del Estado de Hidalgo, México<br />
• An embedded robust nonlinear controller to stabilize a fourrotor<br />
rotorcraft in presence of bounded disturbances is<br />
proposed.<br />
• The stability analysis of the close-loop system is proved<br />
using the Lyapunov theory.<br />
• A new prototype with an embedded control system has been<br />
developed.<br />
• The proposed algorithms were validated through simulation<br />
and real time experiments.
Session WeBT6 Continental Ballroom 6 Wednesday, September 28, <strong>2011</strong>, 10:00–11:30<br />
Symposium: Aerial Robotics: Control and Planning<br />
Chair Srikanth Saripalli, Arizona State Univ.<br />
Co-Chair Pieter Abbeel, UC Berkeley<br />
10:50–10:55 WeBT6.5<br />
Differential Flatness Based Control of a<br />
Rotorcraft For Aggressive Maneuvers<br />
Jeff Ferrin, Robert Leishman and Tim McLain<br />
Mechanical Engineering, Brigham Young University, U.S.A.<br />
Randy Beard<br />
Electrical Engineering, Brigham Young University, U.S.A.<br />
• A differential flatness based control<br />
method for rotorcraft is presented<br />
• Feed forward term allows for improved<br />
path following performance with large<br />
pitch and roll angles<br />
• The control method also allows for<br />
independent yaw control while flying the<br />
path<br />
• Hardware results demonstrate substantial<br />
improvement over standard feedback<br />
control methods (no feed forward term)<br />
Pitch and roll angles of aircraft<br />
during aggressive flight<br />
11:00–11:15 WeBT6.7<br />
Magnetic Localization for<br />
Perching UAVs on Powerlines<br />
Joseph Moore and Russ Tedrake<br />
Computer Science and Artificial Intelligence Lab (CSAIL), MIT, USA<br />
• Goal: Perch on powerlines to recharge<br />
• Potential challenges<br />
• Unknown current phase leads to<br />
ambiguity in positions<br />
• Signal strength decreases like<br />
1/distance^3<br />
• Developed onboard magnetometer and<br />
estimation algorithm (using EKF)<br />
• Capable of producing sufficient estimates<br />
for our dynamic perching maneuvers,<br />
which start 4m from wire at 8m/s.<br />
10:55–11:00 WeBT6.6<br />
Robust Embedded Egomotion Estimation<br />
Rainer Voigt, Janosch Nikolic, Christoph Hürzeler,<br />
Stephan Weiss, Laurent Kneip and Roland Siegwart<br />
Autonomous Systems Lab, ETH Zürich, Switzerland<br />
• Robust and efficient egomotion estimation in<br />
challenging industrial environments<br />
• Tight coupling of visual and inertial cues to<br />
cope with poorly or repetitively textured<br />
environments<br />
• Real-time operation on low-power embedded<br />
computing platform<br />
11:15–11:30 WeBT6.8<br />
Persistent Surveillance with a Team of MAVs<br />
Nathan Michael and Kartik Mohta<br />
GRASP Laboratory, University of Pennsylvania, USA<br />
Ethan Stump<br />
Army Research Laboratory, USA<br />
• Consider the problem of deploying a team<br />
of aerial robots to persistently survey<br />
discrete locations of interest<br />
• Formulate the problem as a Vehicle<br />
Routing Problem with Time Windows and<br />
detail a real-time optimal solution<br />
methodology<br />
• Detail a system architecture capable of<br />
addressing the problem of persistent<br />
surveillance with a team of autonomous<br />
micro-aerial vehicles<br />
• Report simulation and experimental<br />
results for teams of quadrotors both<br />
indoors and outdoors<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–124–<br />
Two quadrotors are deployed<br />
autonomously to survey discrete<br />
interest points
Session WeBT7 Continental Parlor 7 Wednesday, September 28, <strong>2011</strong>, 10:00–11:30<br />
Passive Walking & Leg-Wheeled Robots<br />
Chair Dong-Soo Kwon, KAIST<br />
Co-Chair Martin Buehler, iRobot<br />
10:00–10:15 WeBT7.1<br />
Nonlinear Structure of Escape-Times to Falls for<br />
a Passive Dynamic Walker in an Irregular Slope<br />
(A, B, C) (A)<br />
Hiromichi Suetani and Aiko Ideta<br />
(A) Department of Physics and Astronomy, Kagoshima University, Japan<br />
(B) Decoding and Controlling Brain Information, PRESTO, JST, Japan<br />
(C) RIKEN Advanced Science Institute, Japan<br />
(D)<br />
Jun Morimoto<br />
(D)<br />
Dept. of BRI, ATR Computational Neuroscience Laboratories, Japan<br />
• Passive dynamic walkers (PDWs) use the<br />
“limit cycle attractors” for their gait. But,<br />
the area of their basins is very small, i.e.,<br />
PDW is very susceptible to noise.<br />
• A set of escape-times from walking to<br />
falling of PDW with noise exhibits a<br />
characteristic nonlinear structure in the<br />
state space, which can be considered as<br />
the “basin ruin” of the noiseless PDW.<br />
• Analyzing such a nonlinear structure<br />
using a multi-class Support Vector<br />
Machines and Canonical Correlation<br />
Analysis<br />
• Nonlinear structure of this escape-times<br />
may be useful for reinforcement<br />
learning of PDWs.<br />
Escape-times from walking to<br />
falling in a Poincare surface.<br />
Each color indicates the number<br />
of steps left before falling.<br />
10:30–10:45 WeBT7.3<br />
Optimal gait switching for legged locomotion<br />
B. Kersbergen, G.A.D. Lopes,<br />
T.J.J. van den Boom, B. De Schutter and R. Babuska<br />
Delft Center for Systems and Control,<br />
Delft University of Technology, The Netherlands<br />
• The gait space of multi-legged robots is<br />
combinatorial<br />
• We use switching max-plus linear models<br />
to describe legged locomotion<br />
• We present a systematic way to generate<br />
optimal gait transitions, in the sense of<br />
minimizing the foot velocity variation<br />
during stance<br />
• All gait switches are intrinsically safe<br />
Optimal gait switch for an<br />
hexapod robot<br />
10:15–10:30 WeBT7.2<br />
Increasing the Robustness of Acrobot walking<br />
control using compliant mechanisms<br />
Maziar Ahmad Sharbafi and Mohammad Javad Yazdanpanah<br />
and Majid Nili Ahmadabadi<br />
School of Electrical and Computer Engineering, University of Tehran, Iran<br />
• Two controllers are designed based on<br />
Hybrid Zero Dynamics for flat and inclined<br />
surfaces.<br />
• Calculating the difference of force<br />
generated by two controller on inclined<br />
surface<br />
• Finding a nonlinear compliance to<br />
compensate the required force<br />
• Evaluation of the controller on flat surface<br />
• Evaluation of the controller on inclined<br />
surface<br />
• Analyzing the controller performance<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–125–<br />
Walking on slope with HZD based<br />
designed controller for flat surface.<br />
a nonlinear compliance is inserted<br />
to enable it to walk on slope.<br />
10:45–10:50 WeBT7.4<br />
Development and Experiment of a Kneed Biped Walking<br />
Robot Based on Parametric Excitation Principle<br />
Yoshihisa Banno, Kouichi Taji and Yoji Uno<br />
Mechanical Science and Engineering, Nagoya University, Japan<br />
Yuji Harata<br />
Mechanical Systems and Applied Mechanics, Hiroshima University, Japan<br />
• The parametric excitation walking with a<br />
simple periodic reference trajectory was<br />
realized.<br />
• A kneed biped robot based on parametric<br />
excitation principle were developed.<br />
• In the experiment, a sustainable walking<br />
on a level ground was achieved about 17<br />
steps.<br />
• Several angular coordinates of<br />
experimental results were very close to<br />
those of simulation results.<br />
Developed biped robot
Session WeBT7 Continental Parlor 7 Wednesday, September 28, <strong>2011</strong>, 10:00–11:30<br />
Passive Walking & Leg-Wheeled Robots<br />
Chair Dong-Soo Kwon, KAIST<br />
Co-Chair Martin Buehler, iRobot<br />
10:50–10:55 WeBT7.5<br />
Switchblade: An Agile Treaded Rover<br />
Nicholas Morozovsky, Christopher Schmidt-Wetekam,<br />
Thomas Bewley<br />
Mechanical and Aerospace Engineering Department, University of California<br />
San Diego, USA<br />
• Able to traverse rough terrain while<br />
retaining a small form factor for navigating<br />
confined spaces<br />
• Combination of a novel transforming<br />
mechanical design, capable onboard<br />
electronics, and advanced feedback<br />
control algorithms<br />
• Manipulates its center of mass to perform<br />
unique maneuvers<br />
• Well suited for a variety of sociallyrelevant<br />
applications, including<br />
reconnaissance, mine exploration, and<br />
search & rescue<br />
11:00–11:15 WeBT7.7<br />
Robust obstacle crossing of a wheel-legged<br />
mobile robot using minimax force distribution<br />
and self-reconfiguration<br />
Pierre Jarrault, Christophe Grand and Philippe Bidaud<br />
Institut des Systèmes Intelligent et de Robotique<br />
Université Pierre et Marie Curie, France<br />
• Control algorithm for obstacle<br />
crossing with a poly-articulated<br />
wheeled mobile robot<br />
• Stability criterion based on the<br />
smallest force sustainable at the<br />
wheel/ground contact<br />
• Optimization of both the force<br />
distribution and the center of mass<br />
location in order to maximize the<br />
criterion<br />
• Cross-ability analysis of an obstacle<br />
10:55–11:00 WeBT7.6<br />
Passive Dynamic Walking of Combined Rimless<br />
Wheel and Its Speeding-up by Adjustment of<br />
Phase Difference<br />
Ryosuke Inoue, Fumihiko Asano, Daiki Tanaka and Isao Tokuda<br />
School of Infromation Science,<br />
Japan Advanced Institute of Science and Technology, Japan<br />
• A combined rimless wheel (CRW) that<br />
consists of two identical 8-legged rimless<br />
wheels is introduced.<br />
• Numerical simulations show that the<br />
walking speed dramatically increases by<br />
adjustment of the phase difference.<br />
• The validity of the theoretical results is<br />
confirmed by using a prototype<br />
experimental CRW.<br />
• The mechanism of speeding-up is<br />
investigated from the potential barrier<br />
point of view.<br />
11:15–11:30 WeBT7.8<br />
Zero-Moment Point Feedforward Balance<br />
Control of Leg-Wheel Hybrid Structures by using<br />
Input/Output Linearization<br />
Sang-ik An and Dong-Soo Kwon<br />
Mechanical Engineering, KAIST, Korea<br />
• A balance control algorithm is proposed<br />
which maximizes the dynamic stability by<br />
controlling the ZMP to remains at the<br />
geometric center of the supporting<br />
polygon while the robot traverses the<br />
desired trajectory.<br />
• The ZMP feedforward constraint is<br />
inserted into the dynamically decoupled<br />
model to satisfy the maximum dynamic<br />
stability, but it generates the internal<br />
dynamics.<br />
• Input/Output Linearization is utilized to<br />
address the problem of the internal<br />
dynamics.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–126–<br />
Leg-Wheel Hybrid Robot
Session WeBT8 Continental Parlor 8 Wednesday, September 28, <strong>2011</strong>, 10:00–11:30<br />
Multirobot Systems: Rendezvous & Task Switching<br />
Chair Richard Vaughan, Simon Fraser Univ.<br />
Co-Chair Filippo Arrichiello, Univ. di Cassino<br />
10:00–10:15 WeBT8.1<br />
Bayesian Rendezvous for Mobile Robots<br />
Sven Gowal and Alcherio Martinoli<br />
DISAL, EPFL, Switzerland<br />
• We study the rendezvous with differentialwheeled<br />
robots.<br />
• Robots have a limited and noisy<br />
perception of their neighboring<br />
teammates.<br />
• Rendezvous convergence properties are<br />
proven under noisy observations.<br />
• A Bayesian framework is exploited to<br />
improve the rendezvous performance.<br />
• Experiments are carried out on real<br />
Khepera III robots equipped with a IRbased<br />
range and bearing module.<br />
3 simulated Khepera III robots<br />
performing the Bayesian<br />
rendezvous with a particle filter.<br />
10:30–10:45 WeBT8.3<br />
A decentralized controller-observer scheme<br />
for multi-robot weighted centroid tracking<br />
Gianluca Antonelli, Filippo Arrichiello, Alessandro Marino<br />
DAEIMI, University of Cassino, Italy<br />
Fabrizio Caccavale<br />
DIFA, University of Basilicata, Italy<br />
• The paper presents a decentralized<br />
controller-observer scheme for centroid<br />
tracking with a multi-robot system<br />
• Each robot uses information from a local<br />
observer to cooperatively track an<br />
assigned time-varying reference<br />
• Convergence of the scheme is proven for<br />
fixed and switching communication<br />
topologies, and for directed and<br />
undirected graphs<br />
• Numerical simulations relative to different<br />
case studies are illustrated<br />
10:15–10:30 WeBT8.2<br />
Power-Aware Rendezvous with Shrinking<br />
Footprints<br />
Hassan Jaleel and Magnus Egerstedt<br />
Electrical and Computer Engineering<br />
Georgia Institute of Technology, USA<br />
• Power consumption is linked to sensor<br />
footprint area in multi-agent networks.<br />
• The rendezvous problem is studied when<br />
the footprints shrink over time due to<br />
power consumption.<br />
• A nonlinear interaction law is proposed for<br />
ensuring that no interactions are lost<br />
despite the shrinking footprints.<br />
• Lyapunov-based techniques are used to<br />
ensure that connectivity is achieved for all<br />
times.<br />
10:45–10:50 WeBT8.4<br />
Market-based Coordination of<br />
Coupled Robot Systems<br />
Ling Xu and Anthony Stentz<br />
Robotics Institute, Carnegie Mellon University, USA<br />
• We address the problem of<br />
environmental coverage with multiple<br />
robots<br />
• To solve this coupled problem, we<br />
introduce a parallel branch-and-bound<br />
approach that divides the computation<br />
among the robots<br />
• We also introduce an auctions<br />
framework, based on market-based<br />
techniques, which enables the robots to<br />
share the work and focus the search<br />
• Test results show parallel branch-andbound<br />
with auctions can lead to<br />
significant improvements for both search<br />
tree size and computation time<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–127–<br />
Parallel branch-and-bound with<br />
auctions
Session WeBT8 Continental Parlor 8 Wednesday, September 28, <strong>2011</strong>, 10:00–11:30<br />
Multirobot Systems: Rendezvous & Task Switching<br />
Chair Richard Vaughan, Simon Fraser Univ.<br />
Co-Chair Filippo Arrichiello, Univ. di Cassino<br />
10:50–10:55 WeBT8.5<br />
Event-driven Gaussian Process for Object<br />
Localization in Wireless Sensor Networks<br />
Jae Hyun YOO, Woojin Kim, and H.Jin KIM<br />
School of Mechanical & Aerospace Engineering,<br />
Seoul National University, South Korea<br />
• Wireless sensor networks suffer from<br />
excessive traffic between communicating<br />
nodes<br />
• In error-bounded algorithm, each node<br />
decides whether or not to transmit data<br />
to a sink node<br />
• Gaussian process regression is<br />
employed for position estimation of a<br />
unknown object<br />
• RSSI(Received Signal Strength<br />
Indicator), IR(Infrared Rays) and<br />
Magnetic sensors are used<br />
• Experimental results demonstrate the<br />
efficiency and accuracy of the proposed<br />
event-driven Gaussian process approach<br />
Experimental setup for the<br />
evaluation of the event-driven<br />
Gaussian process with thirty sensor<br />
nodes and one mobile robot<br />
11:00–11:15 WeBT8.7<br />
Task Switching in Multirobot Learning<br />
through Indirect Encoding<br />
David D’Ambrosio and Joel Lehman and Sebastian Risi and<br />
Kenneth Stanley<br />
Department of Electrical Engineering and Computer Science,<br />
University of Central Florida, United States<br />
• Training multiple robots to work together<br />
to perform complementary tasks is difficult<br />
• In this paper, multiagent HyperNEAT<br />
exploits the regularities among different<br />
robots and different tasks<br />
• Teams of Khepera III robots cooperatively<br />
explore an area and then return home<br />
when called<br />
• Teams that exploit regularities between<br />
subtasks solve the problem more<br />
consistently than those that cannot<br />
Z<br />
Multiagent HyperNEAT exploits<br />
the relationship among robots<br />
10:55–11:00 WeBT8.6<br />
Multi-Robot Patrolling with Coordinated<br />
Behaviours in Realistic Environments<br />
Luca Iocchi 1 , Luca Marchetti 1,2 , Daniele Nardi 1<br />
1 Department of Computer and Systems Science,\newline<br />
Sapienza, University of Rome, Italy.<br />
2 INRIA Sophia Antipolis - Mèditerranèe<br />
Sophia Antipolis, France.<br />
• Multi-Robot Patrolling needs more experiments in real and realistic<br />
simulated environments<br />
• Many strategies (devised and tested on abstract simulators) may act suboptimally<br />
or fail in real environments.<br />
• MRP needs on-line coordination in order to deal with un-modeled aspects<br />
of the problem during execution.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–128–<br />
Contributions<br />
• Realistic simulator (ROS)<br />
• Evaluation of standard MRP<br />
strategies under real conditions.<br />
• Definition of coordinated behaviors<br />
to improve MRP strategies in real<br />
environments.<br />
11:15–11:30 WeBT8.8<br />
Multi-Robot Coordination Methodology<br />
in<br />
Congested Systems with Bottlenecks<br />
Satoshi Hoshino<br />
Chemical Resources Laboratory, Tokyo Institute of Technology, Japan<br />
• Novel coordination methodology for<br />
autonomous mobile robots in congested<br />
systems with bottlenecks is proposed<br />
• Robot behavior control technique is<br />
improved to generate more efficient<br />
interaction force among robots<br />
• Environmental rule is designed to exert<br />
amplified interaction force on robots<br />
moving in congestion<br />
• Effectiveness of the robot behavior<br />
control technique and environmental rule<br />
through simulation experiments is shown<br />
Entrance<br />
1<br />
2<br />
Circuit 1<br />
8<br />
7<br />
Moving direction<br />
Velocity [m/s]<br />
1.5<br />
1.0<br />
0.5 Increased velocity<br />
0<br />
Constant & safe<br />
length L = 3.0 [m]<br />
3<br />
Crossing: C 1 & C 2<br />
Junction: C 1 & C 3<br />
Entrance<br />
9<br />
10<br />
Circuit 3 Circuit 2<br />
16<br />
6 5<br />
Crossing: C 1 & C 2 Lane unit = 20 [m]<br />
Junction: C 2 & C 3<br />
15<br />
40 [m]<br />
14<br />
13<br />
C 1<br />
Crossing & junction<br />
Simulation environment and<br />
velocity distribution in each lane<br />
C 3<br />
4<br />
C 2<br />
11<br />
12<br />
Original velocity
Session WeBT9 Continental Parlor 9 Wednesday, September 28, <strong>2011</strong>, 10:00–11:30<br />
Visual Servoing<br />
Chair Francois Chaumette, INRIA Rennes-Bretagne Atlantique<br />
Co-Chair Seth Hutchinson, Univ. of Illinois<br />
10:00–10:15 WeBT9.1<br />
From Optimal Planning to Visual Servoing<br />
With Limited FOV<br />
Paolo Salaris, Lucia Pallottino and Antonio Bicchi<br />
Interdept. Research Center “Enrico Piaggio”, University of Pisa, Italy<br />
Seth Hutchinson<br />
Dept. of Electrical and Computer Engineering, University of Illinois, USA<br />
• This paper presents an optimal feedback<br />
control scheme to drive a vehicle toward a<br />
desired position<br />
• The vehicle is equipped with limit Field-Of-<br />
View camera and must follow the shortest<br />
path keeping a given landmark in sight<br />
• Feedback control laws are defined<br />
exploiting geometric properties of the<br />
synthesis available from previous works<br />
Example of an optimal (shortest)<br />
path on the motion plane<br />
• By using a slightly generalized stability analysis setting, which is that<br />
of stability on a manifold, a proof of stability is given<br />
• Simulations and experiments demonstrate the effectiveness of the<br />
proposed technique<br />
10:30–10:45 WeBT9.3<br />
Intensity-based visual servoing for non-rigid<br />
motion compensation of soft tissue structures<br />
due to physiological motion using 4D ultrasound<br />
Deukhee Lee and Alexandre Krupa<br />
INRIA Rennes-Bretagne Atlantique, IRISA, France<br />
• Intensity-based visual tracking of 3D nonrigid<br />
motion in real time<br />
− 3D non-rigid motion model using<br />
TPS<br />
− Intensity-based motion estimation of<br />
a set of 3D points<br />
• Visual servo control structure to stabilize<br />
3D non-rigid motion using 4D-US imaging<br />
− Global rigid motion extraction<br />
− Position-based visual servo control<br />
• Successful robotic experiments for motion<br />
compensation with deformable TM<br />
phantom<br />
Manipulator<br />
6-DOF robot<br />
4D ultrasound ound probe<br />
Abdominal US<br />
phantom<br />
Turning table<br />
Drawer<br />
Deformable bl phantom h t<br />
A 6-DOF robot holding a 4D-US<br />
probe compensates rigid<br />
motion(top) and non-rigid<br />
motion(bottom).<br />
10:15–10:30 WeBT9.2<br />
Constrained Manipulator Visual Servoing (CMVS):<br />
Rapid Robot Programming in Cluttered Workspaces<br />
Ambrose Chan and Elizabeth Croft<br />
Mechanical Engineering, University of British Columbia, Canada<br />
James Little<br />
Computer Science, University of British Columbia, Canada<br />
• Model-free visual servoing of eye-in-hand<br />
manipulators in cluttered environments<br />
• Exploits the natural by-products of the<br />
teach-by-showing process, to help the<br />
robot navigate non-convex spaces<br />
• Robot servos to previously untaught<br />
locations under challenging constraints:<br />
• whole-arm collisions<br />
• object occlusions<br />
• robot's joint + camera's sensing limits<br />
• Automatic extraction of relevant cost<br />
functions and constraints for servoing<br />
• Experimental verification with Barrett WAM<br />
7-DOF + Sony XC-HR70 camera<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–129–<br />
Robot observes while the user<br />
obtains a “reference image” for<br />
visual servoing<br />
10:45–10:50 WeBT9.4<br />
Improving ultrasound intensity-based visual<br />
servoing: tracking and positioning tasks with<br />
2D and bi-plane probes<br />
Caroline Nadeau and Alexandre Krupa<br />
IRISA, INRIA Rennes-Bretagne Atlantique, France<br />
• Control of the in-plane and out-of-plane<br />
motions of an ultrasound probe<br />
• Intensity-based approach (no image<br />
processing or segmentation step)<br />
• On-line estimation of the current<br />
interaction matrix for positioning task with<br />
a classical 2D probe<br />
• Modelling of a bi-plane approach and<br />
experimental results with 2D and bi-plane<br />
ultrasound probes<br />
Validation of the method with a<br />
robotic arm using a realistic<br />
abdominal phantom
Session WeBT9 Continental Parlor 9 Wednesday, September 28, <strong>2011</strong>, 10:00–11:30<br />
Visual Servoing<br />
Chair Francois Chaumette, INRIA Rennes-Bretagne Atlantique<br />
Co-Chair Seth Hutchinson, Univ. of Illinois<br />
10:50–10:55 WeBT9.5<br />
Automatic landing on aircraft carrier by<br />
visual servoing<br />
Laurent Coutard and François Chaumette<br />
INRIA Rennes-Bretagne Atlantique and IRISA, France<br />
Jean-Michel Pflimlin<br />
Dassault Aviation, France<br />
• Alignment and landing tasks toward a<br />
moving aircraft carrier<br />
• 2D visual features, inspired by cues<br />
used by pilots, are related to aircraft<br />
state<br />
• Control with outputs feedback:<br />
•2D visual features<br />
•Inertial Central Unit<br />
• Validation with a 3D model-based tracker<br />
in a high fidelity simulator<br />
11:00–11:15 WeBT9.7<br />
Towards Vision-Based Control of Cable-Driven<br />
Parallel Robots<br />
Tej Dallej, Marc Gouttefarde, Nicolas Andreff, Micaël Michelin<br />
and Philippe Martinet<br />
(LASMEA/U.B.P - LIRMM - TECNALIA), FRANCE<br />
• Visual servoing methods are a good<br />
alternative for the control of cable-driven<br />
parallel robots.<br />
• The end-effector pose is measured using<br />
a camera and used for regulation.<br />
• The use of computer vision in the<br />
feedback simplifies the kinematic<br />
modeling.<br />
• The forward kinematic model is never<br />
used in the control.<br />
A photograph of ReelAx8 mobile<br />
platform observed by a camera.<br />
10:55–11:00 WeBT9.6<br />
Combining IBVS and PBVS to ensure the<br />
visibility constraint<br />
Olivier Kermorgant and François Chaumette<br />
Lagadic, INRIA Rennes-Bretagne Atlantique, France<br />
• Hybrid visual servoing based on a generic fusion approach<br />
• Pure PBVS is performed inside a safe image area<br />
• 2D information is added to keep the object in the image<br />
• Validation with simulation and experiments<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–130–<br />
Visual servo with a 3D tracking<br />
The model nodes are used as 2D<br />
features when they approach the<br />
image border<br />
11:15–11:30 WeBT9.8<br />
Time-Analysis of a Real-Time Sensor-Servoing<br />
System using Line-of-Sight Path Tracking<br />
Johannes Schrimpf 1 , Morten Lind 2 and Geir Mathisen 1,3<br />
1 Dept. of Engineering Cybernetics,<br />
Norwegian University of Science and Technology, Norway<br />
2 Dept. of Production and Quality Engineering,<br />
Norwegian University of Science and Technology, Norway<br />
3 Dept. of Applied Cybernetics, SINTEF ICT, Norway<br />
• Case system: Visual servoing<br />
for path-tracking with a real-time<br />
controlled robot.<br />
• Measurement and estimation of<br />
the delays in the control loop.<br />
• Stability analysis of a simple<br />
control-model based on pure<br />
delays in the control loop.<br />
• Qualitative validation of the<br />
estimated delay by simulated<br />
and experimental data.
Session WeCT1 Continental Parlor 1 Wednesday, September 28, <strong>2011</strong>, 14:00–15:30<br />
Human-Robot Interaction and Cooperation<br />
Chair Weihua Sheng, Oklahoma State Univ.<br />
Co-Chair Rachid Alami, CNRS<br />
14:00–14:15 WeCT1.1<br />
A Path Planning Method for Human Tracking<br />
Agents using Variable-term Prediction based on<br />
dynamic k Nearest Neighbor Algorithm<br />
Noriko Takemura, Yutaka Nakamura, and Hiroshi Ishiguro<br />
Graduate School of Engineering Science, Osaka University, Japan<br />
• A path planning method for human<br />
tracking by multiple agents.<br />
• Agents’ paths are planned based on<br />
similarity between the humans’ positions<br />
and the agents’ FOV.<br />
• The horizon length of the planned path is<br />
determined according to the accuracy of<br />
predicted human positions.<br />
• Simulation experiments showed that<br />
agents can keep tracking human<br />
movements recorded in a real<br />
environment.<br />
Recorded human movements<br />
used for simulation experiment<br />
(Kyoto station)<br />
14:30–14:45 WeCT1.3<br />
Sound Source Localization Based on<br />
Time Difference Feature and Space Grid Matching<br />
Xiaofei Li, Hong Liu and Xuesong Yang<br />
Shenzhen Graduate School, Peking University, CHINA<br />
• A new spectral weighting GCC-PHAT<br />
method estimates robust time difference<br />
of arrival in real environments.<br />
• Space is divided into grids represented<br />
by Gaussian Mixture Model based on<br />
time difference feature.<br />
• Finding the maximum likelihood grid with<br />
the current time difference feature in<br />
localization step.<br />
• Decision tree reduces the number of<br />
matches greatly.<br />
The partition of horizontal space<br />
and the process of localization<br />
14:15–14:30 WeCT1.2<br />
Using Human Motion Estimation for<br />
Human-Robot Cooperative Manipulation<br />
Anand Thobbi, Ye Gu and Weihua Sheng<br />
Department of Electrical and Computer Engineering,<br />
Oklahoma State University, USA.<br />
• We propose a novel approach that allows<br />
a humanoid robot to determine its<br />
leader/follower role in a collaborative<br />
manipulation task<br />
• Behavior of the robot is governed by a<br />
weighted sum of proactive and reactive<br />
controllers<br />
• Reactive controller learnt using<br />
reinforcement learning<br />
• Design of the proactive controller is based<br />
on human motion prediction<br />
• Proposed framework is demonstrated on a<br />
joint table lifting task<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–131–<br />
Experimental Setup and Results<br />
14:45–14:50 WeCT1.4<br />
Adapting Robot Team Behavior from Interaction<br />
with a Group of People<br />
P. Urcola and L. Montano<br />
Instituto de Investigación en Ingeniería de Aragón<br />
Universidad de Zaragoza, Spain<br />
• A team of robots cooperate to detect and track people using laser range<br />
finder sensors<br />
• Group motion and people entering and leaving the group are detected<br />
• The behavior of the team is adapted to the people and to the<br />
environment<br />
• The system is applied in a group guiding mission scenario<br />
• Simulation tests and experiments in a real environment are presented<br />
Group guiding mission experiment.<br />
The perception of the team is on top right corner
Session WeCT1 Continental Parlor 1 Wednesday, September 28, <strong>2011</strong>, 14:00–15:30<br />
Human-Robot Interaction and Cooperation<br />
Chair Weihua Sheng, Oklahoma State Univ.<br />
Co-Chair Rachid Alami, CNRS<br />
14:50–14:55 WeCT1.5<br />
Towards a Platform-Independent Cooperative<br />
HRI System: II. Perception, Execution and<br />
Imitation of Goal Directed Actions<br />
S. Lallée, U. Pattacini, JD. Boucher, S. Lemaignan, A. Lenz, C.<br />
Melhuish, L. Natale, S. Skachek, K. Hamann, J. Steinwender, EA<br />
Sisbot, G. Metta, R. Alami, M. Warnier, J. Guitton, F. Warneken,<br />
PF. Dominey<br />
• Action perceptual & motor<br />
representations linked within<br />
the same datastructure<br />
• Actions can be recognized,<br />
performed and used in<br />
Shared Plan in cooperation<br />
with a human<br />
• Robotic platform<br />
Independence is guaranteed<br />
through the use of the<br />
EgoSphere abstraction layer<br />
15:00–15:15 WeCT1.7<br />
Improvement of Speaker Localization<br />
by Considering Multipath Interference of Sound Wave<br />
for Binaural Robot Audition<br />
Ui-Hyun Kim, Takeshi Mizumoto,<br />
Tetsuya Ogata, and Hiroshi G. Okuno<br />
Department of Intelligence Science and Technology,<br />
Graduate School of Informatics, Kyoto University, Japan<br />
• This paper presents an improved speaker<br />
localization based on the GCC-PHAT<br />
method for binaural robot audition.<br />
• We proposed a new time delay factor for<br />
the GCC-PHAT method to compensate<br />
multipath interference due to diffraction of<br />
the sound wave after assuming that the<br />
robot head is spherical.<br />
• Experiments conducted in the SIG-2<br />
humanoid robot show that the proposed<br />
method reduces localization errors by 17.8<br />
degrees on average and by over 35<br />
degrees in side directions more than the<br />
conventional DOA estimation.<br />
RMSE (degree)<br />
Multipath interference<br />
with spherical-head assumption<br />
40<br />
30<br />
20<br />
10<br />
0<br />
Conventional DOA estimation<br />
Proposed DOA estimation #1<br />
Proposed DOA estimation #2<br />
Proposed DOA estimation #3<br />
-50 0<br />
Azimuth (degree)<br />
50<br />
RMSE of experimental results<br />
in speaker localization<br />
14:55–15:00 WeCT1.6<br />
Listening for people: Exploiting the spectral<br />
structure of speech to robustly perceive<br />
the presence of people<br />
Barbara Hilsenbeck<br />
Faculty of Mechanical and Mechatronic Engineering<br />
Karlsruhe University of Applied Sciences, Karlsruhe, Germany<br />
Nathan Kirchner<br />
Centre for Autonomous Systems, University of Technology, Sydney, Australia<br />
• As the desire to see robots<br />
ubiquitous in society grows, so<br />
does the need for providing the<br />
robots with the means of<br />
building awareness of any<br />
humans with which it may be<br />
sharing the environment<br />
• This paper presents a realworld<br />
suitable system which<br />
enables robots to robustly<br />
perceive the presence of<br />
people acoustically<br />
15:15–15:30 WeCT1.8<br />
Robot Audition & Beat Identification in Noisy<br />
Environments<br />
David K. Grunberg, Daniel M. Lofaro, Paul Oh, and Y. E. Kim<br />
College of Engineering, Drexel University, USA<br />
• Vision: use beat tracking algorithms for<br />
enabling the Hubo robot to participate in<br />
musical ensembles.<br />
• Musical participation requires the ability to<br />
determine auditory beat locations despite<br />
acoustic noise.<br />
• We explore the use of adaptive filtering to<br />
eliminate acoustic noise for greater<br />
musical accuracy.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–132–<br />
The Hubo robot
Session WeCT2 Continental Parlor 2 Wednesday, September 28, <strong>2011</strong>, 14:00–15:30<br />
Pose Estimation & Visual Tracking<br />
Chair Evangelos Papadopoulos, National Tech. Univ. of Athens<br />
Co-Chair Wim Meeussen, Willow Garage inc.<br />
14:00–14:15 WeCT2.1<br />
Determination of Rigid-Body Pose from<br />
Imprecise Point Position Measurements<br />
Anastasia Tegopoulou and Evangelos Papadopoulos<br />
Department of Mechanical Engineering,<br />
National Technical University of Athens, Athens, Greece<br />
• Determine rigid-body position and<br />
orientation from the position of a number<br />
of its points<br />
• The novel proposed method (NM) uses<br />
normal vectors, by-passing frame<br />
definitions<br />
• The NM takes advantage of point<br />
interconnections and is computationally<br />
efficient<br />
• The NM yields reliable results both with<br />
absolute and relative position sensors<br />
and both with low and high noise levels<br />
Two consecutive time instants for<br />
three point position measurements<br />
during planar motion<br />
14:30–14:45 WeCT2.3<br />
Simultaneous Localization and Capture<br />
with Velocity Information<br />
Qilong Yuan, I-Ming Chen<br />
School of Mechanical and Aerospace Engineering,<br />
Nanyang Technological University, Singapore<br />
• This paper introduces a method to<br />
monitor and capture the velocity,<br />
location, and dynamic behavior of a<br />
human with a self motion capture suit.<br />
• Velocity from lower limb kinematics<br />
and the integration of accelerations<br />
are fused through Kalman Filters to<br />
achieve smooth, accurate and drift<br />
free estimation results.<br />
• Capture of spatial motion for dynamic<br />
behaviors like running and jumping is<br />
realized.<br />
Spatial Motion for Jumping<br />
14:15–14:30 WeCT2.2<br />
Pose Estimation from a Single Image using Tensor<br />
Decompositions and an Algebra of Circulunts<br />
Randy C. Hoover<br />
Dept. of Electrical and Computer Engineering, SDSM&T, USA<br />
Karen S. Braman<br />
Dept. of Mathematics and Computer Science, SDSM&T, USA<br />
Ning Hao<br />
Dept. of Mathematics, Tufts, USA<br />
• New tensor SVD described using an<br />
algebra of circulunts<br />
• Experimental results applied to<br />
image analysis and interpretation<br />
• Able to reconstruct image set<br />
information using a much lower<br />
subspace dimension<br />
• Accurate pose estimation for one<br />
dimensionally correlated sequence<br />
using a single t-eigenimage<br />
• Analyze image sequences containing<br />
multiple degrees of correlations<br />
(multi-modal analysis)<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–133–<br />
�<br />
14:45–14:50 WeCT2.4<br />
Outlet detection and pose estimation for robot<br />
continuous operation<br />
Victor Eruhimov<br />
Itseez, Russia<br />
Wim Meeussen<br />
Willow Garage, USA<br />
• Outlet detection algorithm uses a single<br />
monocular camera<br />
• It locates 6 holes of a 2x1 outlet structure<br />
• A new pose estimation algorithm finds<br />
outlet holes 3D coordinates with submillimeter<br />
accuracy<br />
• It uses an image from a monocular<br />
camera and a wall normal orientation<br />
obtained from a stereo camera<br />
• The algorithm shows no false positives on<br />
a test set of 500+ outlet images<br />
• The plugin function enables PR2<br />
continuous operation that lasted for 13<br />
days
Session WeCT2 Continental Parlor 2 Wednesday, September 28, <strong>2011</strong>, 14:00–15:30<br />
Pose Estimation & Visual Tracking<br />
Chair Evangelos Papadopoulos, National Tech. Univ. of Athens<br />
Co-Chair Wim Meeussen, Willow Garage inc.<br />
14:50–14:55 WeCT2.5<br />
Robust Tracking of Human Hand Postures<br />
for Robot Teaching<br />
Jonathan Maycock, Jan Steffan,<br />
Robert Haschke, and Helge Ritter<br />
Neuroinformatics, Bielefeld University, Germany<br />
• Three methods for real-time tracking<br />
of hands are presented<br />
• A dataglove approach and two<br />
VICON approaches (one direct<br />
calculation and inverse kinematics<br />
approach)<br />
• Qualitative and quantitative are<br />
presented<br />
• Inverse kinematics approach found<br />
to have performed best for both<br />
power and precision grasps<br />
15:00–15:15 WeCT2.7<br />
Visual Tracking of Robots in Uncalibrated<br />
Environments<br />
Hesheng Wang and Weidong Chen<br />
Department of Automation, Shanghai Jiao Tong University, China<br />
Zhongli Wang<br />
Department of Automation, Beijing Jiao Tong University, China<br />
• Proposed a new adaptive controller for<br />
trajectory tracking of a number of feature<br />
points on a robot manipulator<br />
• Developed an adaptive algorithm to<br />
estimate the unknown parameters on-line<br />
• Proved asymptotic convergence of<br />
tracking errors by Lyapunov method and<br />
verified performance by experiments<br />
Visual tracking system<br />
14:55–15:00 WeCT2.6<br />
Visual Tracking Using the<br />
Sum of Conditional Variance<br />
Rogerio Richa, Raphael Sznitman, Russell Taylor, Gregory Hager<br />
Laboratory for Computational Science and Robotics (LCSR)<br />
Johns Hopkins University, Baltimore MD, USA<br />
• The SCV is a similarity metric used<br />
in the medical imaging domain for<br />
multi-modal image registration<br />
• Applied to visual tracking, the SCV<br />
is invariant to non-linear<br />
illumination variations and is<br />
computationally inexpensive<br />
• Compared to other similarity<br />
metrics, it requires significantly less<br />
iterations for convergence and has<br />
a larger convergence radius<br />
15:15–15:30 WeCT2.8<br />
Hybrid Discriminative Visual Object Tracking<br />
with Confidence Fusion for Robotics<br />
Applications<br />
Ren C. Luo, Ching-Chung Kao, and Yen-Chang Wu<br />
Electrical Engineering Department, National Taiwan University, Taiwan<br />
• In this paper, we propose a hybrid visual tracking algorithm that<br />
combines two discriminative trackers<br />
• The two trackers collectively determine the new object location via a<br />
process called confidence fusion<br />
• One component tracker is aimed to discriminate the target from the<br />
background in pixel level while the other is aimed to do so in patch level<br />
• The resultant hybrid tracker is hopefully more powerful since the two<br />
component trackers can complement the ability of discrimination<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–134–
Session WeCT3 Continental Parlor 3 Wednesday, September 28, <strong>2011</strong>, 14:00–15:30<br />
Robot Safety<br />
Chair Richard Voyles, Univ. of Denver<br />
Co-Chair Irene Sardellitti, Italian Inst. of Tech.<br />
14:00–14:15 WeCT3.1<br />
Towards safe human-robot interaction in robotic<br />
cells: an approach based on visual tracking and<br />
intention estimation<br />
Luca Bascetta, Gianni Ferretti and Paolo Rocco<br />
Dipartimento di Elettronica e Informazione, Politecnico di Milano, Italia<br />
Håkan Ardö<br />
Department of Mathematics, Lund University, Lund<br />
Herman Bruyninckx, Eric Demeester and Enrico Di Lello<br />
Department of Mechanical Engineering, Katholieke Universiteit Leuven, Belgium<br />
• Human-robot interaction requires the<br />
elimination of the safety fences<br />
• A new functionality of the safety<br />
controller – to detect and track the<br />
humans in the cell and to estimate their<br />
intentions – is required<br />
• The overall goal is to predict in the least<br />
possible time which area in the vicinity<br />
of the robot the human is heading to<br />
14:30–14:45 WeCT3.3<br />
Relaxing the Inevitable Collision State Concept to Address<br />
Provably Safe Mobile Robot Navigation with<br />
Limited Field-of-View in Unknown Dynamic Environments<br />
Sara Bouraine 1 , Thierry Fraichard 2 and Salhi Hassen 3<br />
1 CDTA , Algeria, 2 INRIA, France, 3 Blida University, Algeria<br />
• How to guarantee motion safety in partially known dynamic environments?<br />
• Absolute motion safety impossible to guarantee<br />
• Settle for weaker motion safety: passive motion safety (PMS) = if a<br />
collision must take place, robot is at rest<br />
• PMS obtained via Braking ICS, a relaxation of regular ICS (Inevitable<br />
Collision State)<br />
• Braking ICS checking algorithm designed and used to obtain provably<br />
passively safe navigation<br />
14:15–14:30 WeCT3.2<br />
Guaranteed Safe Online<br />
Learning of a Bounded System<br />
Jeremy Gillula<br />
Computer Science Dept., Stanford, USA<br />
Claire Tomlin<br />
EECS Dept., UC Berkeley, USA<br />
• Many machine learning algorithms are<br />
limited to situations where safety is not<br />
critical, since they lack guarantees about<br />
their stability and robustness<br />
• This can be solved by augmenting<br />
learning algorithms with reachability<br />
analysis, a general technique for making<br />
safety guarantees<br />
• We apply these ideas to a scenario in<br />
which an aerial robot is attempting to learn<br />
the dynamics of a ground vehicle<br />
• The resulting simulations show that by<br />
combining these two paradigms, one can<br />
create robotic systems that feature both<br />
high performance and guaranteed safety<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–135–<br />
Simulated example application:<br />
tracking a ground target using an<br />
aerial vehicle.<br />
14:45–14:50 WeCT3.4<br />
Capacitive Skin Sensors<br />
for Robot Impact Monitoring<br />
Samson Phan, Zhan Fan Quek, Preyas Shah, Zubair Ahmed, and<br />
Mark Cutkosky<br />
Mechanical Engineering, Stanford University, USA<br />
Dongjun Shin and Oussama Khatib<br />
Computer Science, Stanford University, USA<br />
• Capacitive sensors are embedded in an<br />
energy-absorbing artificial skin<br />
• Sensor scanning provides the history of<br />
a collision, including force magnitudes<br />
and contact locations<br />
• Impact testing demonstrates ability to<br />
discern effects of velocity, collision<br />
angle, contact stiffness, etc.<br />
Test setup and sequence of sensor<br />
readings for a sliding contact
Session WeCT3 Continental Parlor 3 Wednesday, September 28, <strong>2011</strong>, 14:00–15:30<br />
Robot Safety<br />
Chair Richard Voyles, Univ. of Denver<br />
Co-Chair Irene Sardellitti, Italian Inst. of Tech.<br />
14:50–14:55 WeCT3.5<br />
Variable Radius Pulley Design Methodology for<br />
Pneumatic Artificial Muscle-based Antagonistic<br />
Actuation Systems<br />
Dongjun Shin1 , Xiyang Yeh2 , Oussama Khatib1 1<br />
Artificial Intelligence Laboratory, Stanford University, USA<br />
2<br />
Mechanical Engineering , Stanford University, USA<br />
• Analysis of the limitations of<br />
conventional circular pulleys<br />
• A new design methodology for<br />
variable radius pulleys<br />
• Experimental comparisons to<br />
show performance improvement in<br />
position control in the enlarged<br />
workspace.<br />
Variable Radius Pulleys in Pneumatic<br />
Muscle-driven 1-DOF Testbed<br />
15:00–15:15 WeCT3.7<br />
Online Data-Driven Fault Detection<br />
for Robotic Systems<br />
Raphael Golombek and Sebastian Wrede<br />
CoR-Lab, Bielefeld University, Germany<br />
Marc Hanheide<br />
School of Computer Science, University of Birmingham, UK<br />
Martin Heckmann<br />
Honda Research Institute Europe, Offenbach, Germany<br />
• Fault detection applicable for online as<br />
well as a-posteriori analysis<br />
• Exploits inter-component<br />
communication in order to model<br />
normal system behaviour<br />
• Directly applicable to systems built upon a<br />
data-driven or event-based robotics<br />
middle-ware concept<br />
• Flexible design allows to adapt to<br />
systems which do not fit the<br />
aforementioned architectural concepts<br />
Conceptual Overview of the<br />
Data-Driven Fault Detection<br />
Approach<br />
14:55–15:00 WeCT3.6<br />
Fast Computation of Wheel-Soil Interactions for<br />
Safe and Efficient Operation of Mobile Robots<br />
Zhenzhong Jia, William Smith and Huei Peng<br />
Ground Robotics Reliability Center (GRRC), University of Michigan, USA<br />
Department of Mechanical Engineering, University of Michigan, USA<br />
• Fast analytical models of wheel-soil<br />
interactions are important for mobile<br />
robots in off-road environments.<br />
• An accurate, fully-functional, closed-form<br />
wheel-terrain interaction model was<br />
developed by quadratic approximation of<br />
the stress distributions.<br />
• A non-iterative method was derived to<br />
estimate the entry angle when given the<br />
normal wheel load.<br />
• Mobility prediction and real-time vehicle<br />
control become possible by coupling the<br />
wheel-soil interaction model and the entry<br />
angle estimator.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–136–<br />
Determine forces/torques by<br />
integrating the stress distributions<br />
15:15–15:30 WeCT3.8<br />
Containment indicator function construction via<br />
numerical conformal mapping<br />
Shuo Han and Richard M. Murray<br />
California Institute of Technology, Pasadena, USA<br />
• Numerical conformal mapping is used to<br />
obtain a smooth containment indicator<br />
function for optimal motion planning<br />
• The procedure can be approximated by<br />
solving a convex optimization problem<br />
• Several different 2D shapes have been<br />
tested<br />
• Can also be extended to modeling<br />
obstacles without much difficulty
Session WeCT4 Continental Ballroom 4 Wednesday, September 28, <strong>2011</strong>, 14:00–15:30<br />
Symposium: Haptic Feedback and System Evaluation<br />
Chair Marcia O'Malley, Rice Univ.<br />
Co-Chair Yasuyoshi Yokokohji, Kobe Univ.<br />
14:00–14:15 WeCT4.1<br />
The sigma.7 haptic interface for MiroSurge:<br />
A new bi-manual surgical console<br />
Andreas Tobergte¹, Patrick Helmer², Ulrich Hagn¹,<br />
Patrice Rouiller², Sophie Thielmann¹, Sebastien Grange²,<br />
Alin Albu-Schäffer¹, Francois Conti², and Gerd Hirzinger¹<br />
German Aerospace Center (DLR) ¹<br />
Force Dimension²<br />
• Design of the haptic devices and the surgical console<br />
• Kinematics and dynamics<br />
• Control with force/torque sensor<br />
• Ergonomics of the console<br />
14:30–14:45 WeCT4.3<br />
Measuring an Operator’s Maneuverability<br />
Performance in the Haptic Teleoperation of<br />
Multiple Robots<br />
Hyoung Il Son 1 , Lewis L. Chuang 1 , Antonio Franchi 1 , Junsuk Kim 2 ,<br />
Dongjun Lee 3 , Seong-Whan Lee 2 , Heinrich H. Bülthoff 1,2 ,<br />
and Paolo Robuffo Giordano 1<br />
1 Max Planck Institute for Biological Cybernetics, Germany;<br />
2 DBCE, Korea University, Korea; 3 DMAE, Seoul National University, Korea<br />
• Maneuverability measure to assess the ease<br />
of the operator in maneuvering the remote<br />
multi-robot slave<br />
• Three haptic cues to perceive<br />
the state of the remote UAVs and their<br />
surrounding environments:<br />
1. Velocity cue<br />
2. Force cue<br />
3. Velocity+Force cue<br />
Haptic Device<br />
• Evaluation by using H 2 norm and<br />
bandwidth of the maneuverability measure<br />
• Results indicate that less control effort<br />
is required in achieving the same level of<br />
tracking accuracy with Force cue<br />
Velocity<br />
Command<br />
Tele-Operator’s<br />
Maneuverability<br />
Haptic<br />
Feedback<br />
JND<br />
Multiple UAVs<br />
14:15–14:30 WeCT4.2<br />
Haptic coupling with augmented feedback between<br />
two KUKA LWR’s and the PR2 robot arms<br />
Koen Buys, Steven Bellens, Wilm Decre,<br />
Ruben Smits, Enea Scioni, Tinne De Laet,<br />
Joris De Schutter, and Herman Bruyninckx<br />
Department Mechanical Eng., KU Leuven, Belgium<br />
• Coupling between Orocos and ROS<br />
robotic frameworks<br />
• Haptic feedback setup with non-uniform<br />
robot coupling<br />
• Coupling of head movement<br />
• Augmented visual feedback in VR goggles<br />
• Quantitative force measurements in<br />
experiments<br />
14:45–14:50 WeCT4.4<br />
Bilateral Physical Interaction with a<br />
Robot Manipulator through a<br />
Weighted Combination of Flow Fields<br />
Antonio Pistillo, Sylvain Calinon and Darwin G. Caldwell<br />
Department of Advanced Robotics, Istituto Italiano di Tecnologia, Italy<br />
• Tasks demonstrated by kinesthetic<br />
teaching technique and encoded<br />
with a combination of local flow<br />
fields.<br />
• The weighting mechanism modifies<br />
standard GMM weights to be<br />
independent from each other.<br />
• Safe Physical Interaction between<br />
the user and the robot is achieved.<br />
• The robot is used as a bi-directional<br />
tangible interface in order to<br />
influence its actions.<br />
• The user can start, stop, pause,<br />
resume and select desired robot<br />
movements by physically guiding it<br />
during task executions.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–137–
Session WeCT4 Continental Ballroom 4 Wednesday, September 28, <strong>2011</strong>, 14:00–15:30<br />
Symposium: Haptic Feedback and System Evaluation<br />
Chair Marcia O'Malley, Rice Univ.<br />
Co-Chair Yasuyoshi Yokokohji, Kobe Univ.<br />
14:50–14:55 WeCT4.5<br />
Motion Control of a Semi-Mobile Haptic Interface<br />
for Extended Range Telepresence<br />
Antonia Pérez Arias and Uwe D. Hanebeck<br />
Intelligent Sensor-Actuator-Systems Laboratory (ISAS),<br />
Karlsruhe Institute of Technology (KIT), Germany<br />
• Prepositioning algorithm for haptic<br />
exploration of spatially unrestricted<br />
target environments<br />
• Key idea: Consideration of user’s<br />
direction of motion<br />
• Benefits:<br />
• Optimal utilization of available<br />
space in user environment<br />
• Robustness against noisy<br />
measurements of user position<br />
• Experimental validation under<br />
real-world conditions<br />
Semi-mobile haptic interface and<br />
operator in user environment.<br />
15:00–15:15 WeCT4.7<br />
Assistance or Challenge?<br />
Filling a Gap in User-Cooperative Control<br />
1Georg Rauter, 1Roland Sigrist, 1Laura Marchal-Crespo,<br />
1, 2Heike Vallery, 1Robert Riener, and 1Peter Wolf<br />
1SMS-Lab, Institute of Robotics and Intelligent Systems (IRIS), ETH-Zurich &<br />
Medical Faculty,University of Zurich, Switzerland<br />
2Biomedical Engineering, Khalifa University of Science, Technology &<br />
Research (KUSTAR), Abu Dhabi, UAE<br />
• Haptic feedback might be effective if the right amount of guidance or<br />
challenge is provided<br />
• We developed a user-cooperative controller that can be continuously<br />
modified from supportive to challenging mode<br />
• In a pilot study, the controller could proof its applicability for motor learning<br />
studies<br />
Rowing simulator: Haptic device consists of a tendon-based parallel robot<br />
14:55–15:00 WeCT4.6<br />
An Objective Index that Substitutes for Subjective<br />
Quality of Vibrotactile Material-Like Textures<br />
Shogo Okamoto and Yoji Yamada,<br />
Dept. of Mechanical Science and Engineering, Nagoya University, Japan<br />
• For the delivery of tactile contents through the Internet, the lossy data<br />
compression of vibrotactile textures are expected.<br />
• An objective index that evaluates the quality deterioration of the lossycompressed<br />
textures is necessary for an alternative for psychological<br />
experiments.<br />
• This study developed such<br />
an index based on the<br />
amplitude spectrum of<br />
vibrotactile stimuli.<br />
• The index was applied to<br />
wood and sandpaper<br />
textures and described the<br />
subjective quality indices by<br />
approximately 80%.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–138–<br />
Developed objective index<br />
Quality indices of compressed vibrotactile<br />
material-like textures<br />
Subjective indices acquired by<br />
psychological experiments<br />
15:15–15:30 WeCT4.8<br />
Design and Characterization of the ReHapticKnob, a<br />
Robot for Assessment and Therapy of Hand Function<br />
Jean-Claude Metzger, Olivier Lambercy,<br />
Dominique Chapuis, Roger Gassert<br />
Rehabilitation Engineering Laboratory, ETH Zurich, Switzerland<br />
• End-effector rehabilitation robot with<br />
unique sensing and actuation capabilities<br />
• 2 active DOF for training of hand<br />
opening/closing (88N) and forearm<br />
rotation (0.98Nm) tasks<br />
• Developed for precise and objective<br />
assessments of hand function and for high<br />
fidelity dynamic interaction<br />
• Preliminary grasping results with healthy<br />
subjects
Session WeCT5 Continental Ballroom 5 Wednesday, September 28, <strong>2011</strong>, 14:00–15:30<br />
Symposium: Symbolic Approaches to Motion Planning and Control<br />
Chair Hadas Kress-Gazit, Cornell Univ.<br />
Co-Chair Calin Belta, Boston Univ.<br />
14:00–14:15 WeCT5.1<br />
LTL-Based Decentralized Supervisory Control of<br />
Multi-Robot Tasks Modelled as Petri Nets<br />
Bruno Lacerda and Pedro U. Lima<br />
Institute for Systems and Robotics, Instituto Superior Técnico, Portugal<br />
• Methodology to enforce coordination rules s<br />
for multi-robot systems<br />
• Each robot is modelled as a Petri net and<br />
coordination rules are written in LTL<br />
• Decentralized approach, uses the LTL<br />
specifications to define the communication n<br />
requirements between the robots<br />
• Greatly improves scalability when<br />
compared to centralized approach<br />
The Th inputs i t of fth the methodology: th d l<br />
A Petri net model of a robot and<br />
an LTL formula for the robot to fulfill<br />
14:30–14:45 WeCT5.3<br />
Foundations of Formal Language for Humans<br />
and Artificial Systems Based on Intrinsic<br />
Structures in Spatial Behaviors<br />
Zhaodan Kong and Bernie Mettler<br />
Department of Aerospace Engineering and Mechanics<br />
University of Minnesota, USA<br />
• Interactions between vehicle<br />
dynamics and environment result in<br />
patterns in spatial behavior.<br />
• Preliminary results studying these<br />
patterns show that behavior exhibits<br />
well defined hierarchic structure.<br />
• Results can be used to create a<br />
formal language, which can be used<br />
to mitigate complexity in planning and<br />
representation.<br />
• Gained insight will also help<br />
understand spatial intelligence and<br />
help design adaptive guidance<br />
algorithms.<br />
Optimal Trajectories of Raven<br />
UAV in Downtown San Diego<br />
14:15–14:30 WeCT5.2<br />
Optimal Multi-Robot Path Planning with Temporal<br />
Logic Constraints<br />
Alphan Ulusoy<br />
Systems Eng., Boston University, USA<br />
Stephen L. Smith<br />
Electrical and Computer Eng., University of Waterloo, Canada<br />
Xu Chu Ding and Calin Belta<br />
Systems Eng., Boston University, USA<br />
Daniela Rus<br />
EECS, Massachusetts Institute of Technology, USA<br />
• A method for planning optimal paths for a<br />
group of robots that satisfy a common<br />
mission.<br />
• The mission is given as a Linear Temporal<br />
Logic formula.<br />
• In addition, an optimizing proposition must<br />
repeatedly be satisfied.<br />
• The goal is to minimize the maximum time<br />
between the satisfying instances of the<br />
optimizing proposition.<br />
• Method utilizes a timed automaton<br />
representation to capture asynchronous<br />
robot motions.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–139–<br />
Simulated team trajectories<br />
14:45–14:50 WeCT5.4<br />
Minimalist Multiple Target Tracking Using<br />
Directional Sensor Beams<br />
Leonardo Bobadilla, Oscar Sanchez, Justin Czarnowski<br />
Steven M. LaValle<br />
Department of Computer Science, University of Illinois, USA<br />
• Multiple-agent tracking is a fundamental<br />
problem of sensor networks with broad<br />
applications.<br />
• We propose simple and efficient<br />
algorithms for reconstructing paths of<br />
agents using weak detection sensors.<br />
• Designed and implemented a low-cost,<br />
low energy consumption hardware<br />
architecture to demonstrate the practicality<br />
proposed approach.<br />
a) Two randomly moving bodies<br />
b) Ground truth paths<br />
c) Reconstructed paths<br />
based on our algorithm
Session WeCT5 Continental Ballroom 5 Wednesday, September 28, <strong>2011</strong>, 14:00–15:30<br />
Symposium: Symbolic Approaches to Motion Planning and Control<br />
Chair Hadas Kress-Gazit, Cornell Univ.<br />
Co-Chair Calin Belta, Boston Univ.<br />
14:50–14:55 WeCT5.5<br />
Temporal Logic Control in Dynamic Environments<br />
with Probabilistic Satisfaction Guarantees<br />
Ana Medina Ayala, Sean Andersson and Calin Belta<br />
Mechanical Engineering, Boston University, United States<br />
• A robot with noisy sensors and<br />
actuators and moving in a dynamic<br />
environment is considered.<br />
• The task is given as a specification in<br />
Probabilistic Computation Tree Logic<br />
(PCTL).<br />
• Algorithms for synthesizing a control<br />
policy to maximize the probability of<br />
satisfaction are derived.<br />
• Different levels of knowledge of<br />
environment dynamics are<br />
considered.<br />
15:00–15:15 WeCT5.7<br />
High-Level Control of Modular Robots<br />
Sebastian Castro, Sarah Koehler and Hadas Kress-Gazit<br />
Sibley School of Mechanical Engineering, Cornell University, USA<br />
• Generation of provably correct<br />
controllers from structured English<br />
specifications with reconfigurable<br />
modular robots.<br />
• Utilizes the LTLMoP framework with the<br />
addition of reconfiguration via descriptors<br />
called traits.<br />
•A configuration-gait-trait library<br />
automatically selects robot morphologies<br />
given a set of active traits.<br />
• Demonstrated through the CKBot<br />
platform and a 3D simulator built on a<br />
physics engine.<br />
Sample Workspace and Task<br />
Execution with the CKBot platform.<br />
14:55–15:00 WeCT5.6<br />
Computing Unions of Inevitable Collision States<br />
and Increasing Safety to Unexpected Obstacles<br />
Daniel Althoff 1 , Christoph N. Brand 1 ,<br />
Dirk Wollherr 1,2 and Martin Buss 1<br />
Institute of Automatic Control Engineering, TUM, Germany 1<br />
Institute for Advanced Study, TUM, Germany 2<br />
• Problem of computing unions of Inevitable<br />
Collision State (ICS) sets is addressed<br />
• Sequential computation of ICS is shown<br />
• Two novel ICS-Checker algorithms are<br />
presented allowing efficient computation<br />
• Concept of robot Maneuverability is introduced,<br />
reducing collision probability to unexpected<br />
obstacles<br />
• Simulation results validate these concepts<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–140–<br />
Top: Union of singel ICS sets<br />
Bottom: ICS set of union of<br />
obstacles<br />
15:15–15:30 WeCT5.8<br />
Synthesis of Feedback Controllers for Multiple<br />
Aerial Robots with Geometric Constraints<br />
Nora Ayanian, Vinutha Kallem and Vijay Kumar<br />
Department of Mechanical Engineering and Applied Mechanics<br />
University of Pennsylvania, USA<br />
• Algorithm for multi-robot navigation<br />
with convergence and safety<br />
guarantees<br />
• Kinematic and dynamic agents<br />
• Maintain communication and avoid<br />
collisions<br />
• Simultaneously decompose joint<br />
configuration space into polytopes<br />
and plan a discrete path using A*<br />
• Sequentially deploy navigationfunction<br />
based local controllers on<br />
polytopes to reach goal<br />
• Implementations on teams of<br />
ground robots and quadrotors<br />
A quadrotor (red) and two ground robots<br />
(blue, green) deploy at left to cover an<br />
intersection in an urban environment<br />
while maintaining communication.
Session WeCT6 Continental Ballroom 6 Wednesday, September 28, <strong>2011</strong>, 14:00–15:30<br />
Symposium: Marine Robotics: Platforms and Applications<br />
Chair Ryan Smith, Queensland Univ. of Tech.<br />
Co-Chair Gianluca Antonelli, Univ. degli Studi di Cassino<br />
14:00–14:15 WeCT6.1*<br />
Semi-Plenary Invited Talk: Wiring an Interactive Ocean<br />
John Delaney, University of Washington<br />
14:30–14:45 WeCT6.3<br />
Towards Mixed-initiative, Multi-robot Field<br />
Experiments: Design, Deployment, and Lessons<br />
Learned<br />
Jnaneshwar Das, Gaurav S. Sukhatme<br />
Department of Computer Science, University of Southern California, USA<br />
Thom Maughan, Mike McCann, Mike Godin, Tom O’Reilly,<br />
Monique Messié, Fred Bahr, Kevin Gomes, Frédéric Py,<br />
James G. Bellingham, Kanna Rajan<br />
Monterey Bay Aquarium Research Institute, USA<br />
• Description of a month-long multi-institute<br />
field experiment with multiple<br />
heterogeneous robotic platforms.<br />
• Design, development, and use of an<br />
Oceanographic Decision Support System<br />
(ODSS) during the experiment.<br />
• Lessons learned and discussion of future<br />
roadmap.<br />
14:15–14:30 WeCT6.2<br />
Semi-Plenary Invited Talk: Wiring an Interactive Ocean<br />
John Delaney, University of Washington<br />
14:45–14:50 WeCT6.4<br />
Current-Sensitive Path Planning for an<br />
Underactuated Free-floating Ocean Sensorweb<br />
Kristen P. Dahl<br />
California Institute of Technology, USA<br />
David R. Thompson, David McLaren, Yi Chao, and Steve Chien<br />
Jet Propulsion Laboratory, California Institute of Technology, USA<br />
• This work investigates multiagent path<br />
planning in strong, dynamic currents using<br />
highly underactuated vehicles.<br />
• The Argo global network consists of over<br />
3000 ocean-observing floats able to<br />
control only depth.<br />
• Accurate current forecasts present the<br />
possibility of intentionally controlling floats’<br />
motion.<br />
• Simulations tested this method’s potential<br />
to improve coverage or direct floats to a<br />
desired location.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–141–<br />
A simulation in which five floats<br />
use current-driven planning to<br />
improve coverage.<br />
(yellow: non-adaptive;<br />
blue: adaptive)
Session WeCT6 Continental Ballroom 6 Wednesday, September 28, <strong>2011</strong>, 14:00–15:30<br />
Symposium: Marine Robotics: Platforms and Applications<br />
Chair Ryan Smith, Queensland Univ. of Tech.<br />
Co-Chair Gianluca Antonelli, Univ. degli Studi di Cassino<br />
14:50–14:55 WeCT6.5<br />
Toward Risk Aware Mission Planning for<br />
Autonomous Underwater Vehicles<br />
Arvind A. Pereira, Jonathan Binney, and Gaurav S. Sukhatme<br />
Department of Computer Science, University of Southern California, USA<br />
Burton H. Jones and Matthew Ragan<br />
Department of Biological Sciences, University of Southern California, USA<br />
• Creation of probabilistic occupancy<br />
maps from AIS data<br />
• Plan minimum risk paths for AUVs<br />
that avoid risky regions<br />
• Paths planned enforce AUV<br />
resurfacing within a maximum<br />
distance from previous waypoint<br />
• Significant surface collision risk<br />
reduction compared to shortest path<br />
and other methods<br />
15:00–15:15 WeCT6.7<br />
Obstacle Detection from Overhead Imagery<br />
using Self-Supervised Learning for<br />
Autonomous Surface Vehicles<br />
Hordur K. Heidarsson<br />
Department of Electrical Engineering, University of Southern California, USA<br />
Gaurav S. Sukhatme<br />
Department of Computer Science, University of Southern California, USA<br />
• Technique for generating an<br />
obstacle map from sonar data<br />
and overhead imagery<br />
• Pipeline consisting of<br />
• Feature extraction<br />
• AdaBoost classifier<br />
• MRF smoothing<br />
• Combining images from<br />
different times<br />
• Resulting map has 3 classes<br />
• Obstacles<br />
• Transient/suspect obstacles<br />
• Free space<br />
14:55–15:00 WeCT6.6<br />
AUV Docking on a Moving Submarine Using a<br />
K-R Navigation Function<br />
Sujit. P.B and Joao Sousa<br />
Department of Electrical and Computer Eng., University of Porto, Portugal<br />
Anthony, J Healey<br />
Naval Postgraduate School, Monterey, CA, USA<br />
• The objective is to design a controller for<br />
the AUV to dock onto a moving submarine<br />
• Developed a Navigation function approach<br />
that integrates path planning with collision<br />
avoidance<br />
• Modeled no-fly zones around stern to<br />
protect propulsor and mitigate against<br />
disturbances<br />
• Other no-fly zones included around the<br />
dock to control approach headings<br />
• Gain must be selected appropriately to<br />
avoid local minima<br />
• Simulation results show the desirability of<br />
the approach from different directions<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–142–<br />
AUV docking on a moving<br />
submarine from stern direction<br />
15:15–15:30 WeCT6.8<br />
Observability metric for the relative localization of AUVs<br />
based on range and depth measurements:<br />
theory and experiments<br />
Filippo Arrichiello and Gianluca Antonelli<br />
DAEIMI, University of Cassino, Italy<br />
Antonio Pedro Aguiar and Antonio Pascoal<br />
IST, Technical University Lisbon, Portugal<br />
• The work addresses the problem of<br />
observability of the relative motion of two<br />
AUVs equipped with, velocity, range and<br />
depth sensors<br />
• We exploited nonlinear observability<br />
concepts to analyze some types of<br />
relative AUV motions<br />
• We derive a specific observability index<br />
(metric) and study its dependence on the<br />
types of motion commanded to the<br />
vehicles.<br />
• The results derived are validated<br />
experimentally in a equivalent, single<br />
beacon navigation scenario<br />
Autonomous surface vessel used<br />
for the experimental validation
Session WeCT7 Continental Parlor 7 Wednesday, September 28, <strong>2011</strong>, 14:00–15:30<br />
Humanoid Control<br />
Chair Eiichi Yoshida, National Inst. of AIST<br />
Co-Chair Alessandro De Luca, Univ. di Roma "La Sapienza"<br />
14:00–14:15 WeCT7.1<br />
Logic Programming with Simulation-based Temporal<br />
Projection for Everyday Robot Object Manipulation<br />
Lars Kunze, Mihai E. Dolha, Michael Beetz<br />
Intelligent Autonomous Systems<br />
Technische Universitaet Muenchen<br />
Germany<br />
• Robots must decide on appropriate action ction<br />
parameterizations based on the expected cted<br />
consequences of their actions<br />
• Logic programming with simulation-based sed<br />
temporal projection can predict the<br />
consequences of manipulation actions s<br />
qualitatively<br />
• Hence, robots can employ these methods ods<br />
to infer action parameterizations that lead ead<br />
to desired outcomes<br />
Logical proof-tree of simulation-<br />
based temporal projections<br />
14:30–14:45 WeCT7.3<br />
Switching Multiple LQG Controllers<br />
Based on Bellman's Optimality Principle<br />
Norikazu Sugimoto1 and Jun Morimoto2 1National Institute of Communication Telecommunication, Kyoto, Japan<br />
2ATR Computational Neuroscience Labs, Kyoto, Japan<br />
State estimator<br />
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( t � 1 | t)<br />
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i<br />
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Value function estimator<br />
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( x(<br />
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r ( t ) � x( t)<br />
Qx<br />
( t ) � u(<br />
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Weighting signal predictor<br />
1 � 1<br />
2 �<br />
� ( t ) � p ( x(<br />
t ), r ( t ) | i)<br />
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i<br />
2 i<br />
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Deviation from Bellman’s optimality principle<br />
14:15–14:30 WeCT7.2<br />
Experimental Evaluation of a Trajectory/Force<br />
Tracking Controller for a Humanoid Robot<br />
Cleaning a Vertical Surface<br />
Fuyuki Sato, Tatsuya Nishii, Jun Takahashi,<br />
Yuki Yoshida, Masaru Mitsuhashi and Dragomir Nenchev<br />
Mechanical Systems Engineering, Tokyo City University, Japan<br />
• The force tracking controller is based on<br />
CoM ground projection and ZMP position<br />
feedback<br />
• The hand trajectory is tracked via a PD<br />
controller<br />
• The trajectory/force tracking controller is<br />
implemented under a mixed<br />
position/torque control mode<br />
• The performance of the controller is<br />
experimentally evaluated with a miniature<br />
humanoid robot HOAP-2 cleaning a<br />
whiteboard under a prerecorded<br />
motion/force pattern<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–143–<br />
A HOAP-2 humanoid robot<br />
cleaning a whiteboard<br />
14:45–14:50 WeCT7.4<br />
Adaptive Predictive Gaze Control<br />
of a Redundant Humanoid Robot Head<br />
Giulio Milighetti and Luca Vallone<br />
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation,<br />
Germany<br />
Alessandro De Luca<br />
Dipartimento di Informatica e Sistemistica, Univ. di Roma “La Sapienza”, Italy<br />
• General concept for gaze control of<br />
redundant humanoid robot heads<br />
• Prediction of target next state by adaptive<br />
Kalman filter<br />
• Combination of proportional feedback and<br />
feedforward term for trajectory tracking<br />
control<br />
• Gain adaptation for improvement of<br />
system dynamics<br />
• Exploitation of inverse kinematics by<br />
means of weighted pseudoinverse<br />
• Addition of self-motions for human-like<br />
head postures<br />
ARMAR-III Head tracking a cup
Session WeCT7 Continental Parlor 7 Wednesday, September 28, <strong>2011</strong>, 14:00–15:30<br />
Humanoid Control<br />
Chair Eiichi Yoshida, National Inst. of AIST<br />
Co-Chair Alessandro De Luca, Univ. di Roma "La Sapienza"<br />
14:50–14:55 WeCT7.5<br />
Dynamic Whole-Body Mobile Manipulation with<br />
a Torque Controlled Humanoid Robot via<br />
Impedance Control Laws<br />
Alexander Dietrich, Thomas Wimböck, and Alin Albu-Schäffer<br />
German Aerospace Center (DLR), Germany<br />
• Cartesian impedance is utilized for task<br />
execution<br />
• Incorporation of reactive subtasks like<br />
collision avoidance, singularity avoidance,<br />
mechanical end stops, null space damping<br />
• Experimental validation of the proposed<br />
whole-body control<br />
• A video demonstrates the performance of<br />
our approach<br />
Torque Controlled Humanoid<br />
Rollin’ Justin<br />
15:00–15:15 WeCT7.7<br />
A Computational Approach for Push Recovery in<br />
case of Multiple Non coplanar Contacts<br />
Darine Mansour and Alain Micaelli<br />
CEA, France<br />
Pierre Lemerle<br />
INRS, France<br />
• A push recovery approach based on a<br />
humanoid simple model is proposed<br />
•A dynamic stability indicator<br />
predicting whether a perturbed system<br />
can be stabilized is generated<br />
• An algorithm stabilizing the system<br />
under fixed contacts when feasible,<br />
is developed<br />
• An algorithm stabilizing the system<br />
through an optimal contact change<br />
when needed, is developed<br />
[1.] Bretl and S. Lall, "Testing Static Equilibrium for Legged<br />
Robots", in IEEE Transactions on Robotics, Aug 2008<br />
Simplified Model of a humanoid<br />
The model consists of a punctual<br />
mass at the COM and (n) non<br />
coplanar contact surfaces. It is an<br />
extension of the model in [1.]<br />
14:55–15:00 WeCT7.6<br />
Gait Pattern Generation and Stabilization for<br />
Humanoid Robot Based on Coupled Oscillators<br />
Inyong Ha, Yusuke Tamura, and Hajime Asama<br />
Department of Precision Engineering, The University of Tokyo, Japan<br />
• Design of gait pattern generator<br />
based on coupled oscillator model<br />
divided into movement oscillator<br />
and balance oscillator<br />
• Walking stabilizer by feedback<br />
controller using oscillator<br />
parameters and sensor data<br />
• Tested on open humanoid robot<br />
platform DARwIn-OP for the<br />
evaluation<br />
15:15–15:30 WeCT7.8<br />
Stretched knee walking with novel inverse<br />
kinematics for humanoid robots<br />
Przemyslaw Krycza<br />
Graduate School of Science and Engineering, Waseda University, Japan<br />
Kenji Hashimoto, Hideki Kondo and Aiman Omer<br />
Faculty of Science and Engineering, Waseda University, Japan<br />
Hunk-ok Lim<br />
Faculty of Engineering, Kanagawa University and Humanoid Research<br />
Institute, Waseda University, Japan<br />
Atsuo Takanishi<br />
Department of Modern Mechanical Engineering and Humanoid Research<br />
Institute, Waseda University, Japan<br />
• Structure modeled with highly redundant<br />
serial kinematic chains<br />
• Inverse kinematics (IK) problem solved<br />
including multiple tasks and their relative<br />
priorities<br />
• Human-like walk with heel-contact and toe<br />
off as well as nearly stretched knee<br />
phases<br />
• Contains IK equations description,<br />
simulation and on machine experiments<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–144–<br />
WABIAN-2R
Session WeCT8 Continental Parlor 8 Wednesday, September 28, <strong>2011</strong>, 14:00–15:30<br />
Multirobot Planning<br />
Chair Noel E. Du Toit, California Inst. of Tech.<br />
Co-Chair Spring Berman, Harvard Univ.<br />
14:00–14:15 WeCT8.1<br />
Multiple Agent Coordination for Stochastic<br />
Target Interception Using MILP<br />
Daniel J. Stilwell and Apoorva Shende<br />
ECE Department, Virginia Tech, USA<br />
Matthew J. Bays<br />
ME Department, Virginia Tech, USA<br />
• Multi-agent coordination control for<br />
multiple moving target interception<br />
formulated as a MILP.<br />
• Binary variables in MILP used to indicate<br />
interception.<br />
• Gaussian uncertainty exploited to<br />
formulate chance interception<br />
constraints. Cost adaptable to the<br />
specific target interception problem.<br />
• Proposed stochastic MILP shown to be<br />
no more computationally expensive than<br />
the corresponding deterministic MILP.<br />
• Suitable for multi-agent systems well<br />
approximated by Gaussian uncertainty.<br />
14:30–14:45 WeCT8.3<br />
A Reciprocal Sampling Algorithm for<br />
Lightweight Distributed Multi-Robot Localization<br />
Amanda Prorok and Alcherio Martinoli<br />
Distributed Intelligent Systems and Algorithms Laboratory<br />
EPFL, Switzerland<br />
• Fully decentralized, real-time particle filter<br />
algorithm accommodating realistic robotic<br />
assumptions<br />
• Probabilistic robot detection model based<br />
on relative range and bearing measures<br />
• Novel collaborative reciprocal sampling<br />
method allows a drastic reduction in<br />
number of particles needed for localization<br />
• Experimental validation on a team of 4<br />
real Khepera III robots equipped with IRbased<br />
range and bearing modules<br />
Multi-robot detection model<br />
14:15–14:30 WeCT8.2<br />
Using Minimal Communication to Improve<br />
Decentralized Conflict Resolution<br />
for Non-holonomic Vehicles<br />
Athanasios Krontiris and Kostas E. Bekris<br />
Computer Sience and Engineering Department<br />
University of Nevada, Reno, USA<br />
• This work considers the problem of<br />
decentralized coordination between<br />
multiple non-holonomic vehicles, each<br />
navigating to a specified goal<br />
• Two automata are integrated<br />
− Lower level automaton: guarantees<br />
collision avoidance.<br />
− Higher level automaton: Updates<br />
desired direction based on a dynamic<br />
priority scheme<br />
• Minimalistic local communication is<br />
employed to avoid livelocks<br />
• Favorable experimental comparison<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–145–<br />
Simulated aircraft in different modes as<br />
specified by the proposed method,<br />
and their desired direction of motion.<br />
The method allows one system at a time<br />
to make progress to its goal.<br />
14:45–14:50 WeCT8.4<br />
Localization Using Ambiguous<br />
Bearings from Radio Signal Strength<br />
Jason Derenick, Jonathan Fink, and Vijay Kumar<br />
GRASP Laboratory, University of Pennsylvania, USA<br />
• Explores the problem of localizing robots<br />
using ambiguous bearing estimates (πperiodic)<br />
drawn from a robot’s anisotropic,<br />
but symmetric, radiation profile.<br />
• Leveraging these bearings, odometry, and<br />
a compass, a MH-EKF is formulated that<br />
exploits motion to disambiguate state and<br />
recover formation scale.<br />
• Theoretical analysis shows that despite<br />
the combinatoric nature of the problem,<br />
only 2 initial hypotheses are needed.<br />
• Experimental results using mobile robots,<br />
with Zigbee transceivers, and simulation<br />
results for a larger system demonstrate<br />
the feasibility of the approach.
Session WeCT8 Continental Parlor 8 Wednesday, September 28, <strong>2011</strong>, 14:00–15:30<br />
Multirobot Planning<br />
Chair Noel E. Du Toit, California Inst. of Tech.<br />
Co-Chair Spring Berman, Harvard Univ.<br />
14:50–14:55 WeCT8.5<br />
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15:00–15:15 WeCT8.7<br />
Efficient and Complete<br />
Centralized Multi-Robot Path Planning<br />
Ryan Luna and Kostas E. Bekris<br />
Computer Science and Engineering, University of Nevada, Reno, USA<br />
• Approach uses intuitive single robot primitives<br />
to efficiently compute multi-robot solutions<br />
• Computes a sequential set of paths to solve<br />
the path-planning problem<br />
• Algorithm is complete for all instances with<br />
two or more empty vertices<br />
• Method is fast and scalable to problems with<br />
hundreds of robots<br />
• Solution quality is highly competitive with<br />
modern coupled and decoupled methods<br />
• No dependence on tunable parameters or<br />
discretization assumptions<br />
Push<br />
2 3<br />
Swap<br />
2<br />
3<br />
Push<br />
1<br />
Swap<br />
1<br />
14:55–15:00 WeCT8.6<br />
M*: A complete Multirobot Path Planning<br />
Algorithm with Performance Bounds<br />
Glenn Wagner and Howie Choset<br />
Robotics Institute, Carnegie Mellon University, USA<br />
• Subdimensional expansion<br />
generates dynamically<br />
generates a low dimensional<br />
search space embedded in full<br />
configuration space<br />
• M* is an implementation of<br />
subdimensional expansion for<br />
graph representations of the<br />
configuration space<br />
• M* and its variants are proven to<br />
be complete and either cost<br />
optimal or bounded suboptimal<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–146–<br />
Plots of the percent of trials in<br />
which each algorithm was able to<br />
find a path within 5 minutes, and<br />
the 10’th (red), 50’th (blue) and<br />
90’th (green) percentile of times<br />
required to find solution<br />
15:15–15:30 WeCT8.8<br />
ARMO: Adaptive Road Map Optimization for<br />
Large Robot Teams<br />
Alexander Kleiner. Dali Sun and Daniel Meyer-Delius<br />
Dept. of Computer Science, University of Freiburg, Germany<br />
� Autonomous robot teams in intra logistics<br />
dispatching transportation tasks<br />
� Multi Robot Path Planning Problem<br />
� Automatic adaptation of the road map<br />
when the map or station load has changed<br />
� Hidden Markov Model for detecting<br />
changes in the map<br />
� Linear Integer Programming for<br />
optimizing the road map configuration
Session WeCT9 Continental Parlor 9 Wednesday, September 28, <strong>2011</strong>, 14:00–15:30<br />
Visual & Multi-Sensor Calibration<br />
Chair Jonathan Kelly, USC<br />
Co-Chair Oliver Birbach, DFKI<br />
14:00–14:15 WeCT9.1<br />
Extrinsic Calibration of<br />
a Single Line Scanning Lidar and a Camera<br />
Kiho Kwak, Daniel F. Huber, Hernan Badino, and Takeo Kanade<br />
• We propose a robust-weighted<br />
extrinsic calibration algorithm<br />
that is implemented easily and<br />
has small calibration error.<br />
• The extrinsic parameters are<br />
estimated by minimizing the<br />
distance error between<br />
corresponding features into the<br />
image.<br />
• The alignment accuracy of our<br />
method is two times better than<br />
the compared state of the art<br />
algorithms.<br />
Carnegie Mellon University, USA<br />
14:30–14:45 WeCT9.3<br />
Optimization Based IMU Camera Calibration<br />
Elmar Mair and Oliver Ruepp and Darius Burschka<br />
Department of Informatics, Technische Universtität München, Germany<br />
Michael Fleps and Michael Suppa<br />
Institute for Robotics and Mechatronics, German Aerospace Center, Germany<br />
• Spatial alignment estimation of IMU and<br />
camera by a nonlinear batch-optimization<br />
• Modeling of the sensors’ trajectories by Bsplines<br />
• No approximations and sequential data<br />
processing like in Kalman filters<br />
• Experiments on simulated and real data<br />
show that the approach compares<br />
favorably to conventional methods<br />
14:15–14:30 WeCT9.2<br />
A Novel 2.5D Pattern for Extrinsic Calibration of<br />
ToF and Camera Fusion System<br />
Jiyoung Jung, Yekeun Jeong, Jaesik Park, Hyowon Ha,<br />
and In-So Kweon<br />
Department of Electrical Engineering, KAIST, South Korea<br />
James Dokyoon Kim<br />
Samsung Advanced Institute of Technology, South Korea<br />
• We present an extrinsic calibration<br />
method to estimate the pose of a color<br />
camera with respect to a 3D Time-of-Flight<br />
camera.<br />
• We use a 2.5D pattern so that the correct<br />
correspondences are obtained for both<br />
color and ToF cameras.<br />
• We refine the intrinsic parameters of the<br />
ToF camera to model its projection as a<br />
pinhole camera model.<br />
• Depth constraint which restricts the depth<br />
measurement to lie on the pattern plane is<br />
also employed into LM optimization as<br />
well as the reprojection errors.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–147–<br />
The 2.5D pattern and the checkerboard<br />
in color and ToF amplitude images<br />
A comparison of 3D rendering results<br />
14:45–14:50 WeCT9.4<br />
Rapid Development of Manifold-Based Graph Optimization<br />
Systems for Multi-Sensor Calibration and SLAM<br />
René Wagner, Oliver Birbach<br />
Safe and Secure Cognitive Systems, DFKI, Germany<br />
Udo Frese<br />
Safe and Secure Cognitive Systems, DFKI, Germany<br />
Real-Time Computer Vision Group, University of Bremen, Germany<br />
• The Manifold Toolkit for Matlab (MTKM)<br />
brings state-of-the-art generic graph<br />
optimization to Matlab.<br />
• Using MTKM, even complex multi-sensor<br />
cross-calibration problems can be solved<br />
with very little code.<br />
• Get MTKM and all presented calibration<br />
and SLAM examples as open source from<br />
http://openslam.org/MTK.html<br />
• Build upon MTKM to get your calibration<br />
tasks done quickly and experiment with<br />
SLAM in a convenient Matlab<br />
environment.<br />
MTKM applications: Hand–<br />
eye+stereo+IMU calibration on a<br />
humanoid service robot, vertical<br />
stereo calibration, 3D pose<br />
adjustment, RGB-D+accelerometer<br />
calibration, 2D pose adjustment,<br />
2D feature SLAM.
Session WeCT9 Continental Parlor 9 Wednesday, September 28, <strong>2011</strong>, 14:00–15:30<br />
Visual & Multi-Sensor Calibration<br />
Chair Jonathan Kelly, USC<br />
Co-Chair Oliver Birbach, DFKI<br />
14:50–14:55 WeCT9.5<br />
Two-Phase Online Calibration for Infrared-based<br />
Inter-Robot Positioning Modules<br />
Sven Gowal, Amanda Prorok and Alcherio Martinoli<br />
DISAL, EPFL, Switzerland<br />
• A novel two-phase online calibration method<br />
is presented.<br />
• The combination of a Least Mean Square<br />
algorithm and Gaussian Process<br />
Regression allows accurate estimation for<br />
robots with limited computational power.<br />
• Online and on-board estimation is possible.<br />
• Experiments are carried out on 4 Khepera<br />
III robots equipped with a IR-based range<br />
and bearing module.<br />
Example of the two-phase<br />
calibration procedure.<br />
15:00–15:15 WeCT9.7<br />
High-Accuracy Hand-Eye Calibration<br />
from Motion on Manifolds<br />
Federico Vicentini, Nicola Pedrocchi, Matteo Malosio<br />
and Lorenzo Molinari Tosatti<br />
National Research Council CNR-ITIA, Italy<br />
• new hand-eye calibration procedure<br />
based on generation of constant<br />
geometrical entities<br />
• robot motion is programmed for tracing<br />
manifolds with good interpolation/fitting<br />
properties<br />
• geometrical solution with real robot<br />
implementation, replacing the solution of<br />
quadratic AX=XB equation<br />
• better accuracy and robustness against<br />
noise in hand-eye measurement<br />
generation of circular manifolds for<br />
hand-eye transform computation<br />
14:55–15:00 WeCT9.6<br />
Self Calibration of a Vision System Embedded in<br />
a Visual SLAM Framework<br />
Cyril Joly and Patrick Rives<br />
ARobAS Team, INRIA Sophia Antipolis-Méditerranée, France<br />
• Self-calibration of the 3D transformation<br />
between a camera and the odometry<br />
during visual SLAM<br />
• Observability analysis based on the<br />
study of global properties of the system<br />
• Parameters estimation can be computed<br />
when the radius of curvature of the<br />
trajectory changes<br />
• Implementation of a simultaneous SLAM<br />
and calibration approach based on the<br />
Smoothing and Mapping (SAM) algorithm<br />
• Validation on real data with a camera<br />
mounted with a non negligible rotational<br />
offset<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–148–<br />
The translation and rotation<br />
transformation between the<br />
camera and the robot has to be<br />
estimated<br />
15:15–15:30 WeCT9.8<br />
A Nonlinear Observer Approach for<br />
Concurrent Estimation of Pose, IMU Bias and<br />
Camera-to-IMU Rotation<br />
Glauco Garcia Scandaroli1 , Pascal Morin1 and Geraldo Silveira2 1 INRIA Sophia Antipolis-Méditerranée, France<br />
2 CTI Renato Archer – DRVC Division, Brazil<br />
• Novel observer designed for pose<br />
(orientation and position), IMU bias and<br />
Camera-to-IMU rotation estimation.<br />
• Exponential stability is obtained under<br />
adequate observability condition.<br />
• Gyroscopes can be used to detect the<br />
satisfaction of the observability condition.<br />
• Experimental results for an inertial-visual<br />
sensor directly exploiting the image<br />
intensities support the effectiveness of the<br />
method.
Session WeDT1 Continental Parlor 1 Wednesday, September 28, <strong>2011</strong>, 16:00–17:30<br />
Human-Robot Collaboration<br />
Chair Tetsunari Inamura, National Inst. of Informatics<br />
Co-Chair Elizabeth Croft, Univ. of British Columbia<br />
16:00–16:15 WeDT1.1<br />
Enhanced Visual Scene Understanding<br />
through Human-Robot Dialog<br />
M. Johnson-Roberson, J. Bohg, B. Rasolzadeh and D. Kragic<br />
CVAP/CAS, KTH Stockholm, Sweden<br />
G. Skantze, J.Gustafson and Rolf Carlson<br />
TMH, KTH Stockholm, Sweden<br />
• Goal: Robot should correctly<br />
segment objects in the scene from<br />
themselves and from the background<br />
• Initial object hypotheses from<br />
active segmentation approach<br />
• Disambiguation through humanrobot<br />
interaction using incremental<br />
dialog system<br />
• Quantitative experimental<br />
evaluation on real data shows<br />
improved scene segmentation after<br />
human-robot interaction<br />
Top: Point Cloud of Scene. Middle: Initial<br />
Labeling. Bottom: Resegmented Scene<br />
after Human-Robot Interaction<br />
16:30–16:45 WeDT1.3<br />
Towards Safe Physical Human-Robot<br />
Collaboration: A Projection-based Safety System<br />
Christian Vogel, Maik Poggendorf, Christoph Walter<br />
and Norbert Elkmann<br />
Fraunhofer Institute for Factory Operation and Automation IFF,<br />
Magdeburg, Germany<br />
• Based on conventional projector and<br />
camera equipment<br />
• Projection of safety-spaces directly into<br />
the environment (e.g. onto the floor)<br />
• Cameras reliably detect violations<br />
whenever projection rays are disrupted<br />
• Reduced influence of changing<br />
environmental light conditions<br />
• Intrinsically safe system<br />
• Dynamic change of shape, size, position<br />
• Display additional information<br />
• Visible/ non-visible light<br />
16:15–16:30 WeDT1.2<br />
Intention-Based Coordination and Interface<br />
Design for Human-Robot Cooperative Search<br />
Dan Xie, Yun Lin, Roderic Grupen and Allen Hanson<br />
Department of Computer Science,<br />
University of Massachusetts Amherst, USA<br />
• A multi-agent search scheme is presented<br />
that supports the recognition of activities<br />
and, thus, learning methods for<br />
cooperative human-robot interaction.<br />
• The robot updates a Probabilistic<br />
Distribution Function of the target object<br />
using the observations and the estimated<br />
state of human peers to compensate the<br />
human behaviors.<br />
• An implicit interface design that reduces<br />
the cognitive workload -- the robot infers<br />
the intention of the user and provides<br />
assistance autonomously.<br />
16:45–16:50 WeDT1.4<br />
Experimental Investigation of Human-<br />
Robot Cooperative Carrying<br />
Chris A. C. Parker and Elizabeth Croft<br />
Mechanical Engineering, University of British Columbia, Canada<br />
• Human behavior in a human-robot<br />
cooperative carrying domain was<br />
investigated.<br />
• 19 non-expert human subjects<br />
participated in this study.<br />
• The importance of visual and haptic<br />
feedback to human users was<br />
examined.<br />
• Humans also were found to apply<br />
significant tension to cooperatively<br />
carried loads under certain conditions.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–149–<br />
Experimental Setup of our<br />
HRI Study
Session WeDT1 Continental Parlor 1 Wednesday, September 28, <strong>2011</strong>, 16:00–17:30<br />
Human-Robot Collaboration<br />
Chair Tetsunari Inamura, National Inst. of Informatics<br />
Co-Chair Elizabeth Croft, Univ. of British Columbia<br />
16:50–16:55 WeDT1.5<br />
A Human-Centered Approach to Robot Gesture<br />
Based Communication<br />
within Collaborative Working Processes<br />
T. Ende, S. Haddadin, S. Parusel, T. Wüsthoff, M. Hassenzahl, A. Albu-Schäffer<br />
Institute of Robotics and Mechatronics (DLR), Germany<br />
Folkwang University of the Arts Essen, Germany<br />
• Identification of relevant gestures from<br />
human observations for human-robot coworking<br />
tasks<br />
• Transfer of gestures to robotic systems<br />
of increasing abstraction<br />
• Evaluation of human recognition rate for<br />
isolated human and robotic gestures<br />
• Robotic gesture lexicon and machine<br />
• Complex use-case with single arm<br />
manipulators in a drug preparation<br />
scenario<br />
Transferring gestures from human observation<br />
to human-robot collaborative tasks<br />
17:00–17:15 WeDT1.7<br />
Motion Coaching with Emphatic Motions and<br />
Adverbial Expression for Humans by a Robot<br />
Keisuke Okuno<br />
Department of Informatics, SOKENDAI, Japan<br />
Tetsunari Inamura<br />
National Institute of Informatics / SOKENDAI, Japan<br />
• Propose a method to bind Emphatic<br />
Motions and Adverbial Expressions.<br />
• Propose a method to control degree of<br />
emphasis of synthesized motions and<br />
adverbial expressions.<br />
• Realize the methods by using a sole<br />
parameter in a phase space.<br />
• Show the feasibility with a motion<br />
coaching system and experiments of<br />
actual sport coaching.<br />
The “α “ binds and control both<br />
emphatic motions and adverbial<br />
expressions<br />
16:55–17:00 WeDT1.6<br />
Human Workflow Analysis using 3D Occupancy<br />
Grid Hand Tracking in a Human-Robot<br />
Collaboration<br />
Claus Lenz, Alice Sotzek, Thorsten Röder,<br />
Helmuth Radrich and Alois Knoll<br />
Robotics and Embedded Systems, Technische Universitä̈ t München, Germany<br />
Markus Huber and Stefan Glasauer<br />
Center for Sensorimotor Research Department of Neurology,<br />
Ludwig-Maximilians-Universität München, Germany<br />
• HMM-based human workflow<br />
analysis of an assembly task<br />
jointly performed by human<br />
and robot<br />
• Training of models without<br />
influence of a robot<br />
• Evaluations to find taskrelevant<br />
and set-up<br />
independent<br />
• Transfer of workflow analysis<br />
models to a robotic system<br />
using a new three-dimensional<br />
occupancy grid tracking<br />
approach to estimate the hand<br />
positions as input data<br />
17:15–17:30 WeDT1.8<br />
A system for interactive learning<br />
in dialogue with a tutor<br />
D. Skočaj, M. Kristan, A. Vrečko, M. Mahnič, M.Janíček,<br />
G.J. Kruijff, M. Hanheide, N. Hawes, T. Keller, M. Zillich, K. Zhou<br />
University of Ljubljana, DFKI Saarbrücken, University of Birmingham,<br />
Albert-Ludwigs-Universität Freiburg, Vienna University of Technology<br />
• Representations and mechanisms that<br />
facilitate continuous learning of visual<br />
concepts in dialogue with a tutor.<br />
• Implemented in a robotic system.<br />
• Beliefs about the world are<br />
• created by processing visual and<br />
linguistic information<br />
• used for planning system behaviour.<br />
• Different kinds of learning initiated by the<br />
human tutor or by the system itself:<br />
• Tutor-driven learning<br />
• Situated tutor-assisted learning<br />
• Non-situated tutor-assisted learning<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–150–<br />
Scenario setup and<br />
system overview
Session WeDT2 Continental Parlor 2 Wednesday, September 28, <strong>2011</strong>, 16:00–17:30<br />
Recognition & Prediction of Motion<br />
Chair Sylvain Calinon, Italian Inst. of Tech.<br />
Co-Chair Sachin Chitta, Willow Garage Inc.<br />
16:00–16:15 WeDT2.1<br />
Realtime Recognition of Complex Daily<br />
Activities Using Dynamic Bayesian Network<br />
Chun Zhu and Weihua Sheng<br />
School of Electrical and Computer Engineering<br />
Oklahoma State University, USA<br />
• Daily complex activities are recognized<br />
using wearable motion sensors and<br />
location information<br />
• Adaptive gesture spotting is proposed<br />
to segment gestures for different<br />
environments and body activities<br />
• A dynamic Bayesian network is<br />
developed to recognize body activities<br />
and hand gestures simultaneously<br />
• Test in a mock apartment shows good<br />
recognition results<br />
The hardware platform<br />
Dynamic Bayesian network for<br />
complex activity recognition<br />
16:30–16:45 WeDT2.3<br />
Movement Segmentation using a<br />
Primitive Library<br />
Franziska Meier 1 , Evangelos Theodorou 1 , Freek Stulp 1<br />
and Stefan Schaal 1,2<br />
1 CLMC Lab, University of Southern California<br />
2 Max-Planck-Institute for Intelligent Systems, Tuebingen<br />
• The segmentation problem is reduced<br />
to online sequential movement<br />
recognition.<br />
• Original Dynamic Movement Primitives<br />
are reformulated to take the form of a<br />
linear dynamical system.<br />
• Based on this new formulation an EM<br />
algorithm is developed to estimate the<br />
duration and goal of<br />
partially observed motion.<br />
• Results on two applications of this new formulation - online movement<br />
recognition and movement segmentation – are presented.<br />
16:15–16:30 WeDT2.2<br />
Motion Data Retrieval Based on Statistic Correlation<br />
between Motion Symbol Space and Language<br />
Seiya Hamano and Wataru Takano<br />
Yoshihiko Nakamura<br />
Department of Mechano-Informatics, The University of Tokyo, Japan<br />
• The motion retrieval method with word<br />
based on Canonical Correlation Analysis<br />
between motion features and word<br />
features is proposed.<br />
• The motion feature is represented by the<br />
proto-symbol space and the word feature<br />
is represented by the binary feature of<br />
word labels.<br />
• This method enables motion retrieve<br />
considering similarity of motion pattern<br />
and closeness of word meaning.<br />
16:45–16:50 WeDT2.4<br />
Encoding the temporal and spatial constraints<br />
of a task in explicit-duration HMM<br />
Sylvain Calinon, Antonio Pistillo and Darwin G. Caldwell<br />
Advanced Robotics Dept, Italian Institute of Technology (IIT), Genova, Italy<br />
•Aim: Recovery from perturbations in<br />
physical human-robot interaction (pHRI)<br />
• We consider scenarios where the user<br />
momentary perturbs the task by physically<br />
holding the robot (the robot is not aware of<br />
the type and place of the perturbation).<br />
• Kinesthetic teaching is used to learn the<br />
task, with non-linear motion encoded as a<br />
weighted combination of linear systems.<br />
• We focus here on the design of a weighting<br />
mechanism that can handle various types<br />
of task constraints.<br />
• We review existing methods and introduce<br />
Hidden Semi-Markov Model (HSMM) as a<br />
way to encapsulate the importance of time<br />
and space constraints in a parametric form.<br />
• Two pHRI experiments show that different<br />
behaviors can be obtained depending on the<br />
chosen HSMM weighting schemes.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–151–
Session WeDT2 Continental Parlor 2 Wednesday, September 28, <strong>2011</strong>, 16:00–17:30<br />
Recognition & Prediction of Motion<br />
Chair Sylvain Calinon, Italian Inst. of Tech.<br />
Co-Chair Sachin Chitta, Willow Garage Inc.<br />
16:50–16:55 WeDT2.5<br />
Behavior Prediction from Trajectories in a House<br />
by Estimating Transition Model Using Stay Points<br />
Taketoshi Mori and Hiroshi Noguchi<br />
Dept. of Life Support Technology (Molten), The Univ. of Tokyo, Japan<br />
Shoji Tominaga, Masamichi Shimosaka,<br />
Rui Fukui and Tomomasa Sato<br />
Dept. of Mechano-Informatics, The Univ. of Tokyo, Japan<br />
• Prediction by preceding activities<br />
before the target behaviors<br />
• Segmentation of trajectory data into<br />
staying or moving and classification<br />
of the segments<br />
• Mining time-series association rules<br />
from transition events of each<br />
segmented trajectory<br />
• Validated by experiment using<br />
trajectories of approx. 2 years.<br />
(Upper) Room Layout<br />
(Lower) Example of Transition<br />
17:00–17:15 WeDT2.7<br />
Trajectory Prediction of Spinning Ball<br />
for Ping-Pong Player Robot<br />
Yanlong Huang, De Xu, Min Tan and Hu Su<br />
State Key Laboratory of Intelligent Control and Management of Complex<br />
Systems, Institute of Automation, Chinese Academy of Sciences, China<br />
• Analytic flying model: the flying ball’s<br />
self-rotational velocity is estimated<br />
and sufficiently taken into account in<br />
the model<br />
• Rebound Model: the relation<br />
between the velocities before and<br />
after rebound<br />
• Trajectory Prediction: initial trajectory<br />
fitting, initial velocities calculation,<br />
landing position prediction, rebound<br />
velocities calculation, striking position<br />
prediction<br />
Z(mm)<br />
600<br />
400<br />
200<br />
0<br />
0<br />
100<br />
200<br />
X(mm)<br />
300<br />
The trajectory prediction<br />
Predict with angular velocity<br />
Predict without angular velocity<br />
Actual<br />
-500<br />
-1000<br />
-1500<br />
-2000<br />
400 -2500 Y(mm)<br />
16:55–17:00 WeDT2.6<br />
Estimation and Prediction of Multiple Flying Balls<br />
Using Probability Hypothesis Density Filtering<br />
Oliver Birbach and Udo Frese<br />
DFKI, Bremen, Germany<br />
� Position and velocity estimation of flying<br />
balls using recently developed GM-PHD<br />
filter<br />
� Detected flying balls represented as peaks<br />
in a Gaussian mixture, propagated by<br />
UKF over time<br />
� Prior of typically pitched balls integrated<br />
into GM-PHD for trajectory starting and<br />
increased robustness<br />
� Used as tracking component in a robotic<br />
ball catching application<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–152–<br />
PHD as projection into image and<br />
onto the floor.<br />
17:15–17:30 WeDT2.8<br />
Invariant Trajectory Indexing for Real Time 3D<br />
Motion Recognition<br />
Jianyu Yang, Y.F. Li and Keyi Wang<br />
Dept of MEEM, Joint Advanced Research Center of<br />
University of Science and Technology of China and<br />
City University of Hong Kong, Suzhou, China<br />
• A dynamic indexing method is proposed<br />
for representing, retrieving and real time<br />
recognizing motion trajectories.<br />
• Trajectory is segmented into basic<br />
segments by classifying the points, and<br />
we use the labels of the basic segments<br />
to title the trajectory.<br />
• All the trajectories of database are titled<br />
as an indexing database, and the query<br />
trajectory is retrieved and recognized by<br />
the matching of titles.<br />
• As the matching of title is simple, this<br />
method is effective for motion retrieval<br />
and real time recognition.<br />
A model of segmented trajectory<br />
(a) and a real case (b).
Session WeDT3 Continental Parlor 3 Wednesday, September 28, <strong>2011</strong>, 16:00–17:30<br />
Haptic Rendering & Object Recognition<br />
Chair M. Cenk Cavusoglu, Case Western Res. Univ.<br />
Co-Chair Masashi Konyo, Tohoku Univ.<br />
16:00–16:15 WeDT3.1<br />
A Comparison of Encoding Schemes for Haptic<br />
Object Recognition using a Biologically<br />
Plausible Spiking Neural Network<br />
Sivalogeswaran Ratnasingam and T.M. McGinnity<br />
Intelligent Systems Research Centre, School of Computing and Intelligent<br />
Systems, Magee campus, University of Ulster, United Kingdom<br />
• A biologically inspired spiking neural<br />
network based haptic object recognition<br />
system is proposed.<br />
• A number of different encoding schemes<br />
to convert the haptic measurements into<br />
spike train are proposed.<br />
• The spiking neural network was trained<br />
using a supervised training approach.<br />
• During the training, firing threshold of the<br />
hidden layer neurons were modified.<br />
• A robot hand with three fingers that have<br />
the same number of degrees of freedom<br />
as the human fingers was used for testing<br />
the system.<br />
• Test results show that the system can<br />
recognise 100 % of 7 different objects.<br />
Figure. Robot hand grasping a ball<br />
16:30–16:45 WeDT3.3<br />
Effect of Visuo-Haptic Co-location on<br />
3D Fitts’ Task Performance<br />
Michael J. Fu, Andrew D. Hershberger, Kumiko Sano,<br />
and M. Cenk Cavusoglu<br />
Electrical Engineering and Computer Science,<br />
Case Western Reserve University, USA<br />
• Goal was to characterize human<br />
performance of point-to-point reaching<br />
for physical, co-located/non-colocated<br />
virtual environment (VE), and rotated<br />
VE visualization conditions<br />
• Existing results on benefits of visual<br />
and haptic workspace co-location in VE<br />
are conflicting<br />
• Key finding was the significant<br />
decrease in end-point error for tasks<br />
performed in a co-located virtual<br />
environment display modality<br />
Experimental setup using<br />
Fishtank VE display<br />
16:15–16:30 WeDT3.2<br />
Study on lower back electrotactile stimulation<br />
characteristics for prosthetic sensory feedback<br />
Konstantinos Dermitzakis and Alejandro Hernandez-Arieta<br />
Artificial Intelligence Laboratory, University of Zurich, Switzerland<br />
Monika Seps<br />
Institute of Neuroinformatics, Swiss Federal Institute of Technology Zurich<br />
(ETHZ), Zurich, Switzerland<br />
• Prosthetic devices are in lack of rich<br />
sensory feedback to their user’s body<br />
• Such sensory feedback is important for<br />
the integration of artificial limbs to the<br />
body image<br />
• The lower back is a comfortable, low<br />
cognitive load feedback area<br />
• This study details the basic characteristics<br />
of electrical stimulation to the lower back<br />
• The lower back is found to be a viable<br />
location for providing such feedback<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–153–<br />
Moving sensation through<br />
electrical stimulation<br />
16:45–16:50 WeDT3.4<br />
Determining Object Geometry with<br />
Compliance and Simple Sensors<br />
Leif P. Jentoft and Robert D. Howe<br />
School of Engineering and Applied Sciences, Harvard University, USA<br />
• Joint-angle sensors on compliant joints<br />
are robust and gentle<br />
• Methods are presented to<br />
• Detect finger contact with objects<br />
• Sweep space to determine object<br />
geometry and surrounding free space<br />
• Eliminates need for expensive sensors to<br />
determine object geometry for grasping in<br />
vision-limited settings
Session WeDT3 Continental Parlor 3 Wednesday, September 28, <strong>2011</strong>, 16:00–17:30<br />
Haptic Rendering & Object Recognition<br />
Chair M. Cenk Cavusoglu, Case Western Res. Univ.<br />
Co-Chair Masashi Konyo, Tohoku Univ.<br />
16:50–16:55 WeDT3.5<br />
Usability of a virtual reality system based on a<br />
wearable haptic interface<br />
Ingo Kossyk and Konstantin Kondak<br />
Robotics and Mechatronics, German Aerospace Center (DLR), Germany<br />
Jonas Dörr and Lars Raschendörfer<br />
Robotics and Biology Laboratory, Technische Universität Berlin, Germany<br />
• We evaluate the usability of a wearable<br />
haptic interface with subjective and<br />
objective measures<br />
• A presence questionnaire with 18 test<br />
persons served as a subjective measure<br />
• A task performance experiment was<br />
conducted for an objective measure<br />
17:00–17:15 WeDT3.7<br />
Enhancement of Human Force Perception by<br />
Multi-point Tactile Stimulation<br />
Lope Ben Porquis, Masashi Konyo and Satoshi Tadokoro<br />
Graduate School of Information Science, Tohoku University, Japan<br />
• Multiple skin-contacts are natural<br />
consequence when gripping an<br />
object or a tool.<br />
• Aggregation of strain energy density<br />
by applying tensile and compressive<br />
forces.<br />
• Experimentally verified that multicontact<br />
stimulation applied on a<br />
tactile interface can enhance force<br />
perception.<br />
• Vacuum stimuli can be used as<br />
complementary stimuli for inducing<br />
force sensation.<br />
SED<br />
Mechanoreceptors<br />
Tactile<br />
Stimuli<br />
Tool Segment<br />
Force<br />
Index Finger<br />
Tensile<br />
Force<br />
Participant<br />
Force<br />
Weight<br />
Compressive<br />
Force<br />
16:55–17:00 WeDT3.6<br />
Teaching by Touching: Interpretation of<br />
Tactile Instructions for Motion Development<br />
Fabio DallaLibera, Fransiska Basoeki, Hiroshi Ishiguro<br />
Department of System Innovation, Osaka University, Japan<br />
Takashi Minato<br />
ATR Hiroshi Ishiguro Laboratory, ATR, Japan<br />
Emanuele Menegatti<br />
Department of Information Engineering, Padua University, Italy<br />
• Touch is an important means of<br />
communication among humans<br />
• Sport or dance instructors communicate<br />
much information using simple touches<br />
• To permit natural interaction, humanoid<br />
robots should interpret the meaning of<br />
tactile instructions<br />
• A prototype system for the development of<br />
robot motions is presented, and a<br />
preliminary analysis on how humans<br />
employ touch to express their intention is<br />
provided.<br />
17:15–17:30 WeDT3.8<br />
Enhancement of Vibrotactile Sensitivity:<br />
Effects of Stationary Boundary Contacts<br />
Tatsuma Sakurai, Masashi Konyo and Satoshi Tadokoro<br />
Graduate School of Information Scienses, Tohoku University, Japan<br />
• Simultaneous contact with vibratory and<br />
stationary surfaces enhances human<br />
vibrotactile sensitivity, which we call<br />
stationary-boundary-contact(SBC)<br />
enhancement.<br />
• SBC produces a sharp line sensation<br />
along the gap between the vibratory and<br />
stationary surfaces<br />
• Psychophysical experiments showed the<br />
detection thresholds were more than<br />
tripled as low at lower frequencies<br />
• The mechanism behind SBC<br />
enhancement is investigated by using a<br />
finite element model for the skin<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–154–<br />
NOT SBC<br />
SBC<br />
Stationary<br />
Surface<br />
Skin<br />
Stationary<br />
Surface<br />
Vibratory<br />
Surface<br />
Vibratory<br />
Surface<br />
Vibratory<br />
Surface<br />
Skin<br />
Impact<br />
Stationary<br />
Surface<br />
Vibratory<br />
Surface<br />
Schema of Stationary Boundary<br />
Contacts
Session WeDT4 Continental Ballroom 4 Wednesday, September 28, <strong>2011</strong>, 16:00–17:30<br />
Industrial Forum: Medical Robotics<br />
Chair Paolo Dario, Scuola Superiore Sant'Anna<br />
Co-Chair<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–155–
Session WeDT5 Continental Ballroom 5 Wednesday, September 28, <strong>2011</strong>, 16:00–17:30<br />
Symposium: Robot Motion Planning: New Frameworks and High Performance<br />
Chair Maxim Likhachev, Carnegie Mellon Univ.<br />
Co-Chair Ron Alterovitz, Univ. of North Carolina at Chapel Hill<br />
16:00–16:15 WeDT5.1<br />
Multi-resolution H-Cost Motion Planning: A New<br />
Framework for Hierarchical Motion Planning for<br />
Autonomous Mobile Vehicles<br />
Raghvendra V. Cowlagi and Panagiotis Tsiotras<br />
School of Aerospace Engineering,<br />
Georgia Institute of Technology, Atlanta, GA, USA<br />
• Computationally efficient<br />
hierarchical motion planner that<br />
incorporates vehicle dynamical<br />
constraints<br />
• Higher-level: Provably complete<br />
multi-resolution path planner using<br />
wavelet transform-based cell<br />
decompositions<br />
• History-based transition costs in<br />
cell decomposition graph for<br />
including vehicle dynamical<br />
constraints<br />
• Lower-level: Local trajectory<br />
generation algorithm based on<br />
geometric constructions and<br />
model predictive control<br />
Schematic illustration of the proposed<br />
motion planning framework<br />
16:30–16:45 WeDT5.3<br />
Massively Parallelizing the RRT and the RRT∗<br />
Joshua Bialkowski and Emilio Frazzoli<br />
Department of Aeronautics and Astronautics,<br />
Massachussetts Institute of Technology, USA<br />
Sertac Karaman<br />
Department of Electrical Enginnering and Computer Science,<br />
Massachussetts Institute of Technology, USA<br />
• GPUs have exhibited recent performance<br />
growth and support for general purpose<br />
computation<br />
• There is potential for significant speed-up<br />
in sampling based motion planning<br />
implementations<br />
• Collision checking is the primary<br />
bottleneck in many implementations of<br />
motion planning problems<br />
• We investigate parallel collision checking<br />
for a multi-link robotic manipulator using<br />
GPU hardware<br />
An N-Link Robotic<br />
Manipulator<br />
16:15–16:30 WeDT5.2<br />
Multi-Scale LPA* with Low Worst-Case<br />
Complexity Guarantees<br />
Yibiao Lu and Xiaoming Huo<br />
H. Milton Stewart School of Industrial and System Engineering<br />
Oktay Arslan and Panagiotis Tsiotras<br />
D. Guggenheim School of Aerospace Engineering<br />
Georgia Institute of Technology, USA<br />
• Combine the incremental<br />
search with map abstraction<br />
• Utilize multiscale graphical<br />
structure constructed via<br />
beamlets<br />
• Improve both run-time<br />
efficiency and robustness at<br />
execution level<br />
• Theoretical proofs on<br />
computational complexity<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–156–<br />
Replanning via m-LPA*<br />
16:45–16:50 WeDT5.4<br />
Deterministic Kinodynamic Planning<br />
with hardware demonstrations<br />
François Gaillard, Michaël Soulignac and Cédric Dinont<br />
CS Dept, ISEN Lille, France<br />
Philippe Mathieu<br />
SMAC-LIFL, University of Lille 1, France<br />
� DKP is a deterministic hybrid approach for<br />
trajectory planning<br />
� Incrementally builds a tree of spline<br />
trajectories<br />
� Guarantees on trajectory feasibility and<br />
reproducibility of the results<br />
� Explicit geometrical representation of local<br />
reachable space<br />
Reachable<br />
space<br />
Iterative planning within<br />
reachable spaces
Session WeDT5 Continental Ballroom 5 Wednesday, September 28, <strong>2011</strong>, 16:00–17:30<br />
Symposium: Robot Motion Planning: New Frameworks and High Performance<br />
Chair Maxim Likhachev, Carnegie Mellon Univ.<br />
Co-Chair Ron Alterovitz, Univ. of North Carolina at Chapel Hill<br />
16:50–16:55 WeDT5.5<br />
Navigation Meshes for Realistic<br />
Multi-Layered Environments<br />
Wouter van Toll, Atlas F. Cook IV, Roland Geraerts<br />
Utrecht University, Netherlands<br />
• A multi-layered environment is<br />
a connected set of 2D floors.<br />
• Examples:<br />
multi-storey buildings<br />
train stations<br />
airports<br />
shopping malls<br />
• We implement a multi-layered<br />
navigation mesh and use this<br />
structure to perform path<br />
planning operations.<br />
17:00–17:15 WeDT5.7<br />
Adaptive Time Horizon for On-line Avoidance<br />
in Dynamic Environments<br />
Zvi Shiller 1 , Oren Gal 2 and Ariel Raz 1<br />
Dept. Mechanical Engineering and Mechatronics, Ariel University Center, Israel<br />
2 Mechanical Engineering, Technion, Israel<br />
• On-line avoidance of static and<br />
moving obstacles based on Velocity<br />
Obstacles (VO)<br />
• VO truncated at an adaptive time<br />
horizon<br />
• Adaptive time horizon allows<br />
sufficient time for safe avoidance<br />
• VO conservatively but tightly<br />
approximates the set of inevitable<br />
collision states (ICS)<br />
• Planner generates near-time optimal<br />
trajectories<br />
16:55–17:00 WeDT5.6<br />
Solving Shortest Path Problems with Curvature<br />
Constraints Using Beamlets<br />
Oktay Arslan and Panagiotis Tsiotras<br />
D. Guggenheim School of Aerospace Engineering,<br />
Georgia Institute of Technology, USA<br />
Xiaoming Huo<br />
H. Milton Stewart School of Industrial and System Engineering,<br />
Georgia Institute of Technology, USA<br />
• A new multiscale graph representation of<br />
the environment which uses beamlets<br />
• Computation of paths with more feasibility<br />
guarantees (i.e., bounded heading angle<br />
on the path)<br />
• Searching on a larger space of curves by<br />
adopting the beamlet graph representation<br />
• Being able to compute complex curves<br />
(i.e., a self-intersecting path)<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–157–<br />
Proposed approach can find a<br />
self-intersecting path which may<br />
result due to the type of heading<br />
angle constraint on the path<br />
17:15–17:30 WeDT5.8<br />
Spline Templates for Fast Path Planning in<br />
Unstructured Environments<br />
Marcel Häselich, Nikolay Handzhiysky,<br />
Christian Winkens and Dietrich Paulus<br />
Computer Sciences, University of Koblenz-Landau, Germany<br />
AGAS robotics, robots.uni-koblenz.de<br />
• Perception and representation of<br />
unstructured environments from multiple<br />
sensors<br />
• Efficient data structure with terrain<br />
negotiability<br />
• Path planning for an autonomous<br />
heavyweight outdoor robot<br />
• Fast algorithm based on precomputed<br />
spline templates (arrays of terrain cells)
Session WeDT6 Continental Ballroom 6 Wednesday, September 28, <strong>2011</strong>, 16:00–17:30<br />
Symposium: Marine Robotics: Control and Planning<br />
Chair Fumin Zhang, Georgia Inst. of Tech.<br />
Co-Chair Mandar Chitre, National Univ. of Singapore<br />
16:00–16:15 WeDT6.1*<br />
Semi-Plenary Invited Talk: Applications of Marine<br />
Robotic Vehicles<br />
Junku Yuh, Korea Aerospace University<br />
16:30–16:45 WeDT6.3<br />
3D-Surface Reconstruction for Partially<br />
Submerged Marine Structures<br />
Using an Autonomous Surface Vehicle<br />
Georgios Papadopoulos 1 , Hanna Kurniawati 2 , Ahmed S. B. M.<br />
Shariff 3 , Liang Jie Wong 3 , Nicholas M. Patrikalakis 1<br />
1 Massachusetts Institute of Technology, USA<br />
2 SMART Center, Singapore<br />
3 National University of Singapore, Singapore<br />
• Proposed Robotic Platform<br />
• GPS denied environment<br />
• Surface Reconstruction<br />
above water-line part<br />
• Both above and below<br />
water-line parts<br />
16:15–16:30 WeDT6.2<br />
Semi-Plenary Invited Talk: Applications of Marine<br />
Robotic Vehicles<br />
Junku Yuh, Korea Aerospace University<br />
16:45–16:50 WeDT6.4<br />
Path tracking: Combined path following<br />
and trajectory tracking<br />
for autonomous underwater vehicle<br />
Xianbo Xiang1,2 , Lionel Lapierre2 , Chao Liu2 and Bruno Jouvencel2 1Huazhong University of Science and Technology, China<br />
2Department of Robotics, LIRMM/CNRS, France<br />
• This paper proposes a novel<br />
path tracking control for<br />
autonomous underwater<br />
vehicles (AUVs), which blends<br />
the conventional path following<br />
and trajectory tracking control<br />
in order to achieve smooth<br />
spatial convergence and tight<br />
temporal performance as well<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–158–
Session WeDT6 Continental Ballroom 6 Wednesday, September 28, <strong>2011</strong>, 16:00–17:30<br />
Symposium: Marine Robotics: Control and Planning<br />
Chair Fumin Zhang, Georgia Inst. of Tech.<br />
Co-Chair Mandar Chitre, National Univ. of Singapore<br />
16:50–16:55 WeDT6.5<br />
Autonomous Data Collection from Underwater<br />
Sensor Networks using Acoustic Communication<br />
Geoffrey A. Hollinger and Gaurav S. Sukhatme<br />
Computer Science Department, University of Southern California, USA<br />
Urbashi Mitra<br />
Department of Electrical Engineering, University of Southern California, USA<br />
• Underwater sensor networks monitor<br />
phenomena such as seismic activity<br />
and environmental conditions<br />
• An Autonomous Underwater Vehicle<br />
(AUV) equipped with an acoustic<br />
modem can gather data from such<br />
networks<br />
• We propose and analyze approximation<br />
algorithms for coordinating the AUV<br />
based on probabilistic variants of TSP<br />
• Simulations using realistic acoustic<br />
communication modeling demonstrate<br />
substantial improvement versus prior<br />
techniques<br />
Example sensor deployment on the<br />
ocean floor to monitor environmental<br />
conditions. Such sensors remain in place<br />
for many months, and collecting data<br />
from them is a challenging problem.<br />
17:00–17:15 WeDT6.7<br />
Underwater SLAM with Robocentric Trajectory<br />
Using a Mechanically Scanned Imaging Sonar<br />
Antoni Burguera, Yolanda González, Gabriel Oliver<br />
Dept. Matemàtiques i Informàtica, Universitat de les Illes Balears, Spain<br />
• Underwater MSIS scan-based<br />
SLAM<br />
• Correction of motion induced<br />
scan distortions<br />
• Explicit trajectory representation<br />
relative to the robot (robocentric)<br />
• Mean position error of 1.15m after<br />
more than 600m experiment<br />
An experiment showing<br />
robocentric trajectory results and<br />
the ground truth (DGPS)<br />
16:55–17:00 WeDT6.6<br />
Modeling and Reactive Navigation<br />
of an Autonomous Sailboat<br />
Clement Petres, Miguel Romero, Frederic Plumet<br />
ISIR, UPMC Univ. Paris 06, France<br />
Bertrand Alessandrini<br />
Fluid Mechanics Laboratory, Ecole Centrale de Nantes, France<br />
• ASAROME project: Autonomous SAiling<br />
Robot for Oceanographic MEasurements<br />
• Experimental identification of a dynamic<br />
model of a sailboat � realistic simulator<br />
• Potential field based path planning<br />
method � real-time reactive navigation<br />
• Simulation of various navigation strategies<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–159–<br />
ASAROME prototype<br />
17:15–17:30 WeDT6.8<br />
A Lower Bound On Navigation Error for Marine<br />
Robots Guided by Ocean Circulation Models<br />
Klementyna Szwaykowska and Fumin Zhang<br />
Electrical and Computer Engineering, Georgia Institute of Technology, USA<br />
• We analyze effectiveness of using Ocean<br />
General Circulation Models (OGCMs) to<br />
guide autonomous marine robots<br />
• Controlled Lagrangian Particle Tracking<br />
(CLPT) used to study motion of<br />
autonomous agents in the ocean<br />
• We derive lower bound on error in<br />
predicted robot position, which depends<br />
on OGCM resolution<br />
• We show that long-term error in predicted<br />
robot position has bounded growth rate<br />
Error in robot position prediction<br />
during 2006 field experiment in<br />
Monterey Bay, CA.
Session WeDT7 Continental Parlor 7 Wednesday, September 28, <strong>2011</strong>, 16:00–17:30<br />
Tracking & Gait Analysis<br />
Chair Gian Luca Mariottini, Univ. of Texas at Arlington<br />
Co-Chair Domenico Prattichizzo, Istituto Italiano di Tecnologia<br />
16:00–16:15 WeDT7.1<br />
Identification of Mobile Entities Based on<br />
Trajectory and Shape Information<br />
Zeynep Yücel Tetsushi Ikeda Takahiro Miyashita Norihiro Hagita<br />
Advanced Telecommunications Research Institute International<br />
Japan<br />
• This study integrates motion characteristics and geometric shape<br />
models in object recognition from range samples.<br />
• The shape model is described by parameters of an elliptic fit and is<br />
utilized to obtain the prior probabilities.<br />
• The motion characteristics<br />
are described in terms of<br />
coherence quality as<br />
moving at the front, rear,<br />
side, or alone and is utilized<br />
to get the posterior<br />
probabilities.<br />
• The method is shown to<br />
yield 92% recognition rate<br />
in an uncontrolled field<br />
experiment with more than<br />
500 participants.<br />
Three entities moving coherently. e i and e j<br />
present side-by-side motion where e j and e k<br />
are in following motion.<br />
16:30–16:45 WeDT7.3<br />
Adaptive Human Shape Reconstruction via<br />
3D Head Tracking for Motion Capture in<br />
Changing Environment<br />
Kazuhiko Murasaki*, Masamichi Shimosaka**,<br />
Taketoshi Mori** and Tomomasa Sato**<br />
*Nippon Telegraph and Telephone Corporation, Japan<br />
**The University of Tokyo, Japan<br />
• Markerless motion capture techniques with dynamic background<br />
scenarios including furniture movements and light condition changes<br />
• Robust human silhouette reconstruction by the following 2 techniques<br />
• Iterative graph-cut methods with head positions<br />
• Novel tree-based multi-class recognition as multi-view face detectors<br />
16:15–16:30 WeDT7.2<br />
Marathoner Tracking Algorithms for a High<br />
Speed Mobile Robot<br />
Eui-Jung Jung, Byung-Ju Yi, Il Hong Suh, and Si Tae Noh<br />
Department of Electronics and System Engineering, Hanyag University, Korea<br />
Jae Hoon Lee<br />
Graduate School of Science and Engineering, Ehime University, Japan<br />
Shin’ichi Yuta<br />
Institute of Engineering Mechanics and Systems, University of Tsukuba, Japan<br />
• Tracking algorithms of a mobile robot that<br />
follows a human body moving at high speed<br />
in unstructured outdoor environment<br />
• The mobile robot is equipped with one laser<br />
range finder to perform high-speed human<br />
tracking and obstacle avoidance.<br />
• A waist part of the human body is found the<br />
best part of the human body for robust<br />
tracking by a laser range finder.<br />
• An obstacle avoidance algorithm<br />
considering the relative velocity between<br />
the robot and obstacles is proposed.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–160–<br />
Marathoner Following Robot<br />
16:45–16:50 WeDT7.4<br />
Cooperative active target tracking for heterogeneous<br />
robots with application to gait monitoring<br />
Fabio Morbidi1 , Christopher Ray2 , Gian Luca Mariottini1 ,<br />
1 ASTRA Robotics Laboratory, Dept. of Computer Science & Engineering<br />
2 Dept. of Kinesiology<br />
University of Texas at Arlington, USA<br />
• Cooperative active target-tracking for a team of heterogeneous robots<br />
equipped with 3-D range-finding sensors<br />
• Gradient-based controllers are designed for each robot and the<br />
Kalman-Bucy filter is used for estimation fusion<br />
• Robots use relative-position measurements to localize themselves in<br />
GPS-denied environments<br />
• Validation on a team of aerial and ground robots tracking the motion of<br />
a human subject for a gait-monitoring task
Session WeDT7 Continental Parlor 7 Wednesday, September 28, <strong>2011</strong>, 16:00–17:30<br />
Tracking & Gait Analysis<br />
Chair Gian Luca Mariottini, Univ. of Texas at Arlington<br />
Co-Chair Domenico Prattichizzo, Istituto Italiano di Tecnologia<br />
16:50–16:55 WeDT7.5<br />
Fast Visual People Tracking using a Feature-<br />
Based People Detector<br />
Achim Königs and Dirk Schulz<br />
Unmanned Systems Group,<br />
Fraunhofer Institute for Communication,<br />
Information Processing and Ergonomics FKIE, Germany<br />
• Visual people tracking that combines<br />
feature-based object detection with<br />
feature tracking.<br />
• Provides a fast tracking process and<br />
allows to specialize to tracking a specific<br />
object.<br />
• The tracking process is based on tracking<br />
the features found by the detector.<br />
• Features are tracked by carrying out a<br />
correspondence search from frame to<br />
frame in combination with a voting<br />
scheme for determining a person’s center<br />
position in the image.<br />
Tracked person, distinguished<br />
from other similar person. White<br />
dots denote the tracked features.<br />
17:00–17:15 WeDT7.7<br />
Particle Filter Based Monocular Human Tracking<br />
with a 3D Cardbox Model and a Novel<br />
Deterministic Resampling Strategy<br />
Ziyuan Liu + , Dongheui Lee + and Wolfgang Sepp *<br />
+ Technische Universität München, Germany<br />
* German Aerospace Center, Germany<br />
• Particle filter based monocular human<br />
upper body tracking<br />
• A novel deterministic resampling strategy<br />
(DRS)<br />
• A new 3D articulated human upper body<br />
model with the name 3D cardbox model<br />
• Quantitative evaluation of the tracking<br />
results<br />
• Comparison between DRS and standard<br />
stratified resampling strategy (SRS)<br />
• Comparison with a commercial motion<br />
capturing system<br />
Tracking example: projected human<br />
model (top), silhouette matching<br />
(middle), raw image (bottom)<br />
16:55–17:00 WeDT7.6<br />
A Nonlinear Controller for People Guidance<br />
based on Omnidirectional Vision<br />
Flávio Garcia Pereira<br />
Department of Engineering and Computing, Federal University of Espírito<br />
Santo, Brazil.<br />
Milton César Paes Santos and Raquel Frizera Vassallo<br />
Department of Electrical Engineering, Federal University of Espírito Santo,<br />
Brazil.<br />
• Guide-tour robot;<br />
• Human’s movement;<br />
• Nonlinear final pose controller ;<br />
• Lyapunov analysis;<br />
• Omnidirectional vision;<br />
• Human detection.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–161–<br />
A robot navigating and a person<br />
following it.<br />
17:15–17:30 WeDT7.8<br />
Non-Drifting Limb Angle Measurement Relative<br />
to the Gravitational Vector<br />
Andrew Petruska and Sanford Meek<br />
Mechanical Engineering, University of Utah, USA<br />
• Two accelerometers and one<br />
rate gyro are used measure<br />
inclination for feedback control<br />
and gait analysis.<br />
• The estimate of inclination from<br />
the two accelerometers is not<br />
biased by angular motion.<br />
• The inclination is blended with<br />
the angular velocity using a<br />
Kalman filter.<br />
• RMS error for both sinusoidal<br />
and constant velocity inputs was<br />
measured to be less than 5� for<br />
gait-like frequencies.<br />
A comparison between the Kalman filter<br />
implemented with the dynamic angle<br />
estimation (solid) and without (dashed).
Session WeDT8 Continental Parlor 8 Wednesday, September 28, <strong>2011</strong>, 16:00–17:30<br />
Multirobot Coordination & Modular Robots<br />
Chair Kasper Stoy, Univ. of Southern Denmark<br />
Co-Chair Stephen L. Smith, Univ. of Waterloo<br />
16:00–16:15 WeDT8.1<br />
Modeling Mutual Capabilities in<br />
Heterogeneous Teams for Role Assignment<br />
Somchaya Liemhetcharat and Manuela Veloso<br />
School of Computer Science, Carnegie Mellon University, USA<br />
• Agents in heterogeneous teams have different capabilities.<br />
• These capabilities are not only individual, but depend on the<br />
composition of the team and the role assignments.<br />
• We contribute the Mutual State Capability-Based Role Assignment<br />
(MuSCRA) model that models pairwise interactions between agents<br />
and their mutual state.<br />
• The MuSCRA model is applicable to dynamic environments where<br />
state affects the performance of agents in the team.<br />
16:30–16:45 WeDT8.3<br />
Evaluation of a Power Management System for<br />
Heterogeneous Modules<br />
in Self-Reconfigurable Multi-Module Systems<br />
Zhuowei Wang, Florian Cordes,<br />
Alexander Dettmann, and Roman Szczuka<br />
German Research Center for Artificial Intelligence<br />
Robotics Innovation Center, Germany<br />
• Power source and diverse<br />
functionality are separated from each<br />
other and encapsulated in different<br />
modules<br />
• Homogeneous power management<br />
system can support more than 250W<br />
power consumption in an established<br />
multi-module system<br />
• Evaluation of key features of the<br />
power management system<br />
Self-reconfiguration of<br />
heterogeneous modular system<br />
supported by the power<br />
management system<br />
16:15–16:30 WeDT8.2<br />
Collision Avoidance for Persistent Monitoring in<br />
Multi-Robot Systems with Intersecting Trajectories<br />
Daniel E. Soltero Stephen L. Smith<br />
EECS, MIT, USA ECE, Waterloo, Canada<br />
Daniela Rus<br />
EECS, MIT, USA<br />
• Group of robots equipped with sensors with<br />
finite footprints persistently monitor a<br />
changing environment.<br />
• Collision and dead-lock avoidance<br />
procedure is developed to treat intersecting<br />
trajectories.<br />
• Analysis of the effects of developed<br />
procedure on the stability of the persistent<br />
controller.<br />
• Implementation on ground vehicles and<br />
aerial vehicles.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–162–<br />
Four robots (yellow-filled circles)<br />
performing a persistent task where<br />
collision avoidance is essential<br />
due to intersecting paths.<br />
16:45–16:50 WeDT8.4<br />
Generalized Programming of Modular Robots<br />
through Kinematic Configurations<br />
M. Bordignon, K. Stoy, and U. Schultz<br />
Modular Robotics Lab, University of Southern Denmark, Denmark<br />
• Programmer models kinematics of<br />
each kind of module in the robot<br />
(~30 lines of geometric declarations)<br />
• Toolchain generates:<br />
– runtime support for computing<br />
forward kinematics<br />
– high-level primitives and low-level<br />
code for morphology-independent<br />
programming of robot<br />
– complete world descriptions for robot<br />
simulation<br />
• General programming language for<br />
modular robots<br />
MODEL<br />
PROGRAM
Session WeDT8 Continental Parlor 8 Wednesday, September 28, <strong>2011</strong>, 16:00–17:30<br />
Multirobot Coordination & Modular Robots<br />
Chair Kasper Stoy, Univ. of Southern Denmark<br />
Co-Chair Stephen L. Smith, Univ. of Waterloo<br />
16:50–16:55 WeDT8.5<br />
Energy-Aware Coverage Control with<br />
Docking for Robot Teams<br />
Jason Derenick, Nathan Michael, and Vijay Kumar<br />
GRASP Laboratory, University of Pennsylvania, USA<br />
• A Centroidal Voronoi Tessellation<br />
(CVT)-based control law is formulated<br />
that leverages a weighting scheme that<br />
embeds an agent’s tradeoff to achieve<br />
its coverage mission and to maintain its<br />
energy reserve.<br />
• The controller is motivated by longterm<br />
persistent surveillance<br />
applications that may exceed the<br />
runtime of an agent.<br />
• As a robot’s energy-reserve depletes, it<br />
is drawn towards a docking station (for<br />
recharging) while still participating in<br />
coverage as the formation adjusts.<br />
• Stability of the distributed (among<br />
Voronoi-neighbors) control law is<br />
considered and simulation results are<br />
presented verifying the analysis.<br />
17:00–17:15 WeDT8.7<br />
Intent Inference and Strategic Escape in Multi-<br />
Robot Games with Physical Limitations<br />
Aris Valtazanos and Subramanian Ramamoorthy<br />
School of Informatics, University of Edinburgh, United Kingdom<br />
• Strategic decision-making framework for<br />
physical multi-robot games (e.g. robotic<br />
soccer)<br />
• Decouple sensory and strategic uncertainty<br />
• Sensory uncertainty: Reachable Set<br />
Particle Filter – combine probabilistic<br />
state estimation with dynamical constraints<br />
• Strategic uncertainty: Probabilistic intent<br />
templates fitted to the adversary and<br />
adjusted through data-driven estimation<br />
16:55–17:00 WeDT8.6<br />
Optimization of Personal Distribution<br />
for Evacuation Guidance based on Vector Field<br />
Masafumi Okada and Teruhisa Ando<br />
Dept. of Mechanical Sciences and Engineering, Tokyo TECH, Japan<br />
• This paper proposes an optimization<br />
method of personal distribution for<br />
evacuation guidance.<br />
• Based on the evacuation route, the<br />
intention of the evacuee is modeled by<br />
vector field.<br />
• The action of the operator is also modeled<br />
by vector field considering indicating<br />
direction.<br />
• By using the autonomous mobile robots<br />
and a radio controlled robot, the proposed<br />
method is evaluated in the quasi-human<br />
environment.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–163–<br />
Guidance experiment with<br />
a radio controlled robot<br />
17:15–17:30 WeDT8.8<br />
Optimisation Model and Exact Algorithm for<br />
Autonomous Straddle Carrier Scheduling at<br />
Automated Container Terminals<br />
Binghuang Cai, Shoudong Huang, Dikai Liu,<br />
Shuai Yuan, and Gamini Dissanayake<br />
Centre for Autonomous Systems, University of Technology, Sydney, Australia<br />
Haye Lau and Daniel Pagac<br />
Patrick Technology Systems, Sydney, Australia<br />
• An optimisation model is presented for<br />
Autonomous Straddle Carriers Scheduling<br />
(ASCS) at automated container terminals.<br />
• An exact algorithm based on Branch-and-<br />
Bound-with-Column-Generation (BBCG) is<br />
presented for the ASCS problem solving.<br />
• The BBCG algorithm is compared to<br />
Binary-integer-Programming-with-<br />
Dynamic-Programming (BPDP) and<br />
Exhaustive-Search-with-Permutation-and-<br />
Combination (ESPC) algorithms.<br />
• Simulation results and discussions<br />
demonstrate the effectiveness and<br />
efficiency of the presented model and<br />
algorithm.
Session WeDT9 Continental Parlor 9 Wednesday, September 28, <strong>2011</strong>, 16:00–17:30<br />
Calibration & Identification<br />
Chair Giorgio Metta, Istituto Italiano di Tecnologia (IIT)<br />
Co-Chair Jozsef Kovecses, McGill Univ.<br />
16:00–16:15 WeDT9.1<br />
Skin Spatial Calibration<br />
Using Force/Torque Measurements<br />
Andrea Del Prete, Lorenzo Natale,<br />
Francesco Nori and Giorgio Metta<br />
RBCS, Italian Institute of Technology, Italy<br />
Simone Denei, Fulvio Mastrogiovanni and Giorgio Cannata<br />
DIST, University of Genova, Italy<br />
• This paper deals with the problem of<br />
estimating the position of tactile elements<br />
(i.e. taxels) mounted on a robot body part<br />
• Taxel positions are estimated measuring<br />
contact forces/torques applied on the<br />
sensorized part using a F/T sensor<br />
mounted on the robot kinematic chain<br />
• Differently from previous works, this<br />
procedure provides not only the network<br />
topology, but also a metric information<br />
• The method has been validated with<br />
experiments on the iCub humanoid robot,<br />
leading to a final average error of about 7<br />
mm<br />
Taxel position estimations.<br />
As reference the edges of the<br />
triangular skin modules are drawn.<br />
16:30–16:45 WeDT9.3<br />
Static Calibration of the DLR Medical Robot<br />
MIRO, a Flexible Lightweight Robot with<br />
Integrated Torque Sensors<br />
Julian Klodmann, Rainer Konietschke,<br />
Alin Albu-Schäffer and Gerd Hirzinger<br />
German Aerospace Center (DLR), Germany<br />
• Stepwise approach to calibrate<br />
robots in the assembled state<br />
• Planning of static poses in the<br />
entire workspace to obtain<br />
convergence<br />
16:15–16:30 WeDT9.2<br />
Muscle Strength and Mass Distribution<br />
Identification Toward Subject-Specific<br />
Musculoskeletal Modeling<br />
Mitsuhiro Hayashibe<br />
DEMAR INRIA and LIRMM, France<br />
Gentiane Venture<br />
Tokyo University of Agriculture and Technology, Japan<br />
Ko Ayusawa, Yoshihiko Nakamura<br />
University of Tokyo, Japan<br />
• Framework toward subject-specific<br />
musculoskeletal modeling,<br />
• Mass Distribution Identification to<br />
improve the joint torque estimation,<br />
• Muscle Strength Identification to<br />
improve the muscle force estimation<br />
accuracy,<br />
• This first result highlights that the<br />
reliable muscle force estimation could<br />
be extracted after these identifications.<br />
16:45–16:50 WeDT9.4<br />
Simultaneous Calibration, Localization,<br />
and Mapping<br />
Rainer Kümmerle 1 , Giorgio Grisetti 2,1 , and Wolfram Burgard 1<br />
1 Dept. of Computer Science, University of Freiburg, Germany<br />
2 Dept. of Systems and Computer Science, Sapienza University of Rome, Italy<br />
• The calibration parameters of a mobile<br />
robot play a substantial role in navigation<br />
tasks.<br />
• Simultaneously estimate a map of the<br />
environment, the position of the on-board<br />
sensors, and its kinematic parameters<br />
• Our approach performs online estimation<br />
of the calibration parameters and is able<br />
to adapt to non-stationary changes.<br />
• Tested in simulation and with a wide range<br />
of real-world data<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–164–<br />
Effects of carrying a load on the<br />
wheel diameters and the odometry
Session WeDT9 Continental Parlor 9 Wednesday, September 28, <strong>2011</strong>, 16:00–17:30<br />
Calibration & Identification<br />
Chair Giorgio Metta, Istituto Italiano di Tecnologia (IIT)<br />
Co-Chair Jozsef Kovecses, McGill Univ.<br />
16:50–16:55 WeDT9.5<br />
Experimental Evaluation of New Methods for<br />
In-Situ Calibration of Attitude and Doppler<br />
Sensors for Underwater Vehicle Navigation<br />
Giancarlo Troni and Louis L. Whitcomb<br />
Department of Mechanical Engineering<br />
Johns Hopkins University, USA<br />
• Development and in-water experimental<br />
evaluation of two new methods for in-situ<br />
calibration of the alignment rotation matrix<br />
between Doppler sonar velocity sensors<br />
and gyrocompass attitude sensors.<br />
• We report alignment calibration methods<br />
employing only internal vehicle navigation<br />
sensors, e.g. for velocity, acceleration,<br />
attitude, and depth.<br />
• Results from laboratory experiments<br />
comparing these methods to previously<br />
reported techniques indicate satisfactory<br />
performance of the proposed methods.<br />
JHU ROV in the Johns Hopkins<br />
Hydrodynamic Test Facility<br />
17:00–17:15 WeDT9.7<br />
Relative Accuracy Enhancement System<br />
Based on Internal Error Range Estimation for<br />
External Force Measurement in<br />
Construction Manipulator<br />
Mitsuhiro Kamezaki, Hiroyasu Iwata, and Shigeki Sugano<br />
Department of Modern Mechanical Engineering, Waseda University, Japan<br />
• Our framework adopts a relative accuracy<br />
improvement strategy without correcting the<br />
models for the practicality, comprising:<br />
• i. Quantifying the internal error range (IER)<br />
by using the sum of the maximal measuring<br />
errors of static and dynamic friction forces<br />
• ii. Calculating the error force vector by using<br />
IER to select cylinders that have less error<br />
• iii. Outputting the front load vector by using<br />
the cylinders whose error force vector is<br />
minimum<br />
• Experimental results indicate that our<br />
framework can enhance the relative<br />
accuracy of external force measurement<br />
Instrumented hydraulic arm<br />
16:55–17:00 WeDT9.6<br />
New Method for Global Identification<br />
of the Joint Drive Gains of Robots<br />
using a Known Payload Mass<br />
M. Gautier (1) and S. Briot (2)<br />
(1) University of Nantes, IRCCyN, France<br />
(2) CNRS, IRCCyN, France<br />
• Off-line identification of robot dynamic parameters<br />
• Inverse Dynamic Identification Model and<br />
LS technique (IDIM-LS)<br />
• Identification of the total joint drive gains in one<br />
step<br />
• Using only the weighed mass of a payload, no<br />
need CAD values<br />
• Using motor current reference, joint position,<br />
available robot’s controller data<br />
• Mixing data from trajectory without load +<br />
trajectory with load<br />
• Validation experiment, 4 first joints of a 6 dof<br />
industrial robot<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–165–<br />
FStaubli RX90 robot<br />
17:15–17:30 WeDT9.8<br />
Recursive State-Parameter Estimation<br />
of Haptic Robotic Systems<br />
Arash Mohtat, Kamran Ghaffari Toiserkan,<br />
and József Kövecses<br />
Centre for Intelligent Machines, McGill University, Canada<br />
• Simultaneous state-parameter estimation<br />
via unscented Kalman filtering (UKF).<br />
• Overcoming limitations: linearity of the<br />
models in the parameters; need for<br />
velocities and accelerations; and, need for<br />
differentiation and linearization.<br />
• The recursive nature allows for offline<br />
processing and online implementation.<br />
• Haptic Applications: State-parameter<br />
estimation of the haptic device;<br />
Environmental parameters identification.<br />
The identification experiment:<br />
The haptic device interacting with<br />
a virtual box.
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–166–
8:00-9:30<br />
Technical Program Digest<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–167–<br />
Thursday<br />
September 29, <strong>2011</strong><br />
Session ThAT1 ⎯⎯ Force and Stiffness Control<br />
Session ThAT2 ⎯⎯ Range and RGB-D Sensing<br />
Session ThAT3 ⎯⎯ Discrete & Kinodynamic Planning<br />
Session ThAT4 ⎯⎯ Symposium: Stochasticity in Robotics and Biological Systems I<br />
Session ThAT5 ⎯⎯ Symposium: Humanoid Robotics and Biped Locomotion<br />
Session ThAT6 ⎯⎯ Symposium: Robots that Can See I<br />
Session ThAT7 ⎯⎯ Mechanisms: Joint Design<br />
Session ThAT8 ⎯⎯ Autonomous Vehicles<br />
Session ThAT9 ⎯⎯ Towards Anthropomimetic Robots<br />
10:00-11:30<br />
Session ThBT1 ⎯⎯ Control for Manipulation & Grasping<br />
Session ThBT2 ⎯⎯ Mapping<br />
Session ThBT3 ⎯⎯ Randomized Planning & Kinematic Control<br />
Session ThBT4 ⎯⎯ Symposium: Stochasticity in Robotics and Biological Systems II<br />
Session ThBT5 ⎯⎯ Symposium: Humanoid Technologies<br />
Session ThBT6 ⎯⎯ Symposium: Robots that Can See II<br />
Session ThBT7 ⎯⎯ Medical Robots & Systems<br />
Session ThBT8 ⎯⎯ Search & Rescue Robots<br />
Session ThBT9 ⎯⎯ Intelligent Transportation Systems (Automotive)<br />
(continues on next page)
14:45-16:15<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–168–<br />
Thursday<br />
September 29, <strong>2011</strong><br />
(continued)<br />
Session ThCT1 ⎯⎯ Manipulation<br />
Session ThCT2 ⎯⎯ Industrial Robots<br />
Session ThCT3 ⎯⎯ Marine Systems I<br />
Session ThCT4 ⎯⎯ Symposium: (Self-)assembly from the Nano to the Macro Scale:<br />
State of the Art and Future Directions<br />
Session ThCT5 ⎯⎯ Symposium: Humanoid Applications<br />
Session ThCT6 ⎯⎯ Symposium: Computer Vision for Robotics<br />
Session ThCT7 ⎯⎯ Exoskeleton Robots & Gait Rehabilitation<br />
Session ThCT8 ⎯⎯ Aerial Robots: Navigation, Tracking & Landing<br />
Session ThCT9 ⎯⎯ Swarms and Flocks<br />
16:45-17:45<br />
Session ThDT1 ⎯⎯ Forum: On Robotics Conferences: A Town Hall Meeting<br />
(in Continental Ballroom)<br />
Session ThDT3 ⎯⎯ Marine Systems II<br />
Session ThDT4 ⎯⎯ Novel System Designs: Locomotion (in Continental Parlor 2)<br />
Session ThDT5 ⎯⎯ Climbing & Brachiation (in Continental Parlor 1)<br />
Session ThDT6 ⎯⎯ Novel System Designs: Sensing and Manipulation<br />
(in Continental Parlor 9)<br />
Session ThDT7 ⎯⎯ Medical Robotics: Motion Planning & State Estimation<br />
Session ThDT8 ⎯⎯ Aerial Robots
Session ThAT1 Continental Parlor 1 Thursday, September 29, <strong>2011</strong>, 08:00–09:30<br />
Force and Stiffness Control<br />
Chair Robert James Webster III, Vanderbilt Univ.<br />
Co-Chair Jonathan Hurst, Oregon State Univ.<br />
08:00–08:15 ThAT1.1<br />
Trajectory Tracking Controller With Dynamic<br />
Gains for Mobile Robots<br />
Cassius Resende<br />
Dept. of Electrical Engineering, Federal University of Espírito Santo, Brazil<br />
Felipe Espinosa, Ignacio Bravo<br />
Dept. of Electronics, University of Alcalá, Spain<br />
Mário Sarcinelli-Filho, Teodiano F. Bastos-Filho<br />
Dept. of Electrical Engineering, Federal University of Espírito Santo, Brazil<br />
• A Takashi-Sugeno fuzzy controller is<br />
proposed to guide a unicycle mobile<br />
robot during trajectory tracking<br />
• Stability of the closed loop system<br />
using such controller is proven, based<br />
on Lyapunov’s theory<br />
• The low complexity of the proposed<br />
controller makes it suitable for<br />
implementation in low-profile<br />
processors<br />
• Theoretical analysis and experimental<br />
results validate the proposed controller<br />
08:30–08:45 ThAT1.3<br />
Force Control for<br />
Planar Spring-Mass Running<br />
Devin Koepl and Jonathan Hurst<br />
Dynamic Robotics Laboratory, Oregon State University, USA<br />
• Presents a novel control<br />
strategy for spring-leg robot<br />
running.<br />
• Strategy makes good use of<br />
passive dynamics, conserving<br />
energy.<br />
• Incorporates active force control<br />
for robustness to disturbances.<br />
• Requires no external sensing,<br />
so practical for real world springleg<br />
robots.<br />
Actuated spring-leg model, with<br />
series hip and leg elasticity.<br />
08:15–08:30 ThAT1.2<br />
Multi-Priority Control in Redundant Robotic Systems<br />
Hamid Sadeghian †, Luigi Villani ††, Mehdi Keshmiri †,<br />
Bruno Siciliano ††<br />
†Mechanical Eng. Dept., Isfahan Univ. of Tech.,Iran<br />
††DIS, Univ. di Napoli Federico II, Italy<br />
• Dynamic level multi-priority control algorithm<br />
as a general framework for the whole body<br />
control of robotic systems is presented.<br />
• Some of the previously developed results<br />
are easily formalized using this general<br />
framework.<br />
• Null-space impedance control is proposed<br />
as one of the main results and is evaluated<br />
by means of computer simulation.<br />
08:45–08:50 ThAT1.4<br />
Deflection-Based Force Sensing<br />
for Continuum Robots:<br />
A Probabilistic Approach<br />
D. Caleb Rucker and Robert J. Webster III<br />
Department of Mechanical Engineering, Vanderbilt University, USA<br />
Medical & Electromechanical Design Laboratory<br />
• Flexible Continuum Robots Can Be Used As Force Sensors<br />
• Pose Measurements + Mechanics-Based Model => Force Estimate<br />
• Extended Kalman Filter Used to Quantify Uncertainty in Estimates<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–169–
Session ThAT1 Continental Parlor 1 Thursday, September 29, <strong>2011</strong>, 08:00–09:30<br />
Force and Stiffness Control<br />
Chair Robert James Webster III, Vanderbilt Univ.<br />
Co-Chair Jonathan Hurst, Oregon State Univ.<br />
08:50–08:55 ThAT1.5<br />
Optimality Principles in Stiffness Control<br />
The VSA Hammer<br />
Manolo Garabini † , Felipe Belo † , Andrea Passaglia † ,<br />
Paolo Salaris † † ‡<br />
, and Antonio Bicchi<br />
†<br />
Interdept. Research Center “E. Piaggio”, University of Pisa, Italy<br />
‡<br />
Italian Institute of Technology, Genova, Italy<br />
• Importance of Variable Stiffness Actuators<br />
(VSA) in safety and performance of robots<br />
• Optimality principles regulating variation of<br />
stiffness in very dynamic tasks where<br />
impacts are maximized<br />
• New optimization problem: maximize<br />
velocity of a link at a given final position,<br />
e.g., for best hammer impact<br />
• Fixed stiffness case: there exists an optimal linear spring for given link and<br />
motor inertia<br />
• VSA case: varying spring stiffness during execution improves final<br />
performance of hammering task<br />
• Optimal control law obtained analytically and validated with experimental<br />
tests<br />
09:00–09:15 ThAT1.7<br />
Optimal and Fault-Tolerant Torque Control of Servo<br />
Motors Subject to Voltage and Current Limits<br />
Farhad Aghili<br />
Canadian Space Agency (CSA), Canada<br />
• Optimal management of motor<br />
excitation currents can significantly<br />
increase the rated speed and<br />
torque of the motor in the face of<br />
the voltage and current limits of<br />
the drivers.<br />
• In the case of one phase failure,<br />
controller optimally reshapes the<br />
stator currents of the remaining<br />
phases for continuing accurate<br />
torque production<br />
• A closed-form solution for the<br />
optimal phase currents rendering<br />
the control algorithm suitable for<br />
real-time implementation<br />
• Experimental results<br />
The architecture of the optimal<br />
and fault-tolerant torque<br />
controller<br />
08:55–09:00 ThAT1.6<br />
A novel stiffness node controller which enables<br />
simultaneous regulation of torque and stiffness<br />
in multi-muscle driven joints<br />
Salvatore Annunziata, Jan Paskarbeit and Axel Schneider<br />
Faculty of Technology, University of Bielefeld, Germany<br />
• Co-activation of two antagonistic<br />
muscles is in general considered to<br />
vary stiffness at joint level.<br />
• However, angular joint positions may<br />
exist for which the stiffness does not<br />
vary with co-activation.<br />
• At those positions any concurrent<br />
torque/stiffness controller is bound to<br />
fail.<br />
• A four-muscle based controller is<br />
presented that enables torque/stiffness<br />
control also at these positions.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–170–<br />
(a) 4-muscle driven joint<br />
(b) Torque/angle muscle setup<br />
(c) Stiffness node<br />
09:15–09:30 ThAT1.8<br />
Online Motion Selection for Semi-Optimal<br />
Stabilization using Reverse-Time Tree<br />
Chyon Hae Kim and Hiroshi Tsujino<br />
Honda Research Institute, Japan<br />
Shigeki Sugano<br />
Graduate School of Creative Science<br />
and Engineering Waseda University, Japan<br />
• We discuss a general method for<br />
creating an approximately optimal<br />
stabilization system.<br />
• The proposed system creates a<br />
reverse-time tree using RApid Semioptimal<br />
MOtion-planning (RASMO).<br />
• We developed an online motion<br />
selection method that selects a<br />
semi-optimal motion according to the<br />
current state of a machine.<br />
• Calculation speed and optimality are<br />
validated in a simulation.<br />
Offline Process: Creating reverse-time tree by RASMO<br />
Input Goal States<br />
Search States<br />
Prune Paths<br />
Is the Tree Depth Sufficient?<br />
Get Reverse-Time Tree<br />
Online Process:� Path selection and feedback control<br />
Yes No<br />
The proposed system<br />
Constraints<br />
Resolver
Session ThAT2 Continental Parlor 2 Thursday, September 29, <strong>2011</strong>, 08:00–09:30<br />
Range and RGB-D Sensing<br />
Chair Robert Zlot, CSIRO<br />
Co-Chair Kai Oliver Arras, Univ. of Freiburg<br />
08:00–08:15 ThAT2.1<br />
3D Object Recognition in Range Images Using<br />
Visibility Context<br />
Eunyoung Kim and Gerard Medioni<br />
Computer Science Department, University of Southern California, USA<br />
• Goal<br />
• Recognize and localize queried objects<br />
in cluttered environments using the<br />
Primesensor<br />
• Only utilize depth information for robots<br />
to work under bad or no illumination<br />
• Contributions<br />
• Propose a new point pair feature using<br />
visibility context that contains<br />
discriminative description<br />
• Improve an pose estimation method<br />
using point pair matches<br />
• Outperform two state-of-the-art methods<br />
in terms of recall & precision rate<br />
08:30–08:45 ThAT2.3<br />
Comparative Evaluation of Range Sensing<br />
Technologies for Underground Void Modeling<br />
Uland Wong, Aaron Morris, Colin Lea, James Lee,<br />
Chuck Whittaker, Ben Garney and Red Whittaker<br />
Robotics Institute, Carnegie Mellon University, USA<br />
• 10 sensors evaluated across three<br />
underground tunnel environments<br />
and one lab environment<br />
• Technologies assessed include 5 ToF<br />
LIDAR sensors, flash LIDAR, FMCW,<br />
stereo vision, the Kinect and others.<br />
• Metrics of evaluation inspired by 3D<br />
modeling performance in field<br />
(density distribution, accuracy,<br />
coverage)<br />
• Test environments and methodology<br />
are sufficiently general for<br />
applicability of results in related<br />
domains<br />
Sensor modeling is performed in three<br />
representative tunnel environments.<br />
08:15–08:30 ThAT2.2<br />
Fast Plane Extraction in 3D Range Data<br />
Based on Line Segments<br />
Kristiyan Georgiev, Ross Creed, Rolf Lakaemper<br />
Department of Computer and Information Sciences<br />
Temple University, USA<br />
• Extract line segments from raw data.<br />
• Determine connected components,<br />
representing possible candidate sets of<br />
coplanar segments.<br />
• Perform region growing on candidate sets<br />
to establish planes from coplanar<br />
segments.<br />
• Region growing contains a fast O(1) plane<br />
update technique in its core, leading to a<br />
real time performance of the algorithm.<br />
08:45–08:50 ThAT2.4<br />
Tracking a Depth Camera: Parameter Exploration<br />
for Fast ICP<br />
François Pomerleau, Stéphane Magnenat, Francis Colas,<br />
Ming Liu, and Roland Siegwart<br />
Department of Mechanical and Process Engineering, ETH Zurich, Switzerland<br />
• Modular ICP library openly<br />
available<br />
• Test datasets available with<br />
millimeter accuracy ground truth<br />
• Capability of 3D pose tracking at<br />
30 Hz with reasonable precision<br />
• Sound statistical evaluation over<br />
a range of parameters<br />
• Library accessible to embedded<br />
systems<br />
One path of depth camera tracked (red) with ICP at<br />
30 Hz vs. the measured ground truth (green)<br />
• Standalone C++ library:<br />
http://github.com/ethz-asl/libpointmatcher<br />
• ROS node:<br />
www.ros.org/wiki/modular_cloud_matcher<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–171–
Session ThAT2 Continental Parlor 2 Thursday, September 29, <strong>2011</strong>, 08:00–09:30<br />
Range and RGB-D Sensing<br />
Chair Robert Zlot, CSIRO<br />
Co-Chair Kai Oliver Arras, Univ. of Freiburg<br />
08:50–08:55 ThAT2.5<br />
Watertight Surface Reconstruction of Caves<br />
from 3D Laser Data<br />
Claude Holenstein, Robert Zlot, and Michael Bosse<br />
Autonomous Systems Laboratory, ICT Centre<br />
CSIRO, Australia<br />
• Watertight surface reconstruction of<br />
caves and other closed environments<br />
from data collected on a mobile sensor<br />
• Developed algorithm is scalable<br />
(kilometers of cave data), and able to<br />
remove returns from dynamic<br />
obstacles occluding sensor field of<br />
view<br />
• High resolution surface models have<br />
been produced for further scientific<br />
study<br />
Surface reconstruction of a section of<br />
Chifley cave. Returns from people on<br />
the mapping team are circled in red.<br />
09:00–09:15 ThAT2.7<br />
People Tracking in RGB-D Data With<br />
On-line Boosted Target Models<br />
Matthias Luber, Luciano Spinello and Kai O. Arras<br />
Social Robotics Lab, University of Freiburg, Germany<br />
• We present a 3D people tracking<br />
approach in RGB-D data<br />
• Using on-line learning, we train targetspecific<br />
models with three types of<br />
RGB-D features and a confidence<br />
maximization search in 3D space<br />
• Integration into multi-hypothesis tracker<br />
with track interpretation feedback<br />
to avoid drift of on-line detectors<br />
and to fill gaps of misdetections<br />
• Results demonstrate clearly improved<br />
tracking performance: reduction of false<br />
negative tracks by 50% and reduction of<br />
track confusions by 24%<br />
3D trajectories of<br />
six persons<br />
08:55–09:00 ThAT2.6<br />
People Detection in RGB-D Data<br />
Luciano Spinello and Kai O. Arras<br />
Social Robotics Lab, University of Freiburg, Germany<br />
• We present Combo-HOD, a fast and<br />
robust RGB-D people detector<br />
• Probabilistic fusion of<br />
Histogram of Oriented Gradients (HOG)<br />
and Histogram of Oriented Depths (HOD)<br />
• Uses a 3-fold accelerated depthinformed<br />
scale space search<br />
• In a comprehensive comparison with<br />
alternative methods, Combo-HOD<br />
outperforms all others with a 13% EER<br />
increase over vision-only HOG<br />
• 85% EER in a range up to 8m from<br />
sensor (Kinect)<br />
• GPU implementation runs at 30 Hz<br />
09:15–09:30 ThAT2.8<br />
‘Misspelled’ Visual Words in Unsupervised<br />
Range Data Classification<br />
Michael Firman and Simon Julier<br />
Department of Computer Science, University College London, UK<br />
• LiDAR scans can be affected by<br />
significant noise, which varies in<br />
different situations<br />
• We explore the effect noise has<br />
on the performance of<br />
unsupervised classifiers, using<br />
different local features<br />
The decrease in classification accuracy as noise is added to an<br />
artificial dataset; we also used real LiDAR data<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–172–<br />
• Topic models are found to be<br />
less robust classifiers than<br />
k-means when noise is added<br />
• Spin images are shown to be<br />
more robust to noise than their<br />
angular derivative
Session ThAT3 Continental Parlor 3 Thursday, September 29, <strong>2011</strong>, 08:00–09:30<br />
Discrete & Kinodynamic Planning<br />
Chair Edwin Olson, Univ. of Michigan<br />
Co-Chair Luigi Biagiotti, Univ. of Modena and Reggio Emilia<br />
08:00–08:15 ThAT3.1<br />
Improved Hierarchical Planner Performance<br />
Using Local Path Equivalence<br />
Ross A. Knepper and Matthew T. Mason<br />
The Robotics Institute, Carnegie Mellon University, USA<br />
• Motion planners face two types<br />
of choice: discrete decisions,<br />
and continuous optimization<br />
problems.<br />
• Using an equivalence relation<br />
among local paths, we separate<br />
the two types.<br />
• We classify path sets according<br />
to route among obstacles.<br />
• 2-stage path selection process:<br />
1) Select a route (set of paths)<br />
progressing toward the goal, and<br />
2) Execute the optimal (safest)<br />
path within the class.<br />
Discrete Continuous<br />
decision optimization<br />
08:30–08:45 ThAT3.3<br />
Choosing Landmarks for Risky Planning<br />
Elizabeth Murphy and Peter Corke<br />
School of Engineering Systems, QUT, Australia<br />
Paul Newman<br />
Mobile Robotics Group, Oxford University, UK<br />
• Risky Planning is a path planning<br />
technique applicable to probabilistic<br />
costmaps that achieves 70% efficiency<br />
increases over normal heuristic search<br />
methods.<br />
• The risk of not returning the shortest path<br />
is quantified and used to leverage<br />
planning speed ups.<br />
• Risky Planning requires the<br />
precomputation of heuristics for A* type<br />
searches which use landmarks to<br />
precompute an informative lower bound<br />
between any two cells in the costmap<br />
• This paper recommends strategies for<br />
placing these landmarks so that risk<br />
bounds of risky planning are accurate and<br />
precomputation is minimized.<br />
An overhead image of an area to<br />
be planned over, and some<br />
strategically placed landmarks<br />
08:15–08:30 ThAT3.2<br />
Simplicial Dijkstra and A* Algorithms for<br />
Optimal Feedback Planning<br />
Dmitry S. Yershov and Steven M. LaValle<br />
Department of Computer Science, U of Illinois at Urbana-Champaign, USA<br />
• Considered Euclidean shortest path<br />
problem in n-dimensional configuration<br />
space among obstacles<br />
• Adaptation of Dijkstra’s and A* algorithms<br />
to compute approximate cost-to-go<br />
function over simplicial complex<br />
• Numerical methods are carefully<br />
designed, analyzed, and proven to<br />
converge<br />
• Methods are demonstrated to work for 2D<br />
and 3D examples. Test results show<br />
significant improvement of Simplicial A*<br />
over Simplicial Dijkstra’s<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–173–<br />
Level sets of cost-to-go function<br />
in 2D and 3D environments<br />
08:45–08:50 ThAT3.4<br />
A Graph Traversal based Algorithm for Obstacle<br />
Detection using Lidar or Stereo<br />
Sujit Kuthirummal, Aveek Das, and Supun Samarasekera<br />
Vision and Robotics Lab, SRI International Sarnoff, USA<br />
• A computationally efficient approach to<br />
obstacle detection (OD) that is applicable<br />
to both structured and unstructured<br />
environments.<br />
• Instead of explicitly identifying obstacles,<br />
we explicitly detect scene regions that are<br />
traversable – safe for the robot to go to –<br />
from the current position.<br />
• The algorithm does not make any flatworld<br />
assumptions or need sensor pitchroll<br />
compensation . It also accounts for<br />
overhanging structures.<br />
• We demonstrate our approach using both<br />
lidar and stereo sensors.<br />
Top: Lidar based OD<br />
Bottom: Stereo based OD
Session ThAT3 Continental Parlor 3 Thursday, September 29, <strong>2011</strong>, 08:00–09:30<br />
Discrete & Kinodynamic Planning<br />
Chair Edwin Olson, Univ. of Michigan<br />
Co-Chair Luigi Biagiotti, Univ. of Modena and Reggio Emilia<br />
08:50–08:55 ThAT3.5<br />
Iterative Path Optimization for<br />
Practical Robot Planning<br />
Andrew Richardson and Edwin Olson<br />
Computer Science & Engineering, University of Michigan, USA<br />
• Two-stage path planning method to<br />
decouple path clearance from<br />
minimum distance path computation<br />
• Avoids some potential field pitfalls with<br />
partially-observed obstacle maps<br />
• Iterative optimization to maximize<br />
clearance (up to bound) and<br />
smoothness<br />
• Algorithm tested extensively on our<br />
indoor/outdoor 14 robot testbed<br />
Smoothed path (blue) maximizes<br />
clearance at A but not B, as<br />
clearance is sufficient. Original path<br />
in orange<br />
09:00–09:15 ThAT3.7<br />
Geometric Maneuverability<br />
with Applications to Low Reynolds Number Swimming<br />
Ross L. Hatton and Howie Choset<br />
Robotics Institute, CMU, USA<br />
Lisa J. Burton and A. E. Hosoi<br />
Department of Mechanical Engineering, MIT, USA<br />
• Locomoting systems use shape changes,<br />
rather than vector thrust, to move through<br />
their environment<br />
• We introduce a geometric framework for<br />
describing their maneuverability<br />
• Local maneuverability is similar to<br />
manipulability for robot arms and is<br />
described by an ellipsoid<br />
• Cyclic maneuverability characterizes<br />
bulk motion over gait or stroke cycles and<br />
is described by a polygon<br />
• Examples are provided for low Reynolds<br />
number swimmers<br />
08:55–09:00 ThAT3.6<br />
Analysis of the Discontinuities in Prioritized<br />
Tasks-Space Control Under Discreet Task<br />
Scheduling Operations<br />
François Keith and Pierre-Brice Wieber<br />
INRIA Rhône-Alpes, France<br />
Nicolas Mansard<br />
CNRS-LAAS, France<br />
Abderrahmane Kheddar<br />
CNRS-UM2 LIRMM, France – CNRS-AIST JRL, UMI3218/CRT Japan<br />
• Presentation of the methods defining<br />
a control law by a hierarchy between<br />
tasks and their continuity properties.<br />
• Illustration of their discontinuous<br />
behavior during discrete events such<br />
as task insertion, removal, swap.<br />
• Definition of a method ensuring a<br />
continuous control law even during<br />
these events.<br />
• Experimentation on HRP-2 robot.<br />
09:15–09:30 ThAT3.8<br />
Input Shaping via B-spline Filters<br />
for 3-D Trajectory Planning<br />
Luigi Biagiotti<br />
Department of Information Engineering<br />
University of Modena and Reggio Emilia, Italy<br />
Claudio Melchiorri<br />
Department of Electronics, Informatics and Systems<br />
University of Bologna, Italy<br />
• Uniform B-splines of degree p are<br />
equivalent to the output of a chain<br />
composed by p average filters<br />
• The frequency spectrum of B-spline<br />
trajectories is completely determined<br />
by the degree p and by time period<br />
T between the knots<br />
• These parameters are selected with the purpose of suppressing residual<br />
vibrations, that may be present because elastic phenomena affecting the<br />
robotic system<br />
• The effectiveness of the proposed approach is proved by considering a<br />
cartesian robot with elastic joints<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–174–
Session ThAT4 Continental Ballroom 4 Thursday, September 29, <strong>2011</strong>, 08:00–09:30<br />
Symposium: Stochasticity in Robotics and Biological Systems I<br />
Chair M. Ani Hsieh, Drexel Univ.<br />
Co-Chair Harry Asada, MIT<br />
08:00–08:15 ThAT4.1*<br />
Semi-Plenary Invited Talk: Set-based Control in<br />
Stochastic Dynamical Systems: Making Almost<br />
Invariant Sets more Invariant<br />
Ira Schwartz, US Naval Research Laboratory<br />
08:30–08:45 ThAT4.3<br />
Noise, Bifurcations, and Modeling of Interacting<br />
Particle Systems<br />
Luis Mier-y-Teran-Romero and Ira B. Schwartz<br />
Nonlinear Systems Dynamics Section, Naval Research Laboratory, USA<br />
Eric Forgoston<br />
Department of Mathematical Sciences, Montclair State University, USA<br />
• Modeled self-propelling particles<br />
interacting through pairwise force in<br />
presence of noise and time delay.<br />
• Identified parameter regions resulting in<br />
different coherent structures of the swarm.<br />
• Showed how stochasticity modifies the<br />
coherent structures and attractor<br />
sensitivity.<br />
Time snapshots of transitioning<br />
swarms<br />
08:15–08:30 ThAT4.2<br />
Semi-Plenary Invited Talk: Set-based Control in<br />
Stochastic Dynamical Systems: Making Almost<br />
Invariant Sets more Invariant<br />
Ira Schwartz, US Naval Research Laboratory<br />
08:45–08:50 ThAT4.4<br />
Probability of success in stochastic robot<br />
navigation with state feedback<br />
Shridhar Shah<br />
Department of Mechanical Engineering, University of Delaware, USA<br />
Chetan Pahlajani<br />
Department of Mathematical Sciences, University of Delaware, USA<br />
Herbert Tanner<br />
Department of Mechanical Engineering, University of Delaware, USA<br />
• Robots with stochastic dynamics need<br />
to navigate in constrained<br />
environments.<br />
• Collision avoidance cannot be<br />
guaranteed with bounded inputs.<br />
• A deterministic, bounded input<br />
controller has a given probability of<br />
success in steering to target region,<br />
under stochastic uncertainty.<br />
• Explicit analytic solutions possible for<br />
single integrator point robots in a<br />
constrained, spherical but obstaclefree<br />
environments.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–175–<br />
Two sample paths for stochastic<br />
navigation using gradient<br />
descent. The green path reaches<br />
the goal while blue path hits the<br />
obstacle boundary.
Session ThAT4 Continental Ballroom 4 Thursday, September 29, <strong>2011</strong>, 08:00–09:30<br />
Symposium: Stochasticity in Robotics and Biological Systems I<br />
Chair M. Ani Hsieh, Drexel Univ.<br />
Co-Chair Harry Asada, MIT<br />
08:50–08:55 ThAT4.5<br />
A Stochastic Approach to Dubins Feedback<br />
Control for Target Tracking<br />
Ross Anderson and Dejan Milutinović<br />
Applied Mathematics&Statistics, University of California, Santa Cruz, USA<br />
• Motivated by an Unmanned Aerial Vehicle<br />
maintaining a nominal distance from an<br />
unpredictable target<br />
• Target is assumed to perform a random<br />
walk<br />
• Dyn. Programming on a Markov chain that<br />
is consistent with stochastic kinematics<br />
produces a feedback controller that can<br />
also be applied for deterministic targets<br />
• Effect of the level of uncertainty of target<br />
motion on the control law is examined<br />
Dubins vehicle tracking<br />
a random walk<br />
09:00–09:15 ThAT4.7<br />
Stochastic Tracking of Migrating Live Cells<br />
Interacting with 3D Gel Environment Using<br />
Augmented-Space Particle Filters<br />
Lee-Ling Ong 1 , Levi Wood 3 , Marcelo Ang Jr. 2, H.Harry Asada 1,3<br />
1 Singapore-MIT Alliance for Research and Technology, Singapore<br />
2 Department of Mechanical Engineering, NUS, Singapore<br />
3 Department of Mechanical Engineering, MIT, Cambridge, MA, USA<br />
• Types of images obtained at discrete<br />
time steps using confocal microscopy of<br />
3D cell migration experiments:<br />
a) 3D images of stained cell nuclei and<br />
b) 2D images of the gel<br />
• Our approach is to track a joint state<br />
representation, using the concepts of<br />
Simultaneous Localization and Mapping<br />
(SLAM)<br />
• This allows mathematically consistent<br />
updates from both sources and<br />
augmentation of new cells and conduit<br />
slices to the state<br />
Bottom figure shows visualization<br />
of the extracted conduit and cells<br />
using IMARIS (from Bitplane)<br />
08:55–09:00 ThAT4.6<br />
Optimization of Stochastic Strategies for<br />
Spatially Inhomogeneous Robot Swarms:<br />
A Case Study in Commercial Pollination<br />
Spring Berman and Radhika Nagpal<br />
Dept. of Computer Science, Harvard University, USA<br />
Ádám Halász<br />
Dept. of Mathematics,West Virginia University, USA<br />
• Scalable approach to optimizing<br />
decentralized robot control policies that<br />
produce a target collective behavior<br />
• Can maintain system performance by<br />
incorporating robot feedback that relies<br />
on coarse-grained localization<br />
• Swarm abstracted to linear ODE model<br />
whose parameters are optimized and<br />
mapped onto robot controllers<br />
• Application: Achieve uniform and<br />
nonuniform pollination of a field with a<br />
swarm of robotic bees<br />
09:15–09:30 ThAT4.8<br />
Stochastic Dynamics of Bacteria Propelled<br />
Spherical Micro-Robots<br />
Veaceslav Arabagi*, Bahareh Behkam**, and Metin Sitti*<br />
*Dept. of Mechanical Engineering, Carnegie Mellon University, USA<br />
**Dept. of Mechanical Engineering, Virginia Tech, USA<br />
• Stochastic dynamic model<br />
of bacteria propelled<br />
micro-beads<br />
• Stokes flow superposition<br />
of flow fields<br />
• Investigate stiffness of<br />
bacteria flagellar hook<br />
through simulations<br />
• Bead radius sensitivity<br />
• Number of attached<br />
bacteria sensitivity<br />
• Studying the long time<br />
random walk behavior of<br />
micro-beads<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–176–
Session ThAT5 Continental Ballroom 5 Thursday, September 29, <strong>2011</strong>, 08:00–09:30<br />
Symposium: Humanoid Robotics and Biped Locomotion<br />
Chair Ambarish Goswami, Honda Res. Inst.<br />
Co-Chair Seung-kook Yun, Honda Res. Inst.<br />
08:00–08:15 ThAT5.1*<br />
Semi-Plenary Invited Talk: Angular Momentum-based<br />
Humanoid Robot Control<br />
Shuuji Kajita, National Inst. of AIST<br />
08:30–08:45 ThAT5.3<br />
Momentum-Based Reactive Stepping Controller on<br />
Level and Non-level Ground for Humanoid Robot<br />
Push Recovery<br />
Seung-kook Yun and Ambarish Goswami<br />
Honda Research Institute, USA<br />
• We present a momentum-based reactive<br />
stepping controller for humanoid robot<br />
push recovery<br />
• Regulation of combinations of linear and<br />
angular momenta allows the controller to<br />
selectively encourage the robot to recover<br />
its balance with or without taking a step<br />
• A reference stepping location called<br />
Generalized Foot Placement Estimator<br />
(GFPE), is computed by modeling the<br />
humanoid as a passive rimless wheel with<br />
two spokes<br />
• GFPE is calculated only once and it does<br />
not move<br />
• The approach works for both level and<br />
non-level grounds<br />
Humanoid takes a downhill step<br />
after a forward push<br />
08:15–08:30 ThAT5.2<br />
Semi-Plenary Invited Talk: Angular Momentum-based<br />
Humanoid Robot Control<br />
Shuuji Kajita, National Inst. of AIST<br />
08:45–08:50 ThAT5.4<br />
Time-Independent, Spatial<br />
Human Coordination For Humanoids<br />
Jean-Christophe PALYART LAMARCHE 1 , Olivier BRUNEAU 2<br />
and Jean-Guy FONTAINE 1<br />
1 Italian Institute of Technology (IIT), Italy<br />
2 Laboratoire d’Ingénierie des Systèmes de Versailles (LISV), France<br />
• Methodology to parameterize movements<br />
with 12 Cartesian gait descriptors, and<br />
one unique gait parameter<br />
• Gait coordination for kinematic simulation<br />
of an anthropomorphic and NAO virtual<br />
models<br />
• Real NAO robot walking<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–177–
Session ThAT5 Continental Ballroom 5 Thursday, September 29, <strong>2011</strong>, 08:00–09:30<br />
Symposium: Humanoid Robotics and Biped Locomotion<br />
Chair Ambarish Goswami, Honda Res. Inst.<br />
Co-Chair Seung-kook Yun, Honda Res. Inst.<br />
08:50–08:55 ThAT5.5<br />
The Effect of Swing Leg Retraction on<br />
Running Energy Efficiency<br />
Matt Haberland and Sangbae Kim<br />
Dept. of Mechanical Engineering, Massachusetts Institute of Technology, USA<br />
J. G. Daniël Karssen and Martijn Wisse<br />
Fac. of Mechanical Engineering, Delft University of Technology, The<br />
Netherlands<br />
• Swing leg retraction (SLR):<br />
rearward rotation of airborne front<br />
leg before touchdown<br />
• SLR significantly improves<br />
energetic efficiency of prismaticlegged<br />
running robots<br />
• SLR reduces touchdown energy<br />
loss, impact forces, and foot<br />
slippage simultaneously<br />
• SLR is recommended as an element<br />
of any control strategy for running<br />
robots<br />
Model consists of point mass body, distributed mass legs,<br />
torque actuator, massless springs, and point feet<br />
09:00–09:15 ThAT5.7<br />
Angular Momentum: Insights into Walking and<br />
Its Control<br />
Bradford C. Bennett<br />
Orthopaedic Surgery, University of Virginia, USA<br />
Thomas Robert<br />
IFSTTAR, Université de Lyon, France<br />
Shawn D. Russell<br />
Mechanical and Aerospace Engineering, University of Virginia, USA<br />
• Nondimensional angular<br />
momentum organization was<br />
independent of walking speed.<br />
• The uncontrolled manifold (UCM)<br />
hypothesis revealed a control<br />
synergy for some angular<br />
momenta.<br />
• Control of bipedal robots may<br />
benefit from simulating the<br />
organization of human gait<br />
X<br />
Y<br />
Z<br />
0.02<br />
0<br />
-0.02<br />
0.02<br />
0<br />
-0.02<br />
0.02<br />
0<br />
Slow<br />
CWS<br />
Fast<br />
-0.02<br />
0 20 40 60 80 100<br />
Gait Cycle (%)<br />
The whole body normalized angular<br />
momentum during a gait cycle for slow,<br />
comfortable, and fast walking.<br />
08:55–09:00 ThAT5.6<br />
Practical Bipedal Walking Control<br />
on Uneven Terrain<br />
Using Surface Learning and Push Recovery<br />
Seung-Joon Yi, Daniel D. Lee<br />
ESE, University of Pennsylvania, USA<br />
Byoung-Tak Zhang<br />
CSE, Seoul National University, Korea<br />
Dennis Hong<br />
ME, Virginia Tech, USA<br />
• Bipedal walking in human environment is<br />
made difficult by two sources of error: the<br />
surface unevenness and external<br />
disturbances.<br />
• Electrically compliant swing foot and<br />
onboard sensors are used to estimate the<br />
current surface gradient, and an online<br />
learning algorithm is used to learn the<br />
surface model from noisy estimates<br />
• Biomechanically motivated hierarchical<br />
push recovery controller is used to reject<br />
external disturbances<br />
• Implemented on the DARwIn-OP<br />
commercial humanoid robot<br />
09:15–09:30 ThAT5.8<br />
XoR: Hybrid Drive Exoskeleton Robot<br />
That Can Balance<br />
Sang-Ho Hyon, Jun Morimoto, Takamitsu Matsubara,<br />
Tomoyuki Noda and Mitsuo Kawato<br />
Computational Neuroscience Lab, ATR, Japan<br />
• A novel exoskeleton robot prototype is<br />
developed<br />
• Pneumatic muscles and electric motors<br />
are arranged in a optimal way to achieve<br />
weight-reduction and torque-controllability<br />
• Autonomous postural control using hybrid<br />
drive is presented<br />
• Experimental video is attached<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–178–
Session ThAT6 Continental Ballroom 6 Thursday, September 29, <strong>2011</strong>, 08:00–09:30<br />
Symposium: Robots that Can See I<br />
Chair José Neira, Univ. de Zaragoza<br />
Co-Chair Dieter Fox, Univ. of Washington<br />
08:00–08:15 ThAT6.1*<br />
Semi-Plenary Invited Talk: Vision at Nanoscales<br />
Yu Sun, University of Toronto<br />
08:30–08:45 ThAT6.3<br />
Visual Place Categorization in Maps<br />
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08:15–08:30 ThAT6.2<br />
Semi-Plenary Invited Talk: Vision at Nanoscales<br />
Yu Sun, University of Toronto<br />
08:45–08:50 ThAT6.4<br />
CD SLAM - Continuous Localization and<br />
Mapping in a Dynamic World<br />
Katrin Pirker and Matthias Rüther and Horst Bischof<br />
Institute for Computer Graphics and Vision, Graz University of Technology,<br />
Austria<br />
• we perfrom large-scale,<br />
continuous, monocular localization<br />
and mapping<br />
• we handle short- and long-term<br />
scene dynamics by adding visibility<br />
information to each map point<br />
• efficient keyframe organization<br />
facilitates sliding window bundle<br />
adjustment and speeds up loop<br />
closure correction<br />
• scalability and localization<br />
accuracy is measured in a longterm<br />
experiment in a mixed<br />
indoor/outdoor scenery<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–179–
Session ThAT6 Continental Ballroom 6 Thursday, September 29, <strong>2011</strong>, 08:00–09:30<br />
Symposium: Robots that Can See I<br />
Chair José Neira, Univ. de Zaragoza<br />
Co-Chair Dieter Fox, Univ. of Washington<br />
08:50–08:55 ThAT6.5<br />
Vision-based Mobile Robot’s SLAM and<br />
Navigation in Crowded Environments<br />
Hiroshi Morioka, Sangkyu Yi<br />
Department of Computational Intelligence and System Science,<br />
Tokyo Institute of Technology, Japan<br />
Osamu Hasegawa<br />
Imaging Science and Engineering Laboratory,<br />
Tokyo Institute of Technology, Japan<br />
• We propose vision-based SLAM and<br />
navigation method that is effective even in<br />
crowded environments.<br />
• Proposed method extracts robust 3D<br />
feature points from sequential vision<br />
images and odometry.<br />
• We can eliminate unstable feature points<br />
extracted from dynamic objects.<br />
• We present experiments showing the<br />
utility of our approach in crowded<br />
environments.<br />
09:00–09:15 ThAT6.7<br />
Real-Time Photo-Realistic 3D Mapping<br />
for Micro Aerial Vehicles<br />
Lionel Heng, Gim Hee Lee,<br />
Friedrich Fraundorfer, and Marc Pollefeys<br />
Computer Vision and Geometry Lab, ETH Zürich, Switzerland<br />
• A stereo or Kinect camera generates<br />
a point cloud; a 3D occupancy grid is<br />
built.<br />
• We use IMU measurements to<br />
establish a 1-point RANSAC for<br />
visual odometry (VO) estimates.<br />
• The occupancy grid, images, and VO<br />
estimates are transmitted wirelessly<br />
to a ground control station (GCS).<br />
• The GCS incrementally constructs a<br />
3D textured map via a viewdependent<br />
projective texturing<br />
method.<br />
3D Textured Map from Kinect data<br />
08:55–09:00 ThAT6.6<br />
Vision Based Attitude And Altitude Estimation<br />
For UAVs In Dark Environments<br />
Ashutosh Natraj<br />
MIS Laboratory, University of Picardie Jules Verne, France<br />
Cédric Demonceaux<br />
Le2I Laboratory, University of Burgundy, France<br />
Pascal Vasseur<br />
LITIS Laboratory, University of Rouen, France<br />
Peter Sturm<br />
INRIA Grenoble – Rhônes-Alpes, France<br />
• A new sensor based on a laser projector<br />
and a fish-eye camera is proposed for<br />
UAV applications.<br />
• Attitude and altitude can be estimated in<br />
dark environment with good accuracy and<br />
in real-time.<br />
• The laser projects a circle on the ground<br />
then captured by the fish-eye camera.<br />
• The paper demonstrates the geometric<br />
relations between ground, the laser circle<br />
and the camera and proposes a simple<br />
and fast algorithm for parameter<br />
estimation<br />
09:15–09:30 ThAT6.8<br />
Stereo Visual Odometry for Pipe Mapping<br />
Peter Hansen 1 , Hatem Alismail 2 , Brett Browning 1,2<br />
and Peter Rander 2<br />
1 QRI8 Lab, Carnegie Mellon University, Qatar<br />
2 Robotics Institute/NREC, Carnegie Mellon University, USA<br />
• Generating appearance maps for<br />
inspection of Liquified Natural Gas pipes.<br />
• Using imagery from a customized stereo<br />
camera equipped on a pipe crawling robot.<br />
• Camera pose and scene point estimates<br />
obtained using Perspective-n-Points (PnP)<br />
and Sparse Bundle Adjustment (SBA).<br />
• Accurate pose estimates with error less<br />
than 1% for distance traveled.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–180–<br />
Our stereo camera system,<br />
and an appearance map of the<br />
internal surface of a carbon<br />
steel pipe.
Session ThAT7 Continental Parlor 7 Thursday, September 29, <strong>2011</strong>, 08:00–09:30<br />
Mechanisms: Joint Design<br />
Chair Irene Sardellitti, Italian Inst. of Tech.<br />
Co-Chair Jean-François Brethe, LE HAVRE Univ.<br />
08:00–08:15 ThAT7.1<br />
Robust Estimation of Variable Stiffness<br />
in Flexible Joints<br />
Fabrizio Flacco and Alessandro De Luca<br />
Dipartimento di Informatica e Sistemistica,<br />
Università di Roma “La Sapienza”, Rome, Italy<br />
Irene Sardellitti and Nikos G. Tsagarakis<br />
Advanced Robotics Lab, Italian Institute of Technology, Genova, Italy<br />
• Stiffness is estimated using only position<br />
measurements on the motor sides and<br />
motor dynamic parameters<br />
• Residual based method to estimate the<br />
joint flexibility torque<br />
• Modified kinematic Kalman filter to handle<br />
discretization and quantization errors<br />
• Enhanced recursive least squares that<br />
does not suffer from lack of persistent<br />
excitation<br />
• Simulations and Experiments with the IIT<br />
AwAS joint<br />
08:30–08:45 ThAT7.3<br />
A bio-inspired condylar hinge joint<br />
for mobile robots<br />
Appolinaire C. Etoundi, Stuart C. Burgess<br />
Mechanical Engineering, University of Bristol, UK<br />
Ravi Vaidyanathan<br />
Mechanical Engineering, Imperial College London, UK<br />
• A bio-inspired design of hinge<br />
joint for mobile robots based on<br />
the human knee<br />
• Key feature of the design is its<br />
simplicity and similarity to the<br />
mammalian knee joint<br />
• Prototype model mimics the<br />
curved profiles of the human<br />
knee joint in order to achieve<br />
the benefits of high conformity<br />
and high stiffness and strength<br />
Femur<br />
Bush assembly<br />
Cap<br />
Nylon cable<br />
Strut x2<br />
Tibia<br />
Prototype coldylar joint<br />
08:15–08:30 ThAT7.2<br />
Cartesian stiffness matrix of manipulators with<br />
passive joints: analytical approach<br />
Anatol Pashkevich 1,2 , Alexandr Klimchik 1,2 ,<br />
Stephane Caro 2 and Damien Chablat 2<br />
1 Ecole des Mines de Nantes, France<br />
2 Institut de Recherches en Communications et en Cybernetique de Nantes<br />
• Analytical expressions for stiffness matrix<br />
computation of serial kinematic chain with<br />
passive joints (stiffness mapping for<br />
manipulators with passive joints)<br />
• Recursive procedure for sequentially<br />
modification of the original stiffness matrix<br />
in accordance with the geometry of each<br />
passive joints.<br />
• Aggregation of the serial chain stiffness<br />
matrices with respect to any given<br />
reference point of the mobile platform<br />
• Ability to produce both singular and nonsingular<br />
stiffness matrices for serial and<br />
parallel manipulators<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–181–<br />
Virtual Joint Models of kinematic<br />
chain and parallel manipulator<br />
08:45–08:50 ThAT7.4<br />
The Mechanism of the Linear Load-Sensitive<br />
Continuously Variable Transmission<br />
with the Spherical Driving Unit<br />
Kenjiro TADAKUMA1 , Riichiro TADAKUMA2 (Yamagata University),<br />
Kazuki TERADA3 , Aiguo MING3 , Makoto SHIMOJO3 ,<br />
Mitsuru Higashimori1 , Makoto Kaneko1 ,<br />
1. Osaka University, 2. Yamagata University, 3. UEC, Japan<br />
• The proposed CVT mechanism consists of<br />
spherical drive, drive axis, motor housing,<br />
fixed bracket and linear sliding plate.<br />
• The mechanism changes the reduction ratio<br />
continuously by inclination angle of active<br />
rotational axis.<br />
• This linear mechanism has a load-sensitive<br />
function by changing inclination of active<br />
rotational axis in response to the load.<br />
• The prototype mechanical model linear loadsensitive<br />
continuously variable<br />
transmission has been developed and the<br />
effectiveness of the proposed mechanism<br />
has been confirmed by the experiments.<br />
Figure caption is optional,<br />
use Arial Narrow 20pt
Session ThAT7 Continental Parlor 7 Thursday, September 29, <strong>2011</strong>, 08:00–09:30<br />
Mechanisms: Joint Design<br />
Chair Irene Sardellitti, Italian Inst. of Tech.<br />
Co-Chair Jean-François Brethe, LE HAVRE Univ.<br />
08:50–08:55 ThAT7.5<br />
Dynamic model of a Hyper-redundant Octopuslike<br />
Manipulator for Underwater Applications<br />
Rongjie Kang, Emanuele Guglielmino, David T. Branson<br />
and Darwin G. Caldwell<br />
Advanced Robotics Department, Italian Institute of Technology, Italy<br />
Asimina Kazakidi, Dimitris P. Tsakiris and John A. Ekaterinaris<br />
Foundation for Research and Technology – Hellas (FORTH), Greece<br />
• Octopus arm anatomy and morphology<br />
• Kinematics of an octopus arm inspired<br />
manipulator<br />
• Dynamic modeling for a single segment<br />
with parallel mechanism<br />
• Multiple-segment model<br />
• External forces due to the aquatic<br />
environment, such as buoyancy and<br />
hydrodynamic forces<br />
09:00–09:15 ThAT7.7<br />
Granular Stochastic Modeling of<br />
Robot Micrometric Precision<br />
Brethé Jean-François<br />
GREAH, Le Havre University, France<br />
• Modeling and quantifying robot precision when information from<br />
external sensors in the operating area is available<br />
• Pros and cons of usual precision criteria, especially repeatability<br />
and accuracy in this micrometric context<br />
• Computing the worst error position in the micrometric range<br />
• Granular stochastic modeling means holes and aggregates in the<br />
micrometric structure !<br />
• Comparison to other precision modeling (input and output error<br />
modeling, interval analysis)<br />
08:55–09:00 ThAT7.6<br />
Three Module Lumped Element model of a<br />
Continuum Arm Section<br />
Nivedhitha Giri and Ian D. Walker<br />
Department of Electrical and Computer Engineering, Clemson University,<br />
United States of America<br />
• A continuum arm section is modeled using<br />
lumped model elements (mass, springs<br />
and dampers)<br />
• The model parameters are chosen to<br />
match the physical model of Octarm VI<br />
• Principles of Lagrangian Dynamics are<br />
used to derive the generalized forces in<br />
the system<br />
• Numerical results are compared with<br />
measurements<br />
• The linear approximation model provides<br />
sufficient information about the section’s<br />
configuration with a slight tradeoff in<br />
accuracy<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–182–<br />
Linearized Dynamic model
Session ThAT8 Continental Parlor 8 Thursday, September 29, <strong>2011</strong>, 08:00–09:30<br />
Autonomous Vehicles<br />
Chair Karl Iagnemma, MIT<br />
Co-Chair William Smart, Washington Univ. in St. Louis<br />
08:00–08:15 ThAT8.1<br />
Avoiding steering actuator saturation in off-road mobile<br />
robot path tracking via predictive velocity control<br />
Oliver Hach, Roland Lenain<br />
Cemagref, France<br />
Benoit Thuilot, Philippe Martinet<br />
Ifma and Lasmea, Université Blaise Pascal, France<br />
• High accurate control for off-road mobile<br />
robots<br />
• Observer to estimate grip conditions<br />
• Predictive control to ensure tracking<br />
accuracy and limit velocity in order to<br />
preserve path achievability<br />
• Algorithm applied to avoid actuator<br />
saturation: the maximal admissible<br />
velocity is computed to limit the steering<br />
angle to its saturation<br />
Views of the experimental offroad<br />
mobile robot<br />
08:30–08:45 ThAT8.3<br />
Integrating Stereo Structure<br />
for Omnidirectional Trail Following<br />
Christopher Rasmussen, Yan Lu, and Mehmet Kocamaz<br />
Dept. Computer & Information Sciences, University of Delaware, USA<br />
• A system for autonomous hiking/mountain-biking trail following is<br />
discussed<br />
• Structural information derived from dense stereo is integrated with color<br />
appearance information to segment and track the trail<br />
• The utility of structural information is demonstrated on ground truth<br />
segmentations, and live run results are presented<br />
Height snakes overlaid on height map for trail hypothesis<br />
08:15–08:30 ThAT8.2<br />
Consistent Pile-Shape Quantification for<br />
Autonomous Wheel Loaders<br />
Martin Magnusson and Håkan Almqvist<br />
AASS, Örebro University, Sweden<br />
• Experimental comparison of<br />
several algorithms for quantifying<br />
pile shapes.<br />
• Evaluate consistency w.r.t.<br />
viewpoint and sensor<br />
configuration.<br />
• Extensions of previous algorithms.<br />
• Novel method for segmenting pile<br />
from background in point-cloud<br />
data.<br />
• Propose to use quadric fitting for<br />
estimating relevant pile quantities.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–183–<br />
Application shown above,<br />
variance of convexity measures<br />
illustrated below.<br />
08:45–08:50 ThAT8.4<br />
Performance Analysis and Odometry<br />
Improvement of an Omnidirectional Mobile<br />
Robot for Outdoor Terrain<br />
Genya Ishigami *1 , Elvine Pineda *2 , Jim Overholt *3 ,<br />
Greg Hudas *3 , and Karl Iagnemma *2<br />
*1 Japan Aerospace Exploration Agency, JAPAN<br />
*2 Massachusetts Institute of Technology, USA<br />
*3 U.S. Army TARDEC, USA<br />
• An omnidirectional mobile robot that<br />
possesses high mobility in rough<br />
terrain is presented.<br />
• The robot has four active split offset<br />
caster (ASOC) modules, enabling the<br />
omnidirectional mobility.<br />
• The agility of the robot is evaluated in<br />
various configurations.<br />
• An odometry method along with<br />
kinematic constraints of the robot<br />
configuration is proposed.<br />
ASOC-driven Omnidirectional<br />
Mobile Robot
Session ThAT8 Continental Parlor 8 Thursday, September 29, <strong>2011</strong>, 08:00–09:30<br />
Autonomous Vehicles<br />
Chair Karl Iagnemma, MIT<br />
Co-Chair William Smart, Washington Univ. in St. Louis<br />
08:50–08:55 ThAT8.5<br />
Inertial Rotation Center Position Estimation for a<br />
Perching Treaded Vehicle<br />
Christopher Schmidt-Wetekam, Nicholas Morozovsky,<br />
and Thomas Bewley<br />
Dept. of Mechanical Engineering, UC San Diego, USA<br />
• A treaded vehicle, “Switchblade”, uses a<br />
combination of tread and body actuation in<br />
order to balance while in a perched<br />
configuration.<br />
• Identify stair edges without tactile or visual<br />
feedback by monitoring the position of the<br />
center of rotation<br />
• Use offset acceleration measurements to<br />
calculate rotation center position (RCP)<br />
• Variance of calculated RCP is inversely<br />
proportional to angular acceleration<br />
• RCP variance is minimized when<br />
accelerometers are equidistant and<br />
widely-spaced about the actual RCP<br />
The treaded rover, “Switchblade”,<br />
is intended to climb stairs using a<br />
perching manuever.<br />
09:00–09:15 ThAT8.7<br />
An Automated Truck Platoon for Energy Saving<br />
Sadayuki Tsugawa<br />
Department of Information Engineering, Meijo University, Japan<br />
Shin Kato<br />
Institute of Intelligent Systems, AIST, Japan<br />
Keiji Aoki<br />
Department of ITS Research, JARI, Japan<br />
• Objectives: energy saving and CO2 emission reduction for road transportation<br />
in addition to safety<br />
• Platoon: 3 heavy (25 ton) trucks<br />
• Functions: fully automated driving of an<br />
autonomous type<br />
• Lateral control: lane marker detection with computer vision<br />
• Longitudinal control: gap measurement with radar and lidar, V2V<br />
communications<br />
• Experiments: driving at 80 km/h with 10 m gap on a test track (oval, 3.2<br />
km) and along an expressway (8 km)<br />
• Fuel consumption improvement: 14 % (measurement)<br />
•CO2 emission reduction: 2.1 % (40 % penetration, simulation)<br />
08:55–09:00 ThAT8.6<br />
Improving near-to-near lateral control of<br />
platoons without communication<br />
Jano Yazbeck, Alexis Scheuer,<br />
Olivier Simonin and François Charpillet<br />
LORIA / INRIA, Nancy, France<br />
• Proposition of a decentralized local<br />
approach for a secure platooning.<br />
• Generic method based on memorization<br />
of the predecessor's path to reduce<br />
drastically lateral deviation.<br />
• Use of a longitudinal controller to ensure<br />
collision avoidance.<br />
• Experimentations on differential wheeled<br />
Khepera III robots.<br />
09:15–09:30 ThAT8.8<br />
Context-Aware Video Compression<br />
for Mobile Robots<br />
Daniel A. Lazewatsky*, Bogumil Giertler**, Martha Witick**, Leah<br />
Perlmutter**, Bruce A. Maxwell**, and William D. Smart*<br />
*Dept of Computer Science & Engineering Washington University in St. Louis, USA<br />
**Dept of Computer Science Colby College, USA<br />
• Remote teleoperation<br />
often relies heavily on<br />
live video which travels<br />
over network(s) with<br />
unknown conditions<br />
• General-purpose video<br />
compression does not<br />
take into account<br />
conditions specific to<br />
robot operation<br />
• Integrating robot<br />
odometry into video<br />
compression pipeline can simultaneously reduce bandwidth and latency<br />
without affecting the robot operator’s task performance<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–184–
Session ThAT9 Continental Parlor 9 Thursday, September 29, <strong>2011</strong>, 08:00–09:30<br />
Towards Anthropomimetic Robots<br />
Chair Michael Beetz, Tech. Univ. München<br />
Co-Chair Frank Kirchner, Univ. of Bremen<br />
08:00–08:15 ThAT9.1<br />
A model of reference trajectory adaptation<br />
for interaction with objects<br />
of arbitrary shape and impedance<br />
Chenguang Yang and Etienne Burdet<br />
Imperial College London, UK<br />
• in the presence of an obstacle, trajectory<br />
drifts trial after trial<br />
• this tends to limit the interaction force<br />
• we have derived the dynamics of this<br />
human adaptation strategy and simulated it<br />
• this yields a stable trajectory adaptation<br />
behavior for robots<br />
reference<br />
trajectory<br />
1<br />
1<br />
5<br />
actual<br />
trajectory<br />
5<br />
9 9<br />
stiff object<br />
08:30–08:45 ThAT9.3<br />
Predictive Compliance for Interaction Control of<br />
Robot Manipulators<br />
José de Gea Fernández 1 and Frank Kirchner 1,2<br />
1 German Research Center for Artificial Intelligence (DFKI)<br />
Robotics Innovation Center<br />
2 Robotics Group, University of Bremen<br />
Bremen, Germany<br />
• Predictive Context-Based Adaptive<br />
Compliance (PCAC) architecture is<br />
presented<br />
• Context-based predictions for the<br />
selection and on-line modification of the<br />
compliance of a robot manipulator<br />
• A Bayesian predictor in the form of a<br />
Relevance Vector Machine combines<br />
the use of prior knowledge and<br />
expected sensory feedback to correct<br />
for an erroneous compliance in the<br />
case of a falsely-predicted context<br />
Robot performing the lifting<br />
experiment which makes use of<br />
context-based predictions to<br />
adjust the arm compliance<br />
08:15–08:30 ThAT9.2<br />
Generic Dynamic Motion Generation<br />
with Multiple Unilateral Constraints<br />
L. Saab O. Ramos N. Mansard and P. Souères<br />
LAAS, Université de Toulouse (UPS), France<br />
J.Y. Fourquet<br />
LGP, ENIT, France<br />
• Dynamic whole-body motion generation<br />
• Generic formulation of complex<br />
movements as stack of tasks including<br />
rigid contacts and unilateral constraints<br />
• Global resolution method involving a<br />
cascade of quadratic programming<br />
• Simulation based on the complete<br />
dynamic model of the humanoid robot<br />
HRP-2<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–185–<br />
A sitting task for HRP-2 involving<br />
several non-coplanar contacts<br />
08:45–08:50 ThAT9.4<br />
Parameterizing Actions to have the<br />
Appropriate Effects<br />
Lorenz Mösenlechner and Michael Beetz<br />
Intelligent Autonomous Systems Group<br />
Technische Universität München, Germany<br />
• Performing complex tasks in human<br />
environments is hard and requires<br />
reasoning (qualitative and quantitative) ative)<br />
• Plans use symbolic descriptions of f<br />
locations, objects, … which need to o be<br />
resolved at run-time<br />
• We present a fast and light-weight<br />
reasoning system that integrates<br />
Prolog, a physics engine and OpenGL nGL<br />
to infer locations under the constraints aints<br />
of the current task
Session ThAT9 Continental Parlor 9 Thursday, September 29, <strong>2011</strong>, 08:00–09:30<br />
Towards Anthropomimetic Robots<br />
Chair Michael Beetz, Tech. Univ. München<br />
Co-Chair Frank Kirchner, Univ. of Bremen<br />
08:50–08:55 ThAT9.5<br />
Physics-based Modeling of an<br />
Anthropomimetic Robot<br />
Steffen Wittmeier, Michael Jäntsch,<br />
Konstantinos Dalamagkidis and Alois Knoll<br />
Faculty of Informatics, TU München, Germany<br />
• Control of highly-redundant, tendon-driven<br />
robots remains a very challenging task.<br />
• Here, the reverse-engineered derivation of f<br />
a physics-based model of a hand-crafted,<br />
anthropomimetic robot arm is presented.<br />
• The skeleton model was derived from a<br />
total of 25 laser-scans and actuator as<br />
well as sensor models were implemented.<br />
• Moreover, a muscle control algorithm was<br />
implemented to demonstrate and validate<br />
the dynamics of the simulation model.<br />
• The developed model will subsequently be e<br />
employed as an internal dynamics model<br />
for robot control.<br />
Prototype P of fh the anthropomimetic<br />
h i i<br />
robot ECCE-I.<br />
09:00–09:15 ThAT9.7<br />
A comparison between joint level torque<br />
sensing and proximal F/T sensor torque<br />
estimation: implementation on the iCub<br />
M. Randazzo, M. Fumagalli, F. Nori, L. Natale,<br />
G. Metta, G.Sandini<br />
Robotics Brain and Cognitive Science Department,<br />
Instituto Italiano di Tecnología, Italy<br />
• Two different approaches of<br />
achieving compliant control on a<br />
humanoid robotic arm are compared.<br />
• A model-based approach is used to<br />
estimate both the internal and the<br />
external wrenches using a single<br />
proximal six-axis force-torque sensor.<br />
• On the other hand, integrated torque<br />
sensors have been developed to<br />
directly measure the joint torques.<br />
• The obtained estimations and<br />
torques measurements are<br />
experimentally compared and the<br />
benefits / disadvantages of the two<br />
approaches are discussed.<br />
08:55–09:00 ThAT9.6<br />
Reexamining Lucas-Kanade Method for Real-Time<br />
Independent Motion Detection: An Application to<br />
the iCub Humanoid Robot<br />
C. Ciliberto, U. Pattacini, L. Natale, F. Nori and G. Metta<br />
RBCS, Istituto Italiano di Tecnologia, Italy<br />
• The motionCUT algorithm is presented to<br />
perform real-time independent motion<br />
detection for applications in robotics.<br />
• The canonical Lucas-Kanade method for<br />
optical flow is analyzed to derive a criterion<br />
that is apt to discriminate between the<br />
robot egomotion and independent motion<br />
in camera-acquired image streams.<br />
• Extensive experimental validation is<br />
carried out on the iCub humanoid robot,<br />
demonstrating that the proposed system<br />
responds smoothly to changes in the<br />
environmental conditions.<br />
• A task involving the stereo gaze tracking of<br />
a moving target is examined as a practical<br />
example of the motionCUT capabilities<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–186–<br />
Stereo tracking of a person walking in<br />
a cluttered environment<br />
09:15–09:30 ThAT9.8<br />
Computation with mechanically coupled springs<br />
for compliant robots<br />
Hidenobu Sumioka, Helmut Hauser,<br />
and Rolf Pfeifer<br />
the Department of Informatics<br />
University of Zurich, Switzerland<br />
• We identify the computation that a<br />
compliant physical body can achieve.<br />
• Actuation of the springs around a joint is<br />
used as a computational device.<br />
• Only a linear and static readout is adapted<br />
to compute the output.<br />
• Computer simulation shows the spring<br />
network can emulate nonlinear<br />
combinations which need temporal<br />
integration.<br />
An overview of information<br />
processing in the system and the<br />
emulated trajectories .The system<br />
can emulate 2 nd order systems and<br />
10 th ones accurately.
Session ThBT1 Continental Parlor 1 Thursday, September 29, <strong>2011</strong>, 10:00–11:30<br />
Control for Manipulation & Grasping<br />
Chair Jing Xiao, UNC-Charlotte<br />
Co-Chair Luigi Villani, Univ. di Napoli Federico II<br />
10:00–10:15 ThBT1.1<br />
Adaptive Sliding Mode Control of Grasped<br />
Object Slip for Prosthetic Hands<br />
Erik D. Engeberg<br />
Mechanical Engineering Department, The University of Akron, USA<br />
Sanford Meek<br />
Mechanical Engineering Department, The University of Utah, USA<br />
• The first robustly stable grasped object<br />
slip prevention algorithm for a prosthetic<br />
hand.<br />
• The adaptive integral sliding mode slip<br />
prevention controller also minimizes<br />
inadvertent object crushing when slip<br />
occurs<br />
• Slip is detected by band pass filtering the<br />
shear force derivative signal to amplify<br />
vibrations that occur<br />
• Five different values of object stiffness are<br />
tested with three different control<br />
algorithms<br />
10:30–10:45 ThBT1.3<br />
Self-tuning Cooperative Control of Manipulators<br />
with Position/Orientation Uncertainties in The<br />
Closed-Kinematic Loop<br />
Farhad Aghili<br />
Canadian Space Agency (CSA), Canada<br />
• Adaptive control of cooperative<br />
manipulators in the presence of<br />
uncertainties in the closed-loop<br />
kinematic loop<br />
• The kinematic parameters are<br />
estimated in real-time using two<br />
cascaded estimators<br />
• No force measurement is required to<br />
deal with kinematic uncertainties of<br />
interconnected robotic system.<br />
• Convergence and stability of the<br />
adaptive control process can be<br />
ensured if the direction of angular<br />
velocity of the object does not remain<br />
constant over time.<br />
• Experimental results are presented<br />
The cooperative dual-arm<br />
manipulator system<br />
10:15–10:30 ThBT1.2<br />
Cartesian Impedance Control For A Variable<br />
Stiffness Robot Arm<br />
Florian Petit and Alin Albu-Schäffer<br />
Institute of Robotics and Mechatronics, German Aerospace Center (DLR),<br />
Germany<br />
• Combination of an active Cartesian<br />
impedance controller and the variable<br />
stiffness robot.<br />
• An algorithm to optimize the passive and<br />
active Cartesian stiffness for increased<br />
tracking performance is presented.<br />
• The extended stiffness range provided by<br />
the algorithm is shown.<br />
• Implementation and experimental results<br />
on the DLR Hand Arm System are given.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–187–<br />
The DLR Hand Arm System<br />
10:45–10:50 ThBT1.4<br />
Kinematic Control with Force Feedback for a<br />
Redundant Bimanual Manipulation System<br />
F. Caccavale*, V. Lippiello + , G. Muscio*, F. Pierri*, F. Ruggiero + , L. Villani +<br />
*Dipartimento di Ingegneria e Fisica dell’Ambiente, Università della Basilicata, Italy<br />
+ Dipartimento di Informatica e Sistemistica, Università di Napoli Federico II, Italy<br />
• Kinematic model for a multi-fingered<br />
redundant bimanual manipulation system<br />
with elastic contacts, while grasping an<br />
object<br />
• Two-stages control scheme<br />
• First stage: inverse kinematics with<br />
redundancy resolution to compute joint<br />
references<br />
• Second stage: parallel force/position<br />
control to impose desired object<br />
motion and normal contact forces<br />
• Secondary tasks accomplished through a<br />
prioritized task-sequencing strategy
Session ThBT1 Continental Parlor 1 Thursday, September 29, <strong>2011</strong>, 10:00–11:30<br />
Control for Manipulation & Grasping<br />
Chair Jing Xiao, UNC-Charlotte<br />
Co-Chair Luigi Villani, Univ. di Napoli Federico II<br />
10:50–10:55 ThBT1.5<br />
Robust Manipulation<br />
for Temporary Lack of Sensory Information<br />
by a Multi-Fingered Hand-Arm System<br />
Akihiro Kawamura, Kenji Tahara, Ryo Kurazume<br />
and Tsutomu Hasegawa<br />
Kyushu University, Japan<br />
• A new manipulation scheme against the<br />
temporary lack of sensory information is<br />
proposed.<br />
• According to the availability of sensory<br />
information, the controlled target frame of<br />
the object is switched from the real one to<br />
the virtual one, and vice versa.<br />
• A new virtual object frame based on an<br />
attitude of each fingertip is proposed.<br />
• A numerical simulation result shows that<br />
the proposed method realizes stable<br />
object manipulation even if sensory<br />
information is unavailable.<br />
Multi-fingered hand-arm system<br />
11:00–11:15 ThBT1.7<br />
Impedance control of a non-linearly coupled<br />
tendon driven thumb<br />
Maxime Chalon, Werner Friedl, Jens Reinecke, Thomas<br />
Wimboeck and Alin Albu-Schaeffer<br />
Institute of Robotics and Mechatronics, German Aerospace Center<br />
• The thumb nonlinear actuation structure<br />
provides improved torque and range of<br />
motion but creates new control challenges<br />
• A singular perturbation based controller is<br />
designed and implemented on the real<br />
system<br />
• Experiments and simulation are performed<br />
to verify the performances<br />
• Due to the absence of link side sensor, a<br />
projected gradient method is implemented<br />
in real-time to estimate the joint position<br />
10:55–11:00 ThBT1.6<br />
Determining “Grasping” Configurations for a<br />
Spatial Continuum Manipulator<br />
Jinglin Li and Jing Xiao<br />
IMI Lab, Department of Computer Science<br />
University of North Carolina - Charlotte, USA<br />
• A continuum manipulator, such as a multisection<br />
trunk/tentacle robot, is promising<br />
for deft manipulation of a wide range of<br />
objects of different shapes and sizes.<br />
• This paper presents a general and<br />
complete analysis to determine grasping<br />
configurations for any given 3D object by<br />
a spatial continuum manipulator with three<br />
constant-curvature sections; the<br />
curvature, length, and orientation of each<br />
section are continuous variables.<br />
• Our method finds all types of grasping<br />
configurations through solving intersection<br />
constraint equations. The<br />
implementation results show its efficiency<br />
for on-line operation.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–188–<br />
One grasping configuration,<br />
where sections 3 and 2 of the<br />
continuum manipulator wrap<br />
around the object.<br />
11:15–11:30 ThBT1.8<br />
Energy Shaping Control of Robot Manipulators<br />
in Explicit Force Regulation Tasks with Elastic<br />
Environments<br />
David Navarro-Alarcon, Peng Li, and Hiu Man Yip<br />
Departament Mechanical and Automation Engineering,<br />
The Chinese University of Hong Kong, Hong Kong S.A.R.<br />
• Physical interaction of a robot manipulator<br />
with a purely elastic environment<br />
• The closed-loop elastic energy is shaped<br />
based on the available force sensory<br />
feedback<br />
• Exact force regulation is achieved by<br />
means of a force-modulated “energy<br />
injection” to the system<br />
• Simple experimental results are presented<br />
to validate the approach<br />
Conceptual representation
Session ThBT2 Continental Parlor 2 Thursday, September 29, <strong>2011</strong>, 10:00–11:30<br />
Mapping<br />
Chair Cyrill Stachniss, Univ. of Freiburg<br />
Co-Chair Mitch Bryson, Univ. of Sydney<br />
10:00–10:15 ThBT2.1<br />
Visual Mapping with Uncertainty<br />
for Correspondence-free Localization<br />
using Gaussian Process Regression<br />
T. Schairer1 , B. Huhle1 , P. Vorst2 , A. Schilling1 and W. Straßer1 1Dept. of Graphical Interactive Systems, University of Tübingen, Germany<br />
2Dept. of Cognitive Systems, University of Tübingen, Germany<br />
• Setting:<br />
• Appearance-based localization using<br />
low-resolution omnidirectional images<br />
• Gaussian Process regression for<br />
modeling image variation of the scene<br />
• Problem:<br />
• Time-consuming acquisition of reference<br />
images with exact position information<br />
• Our solution:<br />
• Use available odometry data<br />
• Learn a probabilistic map by fusing<br />
uncertain training data<br />
• Quality increases with every new run<br />
Reference images (dots), odometry<br />
(dotted), ground-truth (solid) and<br />
our approach (dashed).<br />
10:30–10:45 ThBT2.3<br />
Dense visual mapping of large scale<br />
environments for real-time localisation<br />
Maxime Meilland and Patrick Rives<br />
INRIA Sophia Antipolis Méditerrannée, France<br />
Andrew Ian Comport<br />
CNRS, I3S Laboratory, Université Nice Sophia Antipolis, France<br />
• Custom sensor for augmented visual<br />
sphere construction with a unique<br />
center of projection.<br />
• Calibration of a ring of divergent stereo<br />
cameras.<br />
• Automatic environment mapping using<br />
an ego-centered representation.<br />
• Accurate real-time robot localisation<br />
navigating locally within the learnt<br />
model.<br />
10:15–10:30 ThBT2.2<br />
Distributed Large Scale Terrain Mapping for Mining<br />
and Autonomous Systems<br />
Paul Thompson, Eric Nettleton and Hugh Durrant-Whyte<br />
The Australian Centre for Field Robotics (ACFR)<br />
The University of Sydney, Australia<br />
• A method for efficient fusion, estimation and distribution of large<br />
scale terrain<br />
• Based on sparse information smoothing, related to finite-elements<br />
(FEM) and Gaussian processes (GPs)<br />
• Results for efficient fusion and distribution<br />
• Applied to multiple terrain scanning lasers in mining<br />
10:45–10:50 ThBT2.4<br />
Hierarchies of Octrees for Efficient 3D Mapping<br />
K.M. Wurm1 D. Hennes2 D. Holz3 R.B. Rusu4 C. Stachniss1 K. Konolige4 W. Burgard1 1 Dep. of Computer Science, University of Freiburg, Germany<br />
2 Dep. of Knowledge Engineering, Maastricht University, The Netherlands<br />
3 Autonomous Intelligent Systems Group, University of Bonn, Germany<br />
4 Willow Garage Inc., CA, USA<br />
• Multi-resolution 3D representation<br />
organized as a tree of submaps<br />
• Each submap is updated and<br />
transformed individually<br />
• Models occupied and free space<br />
efficiently<br />
• Application to table-top<br />
manipulation and autonomous<br />
exploration<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–189–<br />
Top: model of a table top scene.<br />
Bottom: illustration of the map hierarchy
Session ThBT2 Continental Parlor 2 Thursday, September 29, <strong>2011</strong>, 10:00–11:30<br />
Mapping<br />
Chair Cyrill Stachniss, Univ. of Freiburg<br />
Co-Chair Mitch Bryson, Univ. of Sydney<br />
10:50–10:55 ThBT2.5<br />
A Comparison of Feature and Pose-Based<br />
Mapping using Vision, Inertial and GPS on a UAV<br />
Mitch Bryson and Salah Sukkarieh<br />
Australian Centre for Field Robotics, University of Sydney, Australia<br />
• Presents and compares two different<br />
approaches to integrating IMU, GPS<br />
and monocular vision camera data<br />
from an Unmanned Aerial Vehicle for<br />
building large-scale 3D terrain<br />
reconstructions.<br />
• Novel pose-only non-linear leastsquares<br />
formulation that optimises<br />
relative camera poses based on 1D<br />
epipolar constraints imposed by<br />
vision matches.<br />
• Results presented within an<br />
environmental mapping project over<br />
outback Australia<br />
11:00–11:15 ThBT2.7<br />
Occupancy Grid Rasterization in Large<br />
Environments for Teams of Robots<br />
Johannes Strom Edwin Olson<br />
Compute Science and Engineering , University of Michigan, USA<br />
• We present an efficient algorithm for<br />
dynamic occupancy grid computation in<br />
large environments (220m x 170m and up)<br />
• Map update cost is proportion to impact of<br />
any loop closures<br />
• Rasterization can be sped up by<br />
identifying redundant sensor data<br />
• Applicable in dynamic environments by<br />
not rasterizing transient objects<br />
Rasterized map using proposed<br />
method (100m by 160m)<br />
10:55–11:00 ThBT2.6<br />
Autonomous Semantic Mapping for Robots<br />
Performing Everyday Manipulation Tasks<br />
in Kitchen Environments<br />
Nico Blodow, Lucian Cosmin Goron, Zoltan-Csaba Marton, Dejan<br />
Pangercic, Thomas Ruehr, Moritz Tenorth, and Michael Beetz<br />
Intelligent Autonomous Systems, Technische Universitaet Muenchen, Germany<br />
� Autonomous exploration including the selection of the next best view<br />
pose in order to actively explore unknown space based using a visibility<br />
kernel and associated costmaps<br />
� Registration, segmentation, and interpretation of color point cloud data<br />
from low-cost devices<br />
• Segmentation of differences of point clouds through interaction using the<br />
robot manipulator<br />
11:15–11:30 ThBT2.8<br />
Autonomous mapping for inspection<br />
of 3D structures<br />
Mahmoud Tavakoli, Ricardo Faria,<br />
Lino Maques, Anibal T. de Almeida<br />
Institute for Systems and Robotics, University of Coimbra, Portugal<br />
• Multiple terrain robots map a<br />
structure which should be explored<br />
by a pole climbing robot<br />
• Roombas are equipped with a<br />
wide angle camera<br />
• Patterns are fixed on the climbing<br />
robot<br />
• ARToolkit library is used for pose<br />
estimation between each camera<br />
and each pattern<br />
• A leap-frog methodology is applied<br />
for the cooperative localization of<br />
the robots<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–190–
Session ThBT3 Continental Parlor 3 Thursday, September 29, <strong>2011</strong>, 10:00–11:30<br />
Randomized Planning & Kinematic Control<br />
Chair Nancy Amato, Texas A&M Univ.<br />
Co-Chair Elie Shammas, American Univ. of Beirut<br />
10:00–10:15 ThBT3.1<br />
FIRM: Feedback Controller-Based<br />
Information-state RoadMap<br />
A framework for planning under uncertainty<br />
Ali-akbar Agha-mohammadi, Suman Chakravorty, and Nancy Amato<br />
Computer Science and Engineering Dept., Texas A&M University, USA<br />
Aerospace Engineering Dept., Texas A&M University, USA<br />
• Generalizing PRM to belief space<br />
preserving the edge independence<br />
and thus breaking the curse of history<br />
in POMDPs<br />
• Inducing reachable belief nodes<br />
• Incorporating obstacles in planning<br />
• Online planning and replanning<br />
capabilities<br />
• Providing a feedback law over belief<br />
space<br />
10:30–10:45 ThBT3.3<br />
Optimal Probabilistic Robot Path Planning with<br />
Missing Information<br />
Mohamad Ali Movafaghpour and Ellips Masehian<br />
Faculty of Engineering, Tarbiat Modares University, Iran<br />
• The missing information about the<br />
outcomes of a robot’s actions can be<br />
modeled with a Stochastic Markov<br />
Decision Process with Hidden Variables,<br />
the exact values of which are not known at<br />
the beginning of path planning.<br />
• A Stochastic Dynamic Programming<br />
solution approach for planning with<br />
missing information is formulated as a<br />
Linear Programming (LP) model.<br />
• A new algorithm called Incremental<br />
Assumptive Probabilistic Planning (IAPP)<br />
is proposed for incrementally incorporating<br />
active Hidden Variables into the beliefstate<br />
space and solving an LP.<br />
• An optimality proof for the path generated<br />
by the IAPP and a method for treating<br />
large-scale LP models are also presented.<br />
A4 B4<br />
A6<br />
B5<br />
B6<br />
C1<br />
C2<br />
C3<br />
C4 D4<br />
D1 E1<br />
F1<br />
F2<br />
F3<br />
E4 F4<br />
F5<br />
C6 D6 E6 F6<br />
The robot’s start and goal points<br />
are on A4 and F4, and the<br />
open/closed states of the cells B5<br />
and E4 are unknown at the<br />
beginning. These cells represent<br />
the Hidden Variables whose<br />
states affect the outcomes and<br />
costs of certain actions.<br />
10:15–10:30 ThBT3.2<br />
Computing Spanners of<br />
Asymptotically Optimal Probabilistic Roadmaps<br />
James D. Marble and Kostas E. Bekris<br />
University of Nevada, Reno, USA<br />
• Asymptotically optimal motion planning<br />
algorithms produce very dense<br />
roadmaps<br />
• Spanner graphs are sub-graphs with<br />
fewer edges and guarantees on<br />
resulting path length<br />
• This work combines these two<br />
concepts to produce an asymptotically<br />
near-optimal planner<br />
−Path degradation is bounded<br />
• Small sacrifice in path quality results in:<br />
• Large savings in graph density<br />
• Faster online query resolution<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–191–<br />
Given a dense roadmap (above) the<br />
resulting sparse spanner (below)<br />
preserves path quality.<br />
10:45–10:50 ThBT3.4<br />
Asymptotically-optimal Path Planning for<br />
Manipulation using Incremental Sampling-based<br />
Algorithms<br />
Alejandro Perez, Alexander Shkolnik, Seth Teller, Matthew R. Walter<br />
Computer Science and Artificial Intelligence Laboratory, MIT<br />
Sertac Karaman, Emilio Frazzoli<br />
Laboratory for Information and Decision Systems, MIT<br />
• Optimal motion planning for manipulation<br />
based on the RRT*<br />
• Efficient sampling using Ball Trees<br />
• Rapid generation of low-cost solutions<br />
• Dual-arm planning on Willow Garage’s<br />
PR2 robot<br />
RRT<br />
BT+RRT*<br />
Time-lapse images of RRT and<br />
BT+RRT* trajectories after 2,000<br />
iterations (12DOF)
Session ThBT3 Continental Parlor 3 Thursday, September 29, <strong>2011</strong>, 10:00–11:30<br />
Randomized Planning & Kinematic Control<br />
Chair Nancy Amato, Texas A&M Univ.<br />
Co-Chair Elie Shammas, American Univ. of Beirut<br />
10:50–10:55 ThBT3.5<br />
Quasi-Static Motion Planning on Uneven Terrain<br />
for a Wheeled Mobile Robot<br />
Vijay Eathakota , Aditya Gattupalli and K Madhava Krishna<br />
Robotics Research Center, IIIT Hyderabad, India<br />
• We develop a motion planning algorithm<br />
based on the RRT, connecting the starting<br />
and the end goal positions of the Wheeled<br />
Mobile Robot on Uneven Terrain while<br />
minimizing wheel slip<br />
• The curves connected the adjacent nodes of<br />
the RRT are developed using the forward<br />
motion problem based on Peshkin’s<br />
minimum energy principle<br />
• The result of this planner is a set of ordinary<br />
differential equations (ODEs) along with a set<br />
of Differential Algebraic Equations (DAEs)<br />
• Simulation Results prove the efficacy of the<br />
planner<br />
Wheeled Mobile Robot on<br />
Uneven Terrain<br />
11:00–11:15 ThBT3.7<br />
Exact Motion Planning Solution for Principally<br />
Kinematic Systems<br />
Elie Shammas<br />
Mechanical Engineering, American University of Beirut, Lebanon<br />
Mauricio de Oliveira<br />
Mechanical and Aerospace Eng’g, University of California San Diego, USA<br />
• Motion planning problem for principally<br />
kinematic systems is solve analytically.<br />
• The input of the motion planning problem<br />
is a planar curve and the output is the time<br />
evolution of the input shape variables.<br />
• The base or shape variables are solved<br />
for analytically so that the kinematic snake<br />
traverses exactly a planar trajectory.<br />
• The motion planning techniques are<br />
tested with Serpenpid, sinusoidal, and<br />
cubic planar curves.<br />
Kinematic snake traversing a<br />
cubic spline curve.<br />
10:55–11:00 ThBT3.6<br />
Minimum-time trajectories for kinematic mobile<br />
robots and other planar rigid bodies<br />
Andrei Furtuna, Wenyu Lu,<br />
Weifu Wang, Devin Balkcom<br />
Computer Science, Dartmouth College, USA<br />
• A generalized model of mobile robots includes Dubins, Reeds-<br />
Shepp and other vehicles as special cases.<br />
• Pontryagin’s principle gives strong necessary conditions on the<br />
minimum-time trajectories.<br />
• We present an algorithm that efficiently searches for minimum-time<br />
trajectories.<br />
A minimum-time trajectory for an<br />
omnidirectional vechicle<br />
11:15–11:30 ThBT3.8<br />
Continuous-Curvature Kinematic Control for<br />
Path Following Problems<br />
Vicent Girbés and Leopoldo Armesto and<br />
Josep Tornero and J. Ernesto Solanes<br />
Institute of Design and Manufacturing, Technical University of Valencia, Spain<br />
• Smooth kinematic controller is<br />
introduced, mathematically and<br />
algorithmically.<br />
• Kinematic and Dynamic constraints<br />
are taken into account.<br />
• Capability of combine the kinematic<br />
controller with global path planners<br />
or vision systems as a waypoints<br />
generators.<br />
• Vision-based industrial forklift is<br />
used as an AGV for testing the<br />
proposed method.<br />
• Benchmark evaluation is done to<br />
show better performance than<br />
Pure-Pursuit methods.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–192–<br />
AGV with a vision-based smooth<br />
kinematic controller
Session ThBT4 Continental Ballroom 4 Thursday, September 29, <strong>2011</strong>, 10:00–11:30<br />
Symposium: Stochasticity in Robotics and Biological Systems II<br />
Chair M. Ani Hsieh, Drexel Univ.<br />
Co-Chair Dejan Milutinovic, Baskin School of Engineering, UC Santa Cruz<br />
10:00–10:15 ThBT4.1<br />
A Trajectory-based Calibration Method for<br />
Stochastic Motion Models<br />
Ezequiel Di Mario, Gregory Mermoud, Massimo<br />
Mastrangeli and Alcherio Martinoli<br />
Distributed Intelligent Systems and Algorithms Laboratory,<br />
EPFL, Switzerland<br />
• Quantitative method based on the<br />
Correlated Random Walk (CRW) model<br />
• Minimize Kolmogorov-Smirnov distance<br />
between step length and step angle<br />
distributions<br />
• Validation on real trajectories of<br />
centimeter-sized water-floating robots<br />
• Sensitivity analysis on a multi-robot<br />
simulation: effect of parameter<br />
perturbation on the predicted<br />
distributions of self-assembled<br />
structures<br />
10:30–10:45 ThBT4.3<br />
Stochastic optimal control with variable<br />
impedance manipulators in presence of<br />
uncertainties and delayed feedback<br />
Bastien Berret, Serena Ivaldi, Francesco Nori and Giulio Sandini<br />
Robotics, Brain and Cognitive Sciences, Italian Institute of Technology, Italy<br />
• Recent findings suggest that cocontraction,<br />
or the human ability to change<br />
the intrinsic musculo-skeletal compliance,<br />
plays a crucial role when dealing with<br />
uncertainties and unpredictability<br />
• We use numerical methods for stochastic<br />
optimal control of movements with<br />
variable impedance manipulators in<br />
presence of uncertainties<br />
• We show that actively varying the system<br />
compliance can prevent the<br />
disadvantages of delayed feedback if<br />
coupled with a global and centralized<br />
feedforward motor plan which exploits<br />
muscle co-contraction to achieve<br />
(feedback free) disturbance rejection<br />
Example of two-joint arm moving<br />
in a divergent force field, using<br />
variable impedance control<br />
10:15–10:30 ThBT4.2<br />
Cooperative Multi-Agent Inference over<br />
Grid Structured Markov Random Fields<br />
Ryan Williams and Gaurav Sukhatme<br />
Departments of Electrical Engineering and Computer Science, University of<br />
Southern California, USA<br />
• Cooperative inference with distributed<br />
observations, uncertainty modeled by grid<br />
structured pairwise Markov Random Field<br />
(MRF)<br />
• Proposed Multi-Agent MRF (MAMRF)<br />
decomposes global inference into local<br />
belief exchanges and per-agent inference<br />
problems<br />
• Loopy belief propagation for approximate<br />
inference with synchronous and proposed<br />
region-of-influence message passing<br />
• Spatial mapping simulations over realworld<br />
Moderate Resolution Imaging<br />
Spectroradiometer (MODIS) dataset<br />
illustrates promising performance<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–193–<br />
Cooperative spatial mapping<br />
simulation over MODIS sampling<br />
10:45–10:50 ThBT4.4<br />
Stochastic optimization of a chain sliding mode<br />
controller for the mobile robot maneuvering<br />
A.V. Terekhov, J.-B. Mouret, C. Grand<br />
Institut des Systèmes Intelligents et de Robotique, UPMC-CNRS, France<br />
• Study of aggressive steering<br />
maneuvers<br />
• A chain sliding mode controller is<br />
proposed<br />
• Uses stochastic optimization for<br />
controller parameters tuning<br />
• Validation performed on the real robot
Session ThBT4 Continental Ballroom 4 Thursday, September 29, <strong>2011</strong>, 10:00–11:30<br />
Symposium: Stochasticity in Robotics and Biological Systems II<br />
Chair M. Ani Hsieh, Drexel Univ.<br />
Co-Chair Dejan Milutinovic, Baskin School of Engineering, UC Santa Cruz<br />
10:50–10:55 ThBT4.5<br />
Programmable 3D Stochastic Fluidic Assembly<br />
of cm-scale Modules<br />
Michael T. Tolley<br />
School of Engineering and Applied Sciences, Harvard University, USA<br />
Hod Lipson<br />
School of Mechanical and Aerospace Engineering, Cornell University, USA<br />
• Stochastic Fluidic Assembly (SFA)<br />
offers a potential approach to<br />
achieving programmable matter<br />
• We present a minimalistic<br />
component approach that has led to<br />
three experimental advances in cmscale<br />
SFA:<br />
• Robust assembly<br />
• Parallel hierarchical assembly<br />
• Fully automated assembly using<br />
pressure sensor feedback<br />
• This biologically inspired approach<br />
could lead to batch manufacturing of<br />
small scale target structures<br />
Parallel Stochastic Fluidic Assembly<br />
of Non-Planar 3D Structures<br />
11:00–11:15 ThBT4.7<br />
Visual Localization in Fused Image and Laser<br />
Range Data<br />
Nicholas Carlevaris-Bianco and Anush Mohan<br />
Dept. Electrical Eng. & Computer Science, University of Michigan, U.S.A.<br />
James R. McBride<br />
Research and Innovation Center, Ford Motor Company, U.S.A.<br />
Ryan M. Eustice<br />
Dept. Naval Architecture & Marine Eng., University of Michigan, U.S.A.<br />
• Using a richly instrumented<br />
robotic platform the algorithm<br />
builds a city scale map of<br />
sparse visual features from<br />
co-registered LIDAR and<br />
image data.<br />
• Localization is performed<br />
using the map and a low cost<br />
instrumented camera<br />
system.<br />
• Results presented on data<br />
collected over multiple years,<br />
showing strong robustness<br />
w.r.t. dynamic changes in the<br />
environment.<br />
10:55–11:00 ThBT4.6<br />
Hierarchical Congestion Control<br />
for Robotic Swarms<br />
Vinicius Graciano Santos and Luiz Chaimowicz<br />
VeRLab, UFMG, Brazil<br />
• One of the main challenges in swarm<br />
navigation is to avoid congestions<br />
• We propose the use of hierarchical<br />
abstractions in conjunction with traffic<br />
controls rules to solve this problem<br />
• Simulated and real experiments are<br />
presented to validate the approach.<br />
• Results show that our method allows the<br />
swarm to navigate without congestions in<br />
a smooth and coherent fashion<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–194–<br />
Avoiding congestions between<br />
groups of robots<br />
11:15–11:30 ThBT4.8<br />
Mathematical Analysis of the Minimum Variance<br />
Model of Human-Like Reaching Movements<br />
Mikhail Svinin and Motoji Yamamoto<br />
Mechanical Engineering Department<br />
Faculty of Engineering<br />
Kyushu University, Japan<br />
• Open-loop optimal trajectory planning in<br />
stochastic formulation (control signaldependent<br />
noise).<br />
• Performance index: cumulative average<br />
positional variance over the interval (T,T+R).<br />
• Limiting case of infinite R is considered for<br />
several classes of models (stable plant, n-th<br />
order integrator, diagonizable systems).<br />
• For n-th order integrator and diagonazible<br />
systems with zero eigenvalues, the limiting<br />
model is equivalent to the conventional<br />
minimum effort model<br />
• The conventional minimum jerk model is a<br />
partial case of the minimum variance model.<br />
Post-movement covariance
Session ThBT5 Continental Ballroom 5 Thursday, September 29, <strong>2011</strong>, 10:00–11:30<br />
Symposium: Humanoid Technologies<br />
Chair Tamim Asfour, Karlsruhe Inst. of Tech. (KIT)<br />
Co-Chair Kazuhito Yokoi, National Inst. of AIST<br />
10:00–10:15 ThBT5.1*<br />
Semi-Plenary Invited Talk: Optimal control of motions<br />
of humanoid robots<br />
Katja Mombaur, University of Heidelberg<br />
10:30–10:45 ThBT5.3<br />
Hardware Improvement of Cybernetic Human<br />
HRP-4C<br />
for Entertainment Use<br />
Kenji Kaneko, Fumio Kanehiro, Mitsuharu Morisawa,<br />
Tokuo Tsuji, Kanako Miura, Shin’ichiro Nakaoka,<br />
Shuuji Kajita and Kazuhito Yokoi<br />
AIST, Japan<br />
• Hardware improvement of cybernetic<br />
human HRP-4C for entertainment<br />
• New hand redesigned to realize a humansize<br />
hand with realistic skin and the<br />
average figure of a young Japanese<br />
female<br />
• New foot with active toe joint for realizing<br />
human-like walking motion<br />
• New eye with camera towards both<br />
providing visual information for operators<br />
and realization of motion in response to<br />
spectators<br />
Cybernetic human HRP-4C<br />
improved with,<br />
new hands, an active toe joint,<br />
and a new eye with camera<br />
10:15–10:30 ThBT5.2<br />
Semi-Plenary Invited Talk: Optimal control of motions<br />
of humanoid robots<br />
Katja Mombaur, University of Heidelberg<br />
10:45–10:50 ThBT5.4<br />
Human Robot HRP-4<br />
Humanoid Robotics Platform with Lightweight and Slim Body<br />
Kenji Kaneko, Fumio Kanehiro, Mitsuharu Morisawa<br />
AIST, Japan<br />
Kazuhiko Akachi, Go Miyamori,<br />
Atsushi Hayashi, Noriyuki Kanehira<br />
Kawada Industries, Inc., Japan<br />
• A new lightweight and slim body with a<br />
height of 151 [cm] and weight 39 [kg]<br />
following the legacy of the HRP series<br />
• Total of 34 degrees of freedom, including<br />
7 degrees of freedom for each arm to<br />
facilitate object handling<br />
• Cost reduction by adoption of modularized<br />
units and common parts, reviewing the<br />
machining process, streamlining the basic<br />
specification, and so on<br />
• Improved research efficiency due to the<br />
adoption of OpenRTM-aist, which enables<br />
the use of domestic and international<br />
software assets<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–195–<br />
HRP-4<br />
( Humanoid robotics platform – 4 )
Session ThBT5 Continental Ballroom 5 Thursday, September 29, <strong>2011</strong>, 10:00–11:30<br />
Symposium: Humanoid Technologies<br />
Chair Tamim Asfour, Karlsruhe Inst. of Tech. (KIT)<br />
Co-Chair Kazuhito Yokoi, National Inst. of AIST<br />
10:50–10:55 ThBT5.5<br />
Weakly collision-free paths for continuous<br />
humanoid footstep planning<br />
Nicolas Perrin (1) and Olivier Stasse (2)<br />
Florent Lamiraux (1) and Eiichi Yoshida (2)<br />
(1) CNRS/LAAS, Université de Toulouse UPS, INSA, INP, ISAE, France<br />
(2) CNRS-AIST JRL, UMI3218/CRT, Tsukuba, Japan<br />
• New notion of collisionfreeness<br />
• Equivalence between an<br />
example of discrete<br />
footstep planning and<br />
classical 2D continuous<br />
motion planning<br />
• Possibility to use PRM or<br />
RRT for footstep planning in<br />
a sound and complete way<br />
A continuous weakly collision-free path for the green<br />
shape is converted into a discrete sequence of steps<br />
11:00–11:15 ThBT5.7<br />
Bipedal walking control<br />
based on Capture Point dynamics<br />
Johannes Englsberger, Christian Ott, Maximo A. Roa,<br />
Alin Albu-Schäffer, and Gerhard Hirzinger<br />
Institute of Robotics & Mechatronics, German Aerospace Center (DLR)<br />
• Capture Point (CP) dynamics is an<br />
effective and simple tool to design<br />
feedback controllers for bipedal<br />
robots<br />
• CP control is very flexible and can<br />
be used for high-level control<br />
strategies like push recovery and<br />
online step adjustment<br />
• CP control allows to limit the ZMP<br />
to the support polygon explicitly<br />
• Robustness of CP feedback<br />
control is shown analytically, in<br />
simulation and in experiments on<br />
DLR Biped<br />
DLR Biped<br />
CP shifting with ZMP<br />
CP<br />
COM<br />
ZMP<br />
Shifting CP<br />
from foot to foot<br />
10:55–11:00 ThBT5.6<br />
Using a Multi-Objective Controller to Synthesize<br />
Simulated Humanoid Robot Motion with<br />
Changing Contact Configurations<br />
Karim Bouyarmane and Abderrahmane Kheddar<br />
CNRS-AIST JRL UMI3218/CRT, AIST, Japan<br />
LIRMM, Montpellier 2 University, France<br />
• Non-gaited acyclic multi-contact closedloop-based<br />
motion generation tool for a<br />
humanoid robot.<br />
• Input: sequence of static contact-transition<br />
postures, output: dynamically consistent<br />
motion between the postures.<br />
• Multi-objective control: CoM, end-effector,<br />
posture. Two kinds of objectives: set-point<br />
objective and target objective.<br />
• Objectives automatically decided by a<br />
finite-state machine.<br />
• Results in constraint-based dynamic<br />
simulation (non real-time).<br />
11:15–11:30 ThBT5.8<br />
Human-like Walking<br />
with Toe Supporting for Humanoids<br />
Kanako Miura, Mitsuharu Morisawa, Fumio Kanehiro<br />
Shuuji Kajita, Kenji Kaneko and Kazuhito Yokoi<br />
Intelligent Systems Research Institute,<br />
National Institute of Advanced Industrial Science and Technology, Japan<br />
• Three characteristics of human walking<br />
were added: single toe support, stretching<br />
knees, and foot swing motion.<br />
• Modifications to add these characteristics<br />
are made based on the conventional<br />
walking pattern generator.<br />
• The generated pattern is converted to a<br />
feasible pattern using a prioritized<br />
optimization technique.<br />
• A stabilizer is also improved to maintain<br />
ZMP inside of tiny supporting area of toe.<br />
• The aimed walking motion was<br />
successfully demonstrated with HRP-4C.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–196–
Session ThBT6 Continental Ballroom 6 Thursday, September 29, <strong>2011</strong>, 10:00–11:30<br />
Symposium: Robots that Can See II<br />
Chair José Neira, Univ. de Zaragoza<br />
Co-Chair Dieter Fox, Univ. of Washington<br />
10:00–10:15 ThBT6.1*<br />
Semi-Plenary Invited Talk: Vision in the Field<br />
Peter Corke, QUT<br />
10:30–10:45 ThBT6.3<br />
Capturing City-level Scenes with a<br />
Synchronized Camera-Laser Fusion Sensor<br />
Yunsu Bok, Dong-Geol Choi, Yekeun Jeong, and In So Kweon<br />
RCV Lab., EE Dept., KAIST, Korea<br />
• A novel camera-laser fusion system :<br />
synchronized to be mounted on a fastmoving<br />
ground vehicle<br />
• Reconstruction by accumulating laser<br />
scans : motion estimation without the<br />
2D assumption and refinement by a few<br />
closed loops<br />
• Moving object detection :<br />
a solution for detecting moving objects<br />
which cannot be detected by cameras<br />
10:15–10:30 ThBT6.2*<br />
Semi-Plenary Invited Talk: Vision in Space<br />
Andrew Howard, Space Exploration Technologies (SpaceX)<br />
10:45–10:50 ThBT6.4<br />
Vehicle Detection and Tracking at Nighttime for<br />
Urban Autonomous Driving<br />
Hossein Tehrani, Koji Takahashi, Seiichi Mita,<br />
Smart Vehicle Research Center, Toyota Technological Institute, Japan<br />
David McAllester<br />
Toyota Technological Institute at Chicago, USA<br />
• Present a weighted deformable object<br />
model for detecting and tracking vehicles<br />
using monocular infra-red camera.<br />
• Filters are learned through combination of<br />
latent support vector machine and<br />
histograms of oriented gradients.<br />
• Detected vehicles are tracked by<br />
calculating maximum HOG features<br />
compatibility for both root and parts.<br />
• Achieve an average vehicle detection and<br />
tracking rate of 83.78% with very low false<br />
positive.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–197–<br />
Multi Vehicles detection and<br />
tracking at night time
Session ThBT6 Continental Ballroom 6 Thursday, September 29, <strong>2011</strong>, 10:00–11:30<br />
Symposium: Robots that Can See II<br />
Chair José Neira, Univ. de Zaragoza<br />
Co-Chair Dieter Fox, Univ. of Washington<br />
10:50–10:55 ThBT6.5<br />
Dense Multi-Planar Scene Estimation from a<br />
Sparse Set of Images<br />
Alberto Argiles, Javier Civera and Luis Montesano<br />
I3A, Universidad de Zaragoza, Spain<br />
• 3D scene estimation from visual data is limited, in most cases, to a sparse<br />
set of salient points.<br />
• Robots need dense scene estimations in order to properly interact with a<br />
3D world.<br />
• We present an algorithm to densify a<br />
sparse point-based scene estimation for<br />
Manhattan-like environments.<br />
• The algorithm combines robust multimodel<br />
estimation and geometric and<br />
photometric tests again the visual data.<br />
Input images Dense multi-planar 3D scene estimation<br />
11:00–11:15 ThBT6.7<br />
Real-time Step Edge Estimation Using Stereo<br />
Images for Biped Robot<br />
Minami Asatani<br />
The Fundamental Technology Research Center, Honda R&D Co. Ltd, Japan.<br />
Shigeki Sugimoto and Masatoshi Okutomi<br />
Dept. of Mech. and Control Eng., Tokyo Institute of Technology, Japan.<br />
• A step edge estimation method using<br />
stereo images for the biped robots with<br />
the ability of the overhanging footstep.<br />
• The overhanging footstep: advantageous<br />
in terms of smooth walking and<br />
relaxation of restrictions on gait planning.<br />
• Pre-processing: Occupancy grid<br />
segmentation & fast direct image<br />
alignment for estimating the plane<br />
parameters of each step.<br />
• Step edge estimation: By our image<br />
segmentation technique minimizing a<br />
cost composed of the sum of pixel value<br />
differences of stereo images.<br />
• Optional use of uncalibrated projector for<br />
texture-less surfaces.<br />
The overhanging<br />
footstep<br />
The overhanging footstep and<br />
edge estimation results.<br />
10:55–11:00 ThBT6.6<br />
Plenoptic Flow: Closed-Form Visual<br />
Odometry for Light Field Cameras<br />
Donald G. Dansereau, Ian Mahon,<br />
Oscar Pizarro and Stefan B. Williams<br />
Australian Centre for Field Robotics, University of Sydney, Australia<br />
• Classic approaches to visual odometry are iterative and/or complex<br />
• We present a closed-form, featureless solution to visual odometry for<br />
planar camera arrays, a.k.a. “light field cameras”<br />
• Three solutions capable of estimating a camera’s 6-DOF trajectory in a<br />
closed-form manner are presented<br />
• Computation is simple and results are competitive with SIFT+RANSAC<br />
11:15–11:30 ThBT6.8<br />
Exploiting Motion Priors in Visual Odometry<br />
for Vehicle-Mounted Cameras<br />
with Non-holonomic Constraints<br />
Davide Scaramuzza 1 , Andrea Censi 2 , and Kostas Daniilidis 1<br />
1 GRASP Lab, University of Pennsylvania<br />
2 California Institute of Technology<br />
• We present an algorithm (MOBRAS) for<br />
outlier removal which is not based on<br />
RANSAC<br />
• Contrary to RANSAC, motion hypotheses are<br />
not generated from randomly-sampled point<br />
correspondences, but from a “proposal<br />
distribution” that is built by exploiting the<br />
vehicle non-holonomic constraints<br />
• We show that not only is the proposed<br />
algorithm significantly faster than RANSAC,<br />
but that the returned solution may also be<br />
better in that it favors the underlying motion<br />
model of the vehicle, thus overcoming the<br />
typical limitations of RANSAC<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–198–
Session ThBT7 Continental Parlor 7 Thursday, September 29, <strong>2011</strong>, 10:00–11:30<br />
Medical Robots & Systems<br />
Chair Pierre Dupont, Children's Hospital Boston, Harvard Medical School<br />
Co-Chair Jung Kim, KAIST<br />
10:00–10:15 ThBT7.1<br />
Identification of a hybrid model for simulation of<br />
the instrument/trocar interaction force<br />
J. Verspecht, L. Catoire, S. Torfs and M. Kinnaert<br />
Control Engineering and System Analysis Department<br />
Université libre de Bruxelles (ULB), Belgium<br />
• Design of a simulator for the slave robot<br />
interacting with its environment in<br />
minimally invasive robotic surgery.<br />
• Short introduction to the Extended<br />
Maxwell-Slip (EMS) model, a 2-state<br />
hybrid friction model including a<br />
deformation and a sliding phase<br />
• Identification procedure for the EMS<br />
model using a PWARX identification<br />
techniques namely the clustering based<br />
technique.<br />
• Identification and validation are achieved<br />
using experimental data recorded on a<br />
dedicated test setup.<br />
10:30–10:45 ThBT7.3<br />
Bio-inspired Active Soft Orthotic Device<br />
for Ankle Foot Pathologies<br />
Yong-Lae Park1 , Bor-rong Chen2 , Diana Young1 , Leia Stirling1 ,<br />
Robert J. Wood1,2 , Eugene Goldfield1,3 and Radhika Nagpal1,2 1Wyss Institute for Biologically Inspired Engineering, Harvard University, USA<br />
2School of Engineering and Applied Science, Harvard University, USA<br />
3Children’s Hospital Boston, USA<br />
• Active soft ankle-foot<br />
orthotic device<br />
• Biologically inspired<br />
design based on a<br />
musculoskeletal system<br />
of a human foot and leg<br />
• Soft sensor and<br />
actuator materials with<br />
no rigid frame structure<br />
• For treating gait<br />
pathologies associated<br />
with neuromuscular<br />
disorders<br />
10:15–10:30 ThBT7.2<br />
A Fast Classification System For Decoding<br />
of Human Hand Configurations<br />
Using Multi-Channel sEMG Signals<br />
Myoung Soo Park, Keehoon Kim and Sang Rok Oh<br />
Center of Human-centered Interaction and Robotics<br />
Korea Institute of Science and Technology (KIST), Korea<br />
• We propose a novel fast classification<br />
system that allows real-time decoding of<br />
human movement intentions from multichannel<br />
sEMG signals<br />
• For extracting small-dimensional and<br />
discriminative features, CA-PCA (Classaugmented<br />
PCA) are applied to RMS<br />
(root-mean-squared) sEMG signals<br />
• As a fast classifier, ELM (Extreme<br />
Learning Machine) are selected and used<br />
• From 28,000 8-channel sEMG of 4 actions<br />
and rest, feature and classifier are learned<br />
in less than 10 secs and can classify<br />
280,000 test data with 91.59% accuracy<br />
10:45–10:50 ThBT7.4<br />
A Preliminary Experiment for Transferring<br />
Human Motion to a Musculoskeletal Robot<br />
― Decomposition of Human Running based on Muscular Coordination ―<br />
Taiki Iimura, Keita Inoue, Hang T.T. Pham,<br />
Hiroaki Hirai, and Fumio Miyazaki<br />
Mechanical Science and Bioengineering, Osaka University, Japan<br />
• The electromyographic signals of 8 leg<br />
muscles during human running were<br />
measured.<br />
• Two parameters related to kinematics or<br />
kinetics are defined from EMG data.<br />
• Principal Component Analysis<br />
decomposes human running into only two<br />
muscle coordination patterns.<br />
• The kinematic meanings of the extracted<br />
EMG patterns were visualized by a<br />
musculoskeletal leg robot.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–199–<br />
Robot movements corresponding<br />
to the extracted EMG patterns
Session ThBT7 Continental Parlor 7 Thursday, September 29, <strong>2011</strong>, 10:00–11:30<br />
Medical Robots & Systems<br />
Chair Pierre Dupont, Children's Hospital Boston, Harvard Medical School<br />
Co-Chair Jung Kim, KAIST<br />
10:50–10:55 ThBT7.5<br />
Heart Motion Simulator for Motion<br />
Compensation<br />
Renat Iskakov, Martin Groeger and Gerd Hirzinger<br />
Institute of Robotics and Mechatronics,<br />
German Aerospace Center (DLR), Germany<br />
• Heart motion simulator<br />
(HMS) to accurately<br />
simulate 3D translational<br />
motion trajectories of<br />
human beating heart<br />
• System description<br />
including design,<br />
kinematics, and closedloop<br />
dynamics<br />
• Impedance control structure for accurate simulation and future<br />
interaction with medical tools<br />
• Experiments show the capability of the HMS to simulate different lowamplitude<br />
translational motion trajectories<br />
11:00–11:15 ThBT7.7<br />
New Approach for Abnormal Tissue Localization<br />
with Robotic Palpation and Mechanical Property<br />
Characterization<br />
Bummo Ahn, Yeongjin Kim, and Jung Kim<br />
Department of Mechanical Engineering, KAIST, Korea<br />
• Abnormal tissue localization<br />
- Robotic palpation: scanning & sweeping<br />
- FEM-based mechanical property characterization.<br />
New approach for abnormal tissue localization<br />
Palpation experiment<br />
Mechanical property characterization<br />
10:55–11:00 ThBT7.6<br />
MRI-Powered Actuators for Robotic Interventions<br />
Panagiotis Vartholomeos and Pierre E. Dupont<br />
Cardiovascular Surgery, Children’s Hospital Boston,<br />
Harvard Medical School, USA<br />
Lei Qin<br />
Department of Radiology, Brigham & Women’s Hopsital, Harvard<br />
Medical School, USA<br />
• Novel actuation technology for<br />
robotically assisted MRI-guided<br />
interventional procedures<br />
• Actuators are both powered and<br />
controlled by the MRI scanner<br />
• Design concept and performance<br />
limits are described and derived<br />
analytically<br />
• Experiments in a clinical MR scanner<br />
are used to demonstrate the capability<br />
of the approach for needle biopsies<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–200–<br />
MRI-powered<br />
actuator<br />
MRI-powered actuator performs<br />
needle biopsy in porcine heart.<br />
11:15–11:30 ThBT7.8<br />
Position Estimation of an Epicardial Crawling<br />
Robot on the Beating Heart by Modeling of<br />
Physiological Motion<br />
Nathan Wood and Cameron Riviere<br />
The Robotics Institute, Carnegie Mellon University, USA<br />
Diego Moral del Agua<br />
University of Valladolid, Spain<br />
Marco Zenati<br />
BHS Department of Cardiothoracic Surgery, Harvard Medical School, USA<br />
• HeartLander robot provides therapies to<br />
the heart in a minimally invasive manner<br />
• Modeling of cardiac and respiratory<br />
motion enables more accurate position<br />
estimation of robot on heart<br />
• Fourier series models of physiological<br />
motions are fit using an Extended Kalman<br />
Filter (EKF) framework<br />
• Improves localization over previous<br />
filtering techniques and enables<br />
locomotion synchronization with<br />
physiological cycles on heart phantom
Session ThBT8 Continental Parlor 8 Thursday, September 29, <strong>2011</strong>, 10:00–11:30<br />
Search & Rescue Robots<br />
Chair Timothy H. Chung, Naval Postgraduate School<br />
Co-Chair Geoffrey Hollinger, Univ. of Southern California<br />
10:00–10:15 ThBT8.1<br />
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10:30–10:45 ThBT8.3<br />
Optimal Deployment of Robotic Teams for<br />
Autonomous Wilderness Search and Rescue<br />
Ashish Macwan, Goldie Nejat, and Beno Benhabib<br />
Department of Mechanical and Industrial Engineering, University of Toronto,<br />
Canada<br />
• An optimal re-deployment method for autonomous robotic Wilderness<br />
Search and Rescue (WiSAR) is presented<br />
• WiSAR challenges include: moving lost person, growing search area,<br />
and varying terrain<br />
• The proposed method addresses initial deployment as well as<br />
subsequent online re-deployment<br />
• The novel concept of iso-probability curves is used to predict and<br />
represent the lost person’s probable location<br />
• Optimal deployment / re-deployment is based on minimizing search time<br />
and maximizing probability of success<br />
• A simulated WiSAR search scenario demonstrates the method<br />
10:15–10:30 ThBT8.2<br />
Searching for Multiple Targets Using<br />
Probabilistic Quadtrees<br />
Stefano Carpin and Derek Burch<br />
School of Engineering, University of California - Merced, USA<br />
Timothy H. Chung<br />
Dept. of Systems Engineering, Naval Postgraduate School - Montery, USA<br />
• We extend Probabilistic Quadtrees for<br />
search to deal with an arbitrary, unknown<br />
number of targets;<br />
• An information gain function is defined to<br />
guide the search;<br />
• An entropy based criterion terminates the<br />
search;<br />
• We theoretically show that in the limit the<br />
hierarchical representation converges to a<br />
uniform grid;<br />
• Extensive simulation show hierarchical<br />
search largely outperforms uniform<br />
approaches.<br />
10:45–10:50 ThBT8.4<br />
Development of the high strength retractable<br />
skin and the closed type crawler vehicle<br />
Takeshi Aoki and Takahiro Karino<br />
Advanced robotics, Chiba Institute of Technology, Japan<br />
Hiroyuki Kuwahara<br />
SUSTAINable Robotics, Japan<br />
• Proposal and development of the high<br />
strength retractable skin<br />
• Proposal a new concept of crawler vehicle<br />
and mechanisms covered by the skin<br />
• Development of the mono crawler vehicle,<br />
named “Slug Crawler Vehicle: SCV”<br />
• SCV has water and dust proof abilities by<br />
the Flexible crawler belt<br />
• Verification of its effectiveness by basic<br />
experiments of the prototype model<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–201–<br />
The closed type flexible<br />
crawler vehicle
Session ThBT8 Continental Parlor 8 Thursday, September 29, <strong>2011</strong>, 10:00–11:30<br />
Search & Rescue Robots<br />
Chair Timothy H. Chung, Naval Postgraduate School<br />
Co-Chair Geoffrey Hollinger, Univ. of Southern California<br />
10:50–10:55 ThBT8.5<br />
Human-in-the-Loop: MPC for Shared Control of a<br />
Quadruped Rescue Robot<br />
Rahul Chipalkatty, Hannes Daepp,<br />
Magnus Egerstedt*, and Wayne Book<br />
Mechanical Engineering, Electrical and Computer Engineering*,<br />
Georgia Institute of Technology, USA<br />
• Composition of human input with an<br />
automatic controller to ensure static<br />
rescue robot stability.<br />
• A hybrid control architecture used to<br />
implement the supporting gait.<br />
• Control applied to a rescue robot<br />
simulation with real-time human input from<br />
haptic joysticks.<br />
• Experimental results confirm the viability<br />
of control approach: strengths of both<br />
human and robot control.<br />
10:55–11:00 ThBT8.6<br />
Detection and Tracking of Road Networks in<br />
Rural Terrain Fusing Vision and LIDAR<br />
Michael Manz, Michael Himmelsbach, Thorsten Luettel<br />
and Hans-Joachim Wuensche<br />
Autonomous System Technology, University of the Bundeswehr Munich,<br />
Germany<br />
• Fusing 3D LIDAR data and camera<br />
images into an accumulated, colored 3D<br />
elevation map of the terrain<br />
• Model based estimation of the local road<br />
network geometry using a particle filter<br />
and multiple features within the map<br />
• Supervised learning is applied to assess<br />
the likelihood of particles<br />
• Enables our robot MuCAR-3 to<br />
autonomously navigate on dirt-road<br />
networks<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–202–<br />
Crossing detection
Session ThBT9 Continental Parlor 9 Thursday, September 29, <strong>2011</strong>, 10:00–11:30<br />
Intelligent Transportation Systems (Automotive)<br />
Chair Bernd Radig, TU Muenchen<br />
Co-Chair Juan Nieto, Univ. of Sydney, Australian Centre for Field Robotics<br />
10:00–10:15 ThBT9.1<br />
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10:30–10:45 ThBT9.3<br />
Autonomous Intersection Management:<br />
Multi-Intersection Optimization<br />
Matthew Hausknecht, Tsz-Chiu Au, and Peter Stone<br />
Department of Computer Science, University of Texas at Austin, USA<br />
• In a future of autonomous vehicles,<br />
intelligent traffic management systems<br />
such as Autonomous Intersection<br />
Management (AIM) are crucial<br />
• Exploring AIM for the first time on road<br />
networks, we:<br />
• Examine vehicle navigations<br />
strategies<br />
• Observe an instance of Braess’<br />
Paradox<br />
• Dynamically reverse the direction of<br />
traffic along selected lanes<br />
• We quantify the improvements imparted<br />
by these agent-based traffic control<br />
methods<br />
Intersection in AIM simulator<br />
10:15–10:30 ThBT9.2<br />
Tracking Objects of Arbitrary Shape Using<br />
Expectation Maximization Algorithm<br />
Shuqing Zeng and Yuanhong Li<br />
Electrical and Controls Lab, GM R&D Center, USA<br />
Yantao Shen<br />
Department of Electrical and Biomedical Engineering, University of Nevada,<br />
USA<br />
• Address the general object tracking with<br />
arbitrary shape using rangefinders, which<br />
is a key module for an autonomous driving<br />
vehicle.<br />
• An Expectation Maximization (EM)<br />
algorithm with locally matching is<br />
proposed for motion estimation between<br />
two consecutive range images.<br />
• The complexity of the algorithm is O(N)<br />
with N the numbers of scan points.<br />
• Quantitative performance evaluation of the<br />
algorithm using a benchmarking vehicular<br />
data set.<br />
• Results of road tests show the<br />
effectiveness and efficiency of the system.<br />
10:45–10:50 ThBT9.4<br />
Evaluation of Different Approaches for Road<br />
Course Estimation using Imaging Radar<br />
Frederik Sarholz, Jens Mehnert, Jens Klappstein<br />
and Juergen Dickmann<br />
Group Research/Environment Perception, Daimler AG, Germany<br />
Bernd Radig<br />
Intelligent Autonomous Systems, Technische Universitaet Muenchen, Germany<br />
• Three approaches for road course<br />
estimation are evaluated based on<br />
several hundred kilometers of country<br />
roads:<br />
1. spatial derivative + lane free of radar<br />
echoes<br />
2. angle extraction of the road border +<br />
projection onto the vehicle’s course<br />
(performs up to 78% better than 1)<br />
3. usage of objects moving parallel to the<br />
road course<br />
(performs up to 209% better than 1)<br />
• The driven trajectory is taken as ground<br />
truth<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–203–<br />
x[m]<br />
160<br />
120<br />
80<br />
40<br />
0<br />
x[m]<br />
80<br />
40<br />
y[m] y[m]<br />
Left: estimation with second approach<br />
Right: estimation with third approach,<br />
the dots are the measured positions of<br />
the followed vehicle<br />
0
Session ThBT9 Continental Parlor 9 Thursday, September 29, <strong>2011</strong>, 10:00–11:30<br />
Intelligent Transportation Systems (Automotive)<br />
Chair Bernd Radig, TU Muenchen<br />
Co-Chair Juan Nieto, Univ. of Sydney, Australian Centre for Field Robotics<br />
10:50–10:55 ThBT9.5<br />
A Car Transportation System Using<br />
Multiple Mobile Robots: iCART II<br />
K. Kashiwazaki 1 , N. Yonezawa 1 , M. Endo 2 , K. Kosuge 1 ,<br />
Y. Sugahara 1 , Y. Hirata 1 , T. Kanbayashi 3 , K. Suzuki 3 ,<br />
K. Murakami 3 and K. Nakamura 3<br />
1 Department of Bioengineering and Robotics, Tohoku University, Japan<br />
2 Department of Mechanical Engineering, Nihon University, Japan<br />
3 IHI Transport Machinery Co., Ltd., Japan<br />
• This paper proposes a car transportation<br />
system, iCART II, based on “a-robot-fora-wheel”<br />
concept.<br />
• A prototype system, MRWheel (a Mobile<br />
Robot for a Wheel), is designed.<br />
• We propose a decentralized control<br />
algorithm for car transportation in<br />
coordination by using a leader-follower<br />
type multiple robot system.<br />
• The proposed control algorithm is applied<br />
to the car transportation system and the<br />
experimental results are reported.<br />
Mobile Base Module<br />
Connecting Module<br />
Lifter Module Omni-directional Wheel<br />
11:00–11:15 ThBT9.7<br />
Intelligent Driver Drowsiness Detection System<br />
Using Uncorrelated Fuzzy Locality Preserving<br />
Analysis<br />
Rami N. Khushaba, Sarath Kodagoda, Gamini Dissanayake<br />
ARC centre of Excellence for Autonomous Systems, Faculty of Engineering and<br />
Information Technology, University of Technology, Sydney (UTS), Australia.<br />
Sara Lal<br />
Department of Medical and Molecular Biosciences, Faculty of Science,<br />
University of Technology, Sydney (UTS), Australia.<br />
• Driver drowsiness is believed to account<br />
for 20% of all vehicle accidents on<br />
motorways and monotonous roads.<br />
• Problem: identify physiological<br />
associations between driver drowsiness<br />
and the corresponding patterns of EEG,<br />
EOG, and ECG.<br />
• Approach: A pattern recognition approach<br />
is adopted by proposing a novel feature<br />
extraction and reduction algorithm UFLPA.<br />
• Experiments on 31 participants provided a<br />
classification accuracy of ~95%.<br />
Driving simulation system<br />
10:55–11:00 ThBT9.6<br />
Probabilistic Road Geometry Estimation using a<br />
Millimetre-Wave Radar<br />
Andres Hernandez-Gutierrez, University of Sydney, Australian<br />
Centre for Field Robotics<br />
Juan Nieto, University of Sydney, Australian Centre for Field<br />
Robotics<br />
Tim Bailey, University of Sydney<br />
Eduardo Nebot, Unversity of Sydney<br />
11:15–11:30 ThBT9.8<br />
An Efficient Control Strategy for the Traffic<br />
Coordination of AGVs<br />
Roberto Olmi<br />
Elettric80, Italy<br />
Cristian Secchi and Cesare Fantuzzi<br />
DISMI, University of Modena and Reggio Emilia, Italy<br />
• Coordination of AGVs over a zone<br />
cotrolled layout<br />
• Coordination diagrams are used for<br />
representing possible collisions<br />
• A computationally efficient strategy for<br />
segments allocations is proposed<br />
• The traffic control strategy is tested over<br />
real industrial layouts<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–204–
Session ThCT1 Continental Parlor 1 Thursday, September 29, <strong>2011</strong>, 14:45–16:15<br />
Manipulation<br />
Chair Mike Stilman, Georgia Tech.<br />
Co-Chair Ludovic Righetti, Univ. of Southern California<br />
14:45–15:00 ThCT1.1<br />
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15:15–15:30 ThCT1.3<br />
Distributed Generalization of<br />
Learned Planning Models in Robot PbD<br />
Rainer Jäkel, Sven R. Schmidt-Rohr, Rüdiger Dillmann<br />
Institute for Anthropomatics, Karlsruhe Institute of Technology, Germany<br />
Pascal Meißner<br />
FZI - Research Center for Information Technologies, Germany<br />
• Manipulation knowledge can be learned<br />
efficiently from human observation<br />
• Problem: how to determine a reasonable<br />
set of learning features?<br />
• Approach: distributed simulation and<br />
constrained motion planning to generate<br />
statistics about constraint inconsistencies<br />
• Goal: determine a maximal subset of task<br />
constraints, which allows the robot to<br />
solve a set of test problems<br />
15:00–15:15 ThCT1.2<br />
Push Planning for Object Placement on<br />
Cluttered Table Surfaces<br />
Akansel Cosgun, Tucker Hermans, Victor Emeli and Mike Stilman<br />
Center for Robotics and Intelligent Machines<br />
Georgia Institute of Technology, USA<br />
• A planner to find a sequence of<br />
pushes of tabletop objects to make<br />
room for a target object<br />
• Implemented in dynamic simulation<br />
• Efficient and applicable to practical<br />
domains<br />
(a) Initial State<br />
• Placement pose found by convolving object kernel<br />
over table and sampling among the least<br />
overlapping poses<br />
• Iterative Deepening Breadth-First-Search (IDBFS)<br />
used to try different placement poses and gradually<br />
allow more complex plans<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–205–<br />
7<br />
6<br />
4<br />
5<br />
(b) Plan<br />
6<br />
6<br />
7<br />
7<br />
7<br />
6<br />
4<br />
4<br />
5<br />
Area 5 is cleared<br />
15:30–15:35 ThCT1.4<br />
Learning Force Control Policies<br />
for Compliant Manipulation<br />
Mrinal Kalakrishnan * , Ludovic Righetti * ,<br />
Peter Pastor * , and Stefan Schaal *†<br />
* CLMC Lab, University of Southern California, USA<br />
† Max-Planck-Institute for Intelligent Systems, Tübingen, Germany<br />
• Compliant actuation and control are<br />
critical for safe operation of robots in<br />
household environments.<br />
• Manipulation involves exerting forces on<br />
the world through contact interaction.<br />
• We learn control policies for both positions<br />
and forces, using the PI 2 reinforcement<br />
learning algorithm.<br />
• This allows compliant execution with<br />
increased robustness as opposed to stiff<br />
position control.<br />
• We use this method to learn dooropening<br />
and pen-grasping tasks on a<br />
Barrett WAM arm.<br />
4<br />
5<br />
5
Session ThCT1 Continental Parlor 1 Thursday, September 29, <strong>2011</strong>, 14:45–16:15<br />
Manipulation<br />
Chair Mike Stilman, Georgia Tech.<br />
Co-Chair Ludovic Righetti, Univ. of Southern California<br />
15:35–15:40 ThCT1.5<br />
Autonomous Indoor Aerial Gripping<br />
Using a Quadrotor<br />
Vaibhav Ghadiok, Jeremy Goldin and Wei Ren<br />
Electrical & Computer Engineering, Utah State University, USA<br />
• Custom-built quadrotor equipped with<br />
low-cost sensors: gyroscopes,<br />
accelerometers, camera, IR camera and<br />
sonar<br />
• Capabilities include visual SLAM,<br />
waypoint navigation, target search and<br />
aerial gripping while hovering<br />
• IR camera to locate target and<br />
underactuated passively compliant<br />
gripper for gripping<br />
• Nested PID controllers for attitude<br />
stabilization, altitude regulation,<br />
navigation and gripping<br />
15:45–16:00 ThCT1.7<br />
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15:40–15:45 ThCT1.6<br />
Shooting Manipulation System<br />
with High Reaching Accuracy<br />
Tomofumi Hatakeyama and Hiromi Mochiyama<br />
Department of Intelligent Interaction Technologies<br />
University of Tsukuba, Japan<br />
• We propose a shooting manipulation<br />
system with high reaching accuracy for<br />
manipulating a distant object<br />
• The proposed system has excellent<br />
performances such as high motion<br />
straightness, quickness and reaching<br />
accuracy<br />
• The end-effector rests at target point, then<br />
we clarify the mechanics of this motion<br />
• Realize quick capturing of a falling object<br />
at 700[mm] away with high success rate<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–206–<br />
Quick capturing motion<br />
16:00–16:15 ThCT1.8<br />
A Novel Approach to the Manipulation of Body-Parts<br />
Ownership Using a Bilateral Master-Slave System<br />
Masayuki Hara, Akio Yamamoto, and Toshiro Higuchi<br />
Dept. of Precision Engineering, The University of Tokyo, Japan<br />
Giulio Rognini and Hannes Bleuler<br />
Institute of Microengineering, EPFL, Switzerland<br />
Nathan Evans and Olaf Blanke<br />
Brain Mind Institute, EPFL, Switzerland<br />
• A novel tactile rubber hand illusion (RHI)<br />
paradigm with haptics/robotics technology<br />
is proposed for manipulating body-parts<br />
ownership<br />
• A 3-DOF master-slave system is designed<br />
to enable RHI experiments under active<br />
self-touch condition<br />
• The applicability of the proposed system is<br />
validated conducting a self-touch-enabled<br />
RHI experiment<br />
• The proposed approach has a possibility<br />
to answer current open questions about<br />
body representation in human brain<br />
Paintbrush attached on<br />
a slave device<br />
Rubber hand<br />
Paintbrush attached on<br />
a master device<br />
Participant<br />
Self-touch-enabled RHI paradigm<br />
with a master-slave system
Session ThCT2 Continental Parlor 2 Thursday, September 29, <strong>2011</strong>, 14:45–16:15<br />
Industrial Robots<br />
Chair Jun Ota, The Univ. of Tokyo<br />
Co-Chair Rolf Johansson, Lund Univ.<br />
14:45–15:00 ThCT2.1<br />
Assembly Strategy Modeling and Selection for<br />
Human and Robot Coordinated Cell Assembly<br />
Fei Chen 1 , Kosuke Sekiyama 1 , Hironobu Sasaki 1 , Jian Huang 3 ,<br />
Baiqing Sun 4 , Toshio Fukuda 1,2<br />
1 Department of Micro-nano Systems Engineering, Nagoya University, Japan.<br />
2 Center for Micro-nano Mechatronics, Nagoya University, Japan.<br />
3 Department of Control Science and Engineering,<br />
Huazhong University of Science and Technology, China.<br />
4 School of Electrical Engineering, Shenyang University of Technology, China.<br />
•A Dual Generalized Stochastic Petri Net<br />
(GSPN) model is built based on a practical<br />
assembly task for human and robot<br />
coordination by taking possible human and<br />
robot behaviors into consideration.<br />
• Based on GSPN, Monte Carlo method is<br />
carried out to study the time cost and<br />
payment cost for different assembly<br />
subtask scheduling strategies within<br />
human and robot.<br />
• Multiple-Objective Optimization (MOOP)<br />
method related Cost-effectiveness analysis<br />
is adopted to select the optimal assembly<br />
scheduling strategy.<br />
15:15–15:30 ThCT2.3<br />
Reusable Hybrid Force-Velocity controlled<br />
Motion Specifications with executable DSL<br />
Markus Klotzbuecher, Ruben Smits,<br />
Herman Bruyninckx, Joris De Schutter<br />
Department of Mechanical Engineering<br />
Katholieke Universiteit Leuven, Belgium<br />
• An approach for modeling reusable<br />
robotic tasks using Domain Specific<br />
Languages (DSL)<br />
• Models of force-velocity controlled<br />
motions are composed in finite state<br />
machines<br />
• DSL are used to separate platform<br />
independent from platform specific<br />
aspects<br />
• Illustrated by an alignment task<br />
executed on a KUKA LWR and a PR2<br />
robot<br />
Alignment task executed by a<br />
KUKA LWR robot<br />
15:00–15:15 ThCT2.2<br />
Stereographic projection for industrial<br />
manipulator tasks: Theory and experiments<br />
Magnus Bjerkeng and Kristin Y. Pettersen<br />
Department of Engineering Cybernetics, NTNU, Norway<br />
Erik Kyrkebø<br />
SINTEF ICT Applied Cybernetics, Norway<br />
• A pseudoinverse controller using a<br />
task representation based on the<br />
stereographic projection, applied to<br />
a dynamic surveillance problem, is<br />
presented<br />
• A comparative analysis between<br />
similar task representations is<br />
provided<br />
• A qualitative analysis of the global<br />
dynamics is presented and the<br />
existence and uniqueness of<br />
singularities is determined<br />
• Experiments using industrial<br />
manipulators are presente.<br />
15:30–15:35 ThCT2.4<br />
Entropy-Based Motion Selection<br />
for Touch-Based Registration<br />
Using Rao-Blackwellized Particle Filtering<br />
Yuichi Taguchi, Tim K. Marks, and John R. Hershey<br />
Mitsubishi Electric Research Labs (MERL), Cambridge, MA, USA<br />
• 6-DOF robot-to-object registration using<br />
a sequence of touches<br />
• Efficient online estimation using Rao-<br />
Blackwellized Particle Filtering (RBPF)<br />
• Entropy-based motion selection to<br />
determine optimal next touch location<br />
• Comparison of several entropy<br />
measures for RBPF<br />
• Applications: Fully automatic registration<br />
and human-guided registration<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–207–<br />
Probe<br />
Particle Distribution<br />
on Object Model<br />
Force<br />
Sensor<br />
Object
Session ThCT2 Continental Parlor 2 Thursday, September 29, <strong>2011</strong>, 14:45–16:15<br />
Industrial Robots<br />
Chair Jun Ota, The Univ. of Tokyo<br />
Co-Chair Rolf Johansson, Lund Univ.<br />
15:35–15:40 ThCT2.5<br />
Manipulator System Selection Based on<br />
Evaluation of Task Completion Time and Cost<br />
Yanjiang Huang, Lounell B. Gueta and Jun Ota<br />
Research into Artifacts, Center for Engineering, The University of Tokyo, Japan<br />
Ryosuke Chiba<br />
Faculty of System Design, Tokyo Metropolitan University, Japan<br />
Tamio Arai<br />
Department of Precision Engineering, The University of Tokyo, Japan<br />
Tsuyoshi Ueyama<br />
DENSO WAVE INCORPORATED, Japan<br />
Masao Sugi<br />
Department of Mechanical Engineering and Intelligent Systems,<br />
University of Electro-Communications, Japan<br />
• Multiple objective particle swarm<br />
optimization is utilized to search for<br />
appropriate manipulator systems.<br />
• Location optimization and motion<br />
coordination are utilized to calculate task<br />
completion time of manipulator system.<br />
• Relative cost is utilized to calculate cost of<br />
manipulator system.<br />
• Particle swarm optimization and nearest<br />
neighborhood algorithm are employed in<br />
location optimization and motion<br />
coordination.<br />
System selection Start<br />
Select manipulator system, s<br />
Location optimization<br />
Select location of positioning table, L<br />
Motion coordination<br />
Update location<br />
No<br />
Terminate select L ?<br />
Yes<br />
Calculate cost<br />
Update manipulator system<br />
Terminate select s ?<br />
Yes<br />
End<br />
No<br />
Proposed method for<br />
manipulator system selection<br />
15:45–16:00 ThCT2.7<br />
Dynamics identification of industrial robots<br />
using contact force for the IDCS control<br />
Kengo AOKI , Gentiane VENTURE , Yasutaka TAGAWA<br />
Department of Mechanical Systems Engineering,<br />
Tokyo University of Agriculture and Technology, Tokyo, Japan<br />
• The application of identification of<br />
the robot using the base link<br />
information<br />
• The concept of the IDCS (Inverse<br />
Dynamics Compensation via<br />
‘Simulation of feedback control<br />
systems’) controller<br />
• The simulation results of<br />
comparison of the IDCS controller<br />
performance with PID controller<br />
Weight with adding<br />
acceleration sensor<br />
Motor with<br />
an encoder<br />
Top: The controlled object<br />
Bottom: The cross validation of<br />
� � � identification result<br />
15:40–15:45 ThCT2.6<br />
Modeling and Control of a Piezo-Actuated<br />
High-Dynamic Compensation Mechanism for<br />
Industrial Robots<br />
B. Olofsson, O. Sörnmo, A. Robertsson & R. Johansson<br />
Dept. of Automatic Control, Lund University, Sweden<br />
U. Schneider & A. Puzik<br />
Fraunhofer Institute for Manufacturing and Engineering, Germany<br />
• Piezo-actuated compensation mechanism<br />
for usage together with industrial robots<br />
during machining operations<br />
• High-precision milling despite limited<br />
stiffness and accuracy of the robot<br />
• Modeling of the compensation mechanism<br />
using subspace-based identification<br />
methods and model-based control<br />
scheme<br />
• Experimental evaluations of the control<br />
scheme performed on a prototype of the<br />
mechanism are presented<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–208–<br />
Industrial robot together with the<br />
compensation mechanism.<br />
16:00–16:15 ThCT2.8<br />
A Fast Distributed Auction and Consensus Process<br />
Using Parallel Task Allocation and Execution<br />
G.P. Das, T.M. McGinnity, S.A. Coleman and L. Behera<br />
Intelligent Systems Research Centre, University of Ulster, United Kingdom<br />
• Distributed auction and consensus for<br />
task allocation in a multi robot system<br />
• Task allocation is carried out in parallel to<br />
execution of already allocated tasks<br />
• Task allocation in highly dynamic<br />
scenarios is better than other distributed d<br />
auction based approaches<br />
• Performance analysis<br />
• static cases where all robots and<br />
tasks are active from the start<br />
• dynamic case where new tasks are<br />
activated during the execution<br />
• Faster task allocation in both static and<br />
dynamic scenarios<br />
Figure: Task allocation module in<br />
a single robot. Similar modules<br />
run in each robot of the multi<br />
robot system.
Session ThCT3 Continental Parlor 3 Thursday, September 29, <strong>2011</strong>, 14:45–16:15<br />
Marine Systems I<br />
Chair Stephen Barkby, Univ. of Sydney<br />
Co-Chair Terry Huntsberger, Jet Propulsion Lab.<br />
14:45–15:00 ThCT3.1<br />
Flexible Formation Control Realistic Underactuated<br />
Autonomous Surface Vessels<br />
Bradley Bishop, United States Naval Academy<br />
15:15–15:30 ThCT3.3<br />
Safe Maritime Navigation with COLREGS<br />
Using Velocity Obstacles<br />
Yoshiaki Kuwata, Michael T. Wolf, Dimitri Zarzhitsky,<br />
and Terrance L. Huntsberger<br />
Mobility and Robotics Systems, Jet Propulsion Laboratory,<br />
California Institute of Technology, USA<br />
• In maritime navigation, all vessels must obey COLREGS (“rules of the<br />
road”), which requires specific type of maneuvers (e.g., need to overtake<br />
from the starboard side).<br />
• Developed a motion planner that<br />
incorporates such logic for USVs<br />
(Unmanned Surface Vehicles).<br />
• Velocity Obstacles allow us to<br />
express them naturally in the<br />
velocity space.<br />
• Demonstrated in simulation and<br />
on the water three COLREGS<br />
behaviors: crossing, overtaking,<br />
and head-on.<br />
USV in a crossing situation<br />
15:00–15:15 ThCT3.2<br />
Toward Automatic Classification of Chemical<br />
Sensor Data from Autonomous Underwater Vehicles<br />
Michael V. Jakuba 1 , Daniel Steinberg 1 , James C. Kinsey 2 , Dana<br />
R. Yoerger 2 , Richard Camilli 2 , Oscar Pizarro 1 , Stefan B. Williams 1<br />
1 Australian Centre for Field Robotics, University of Sydney, Australia<br />
2 Woods Hole Oceanographic Institution, USA<br />
• Classification of nearly raw<br />
chemical sensor data from<br />
AUVs can aid follow-on<br />
analysis by both humans<br />
and machines.<br />
• The Variational Dirichlet<br />
Process (VDP) was used to<br />
cluster chemical sensor<br />
data from the Sentry AUV.<br />
• The VDP executes rapidly,<br />
accepts data from multiple<br />
sensors, and does not<br />
require operators to specify<br />
the number of classes a<br />
priori.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–209–<br />
Probability of membership in the plume class<br />
derived from chemical sensor data collected by<br />
the Sentry AUV. A plume is evident trailing off to<br />
the west from the Deepwater Horizon site in the<br />
northeast corner of the plot.<br />
15:30–15:35 ThCT3.4<br />
A Novel Propulsion Method<br />
of Flexible Underwater Robots<br />
Jun Shintake, Aiguo Ming, Makoto Shimojo<br />
Department of Mechanical Engineering and Intelligent Systems<br />
The University of Electro-Communications, Japan<br />
• Proposal of the new type of<br />
propulsion method<br />
• Development of prototypes<br />
• Experiments and analysis
Session ThCT3 Continental Parlor 3 Thursday, September 29, <strong>2011</strong>, 14:45–16:15<br />
Marine Systems I<br />
Chair Stephen Barkby, Univ. of Sydney<br />
Co-Chair Terry Huntsberger, Jet Propulsion Lab.<br />
15:35–15:40 ThCT3.5<br />
On Adaptive Underwater Object Detection<br />
David P. Williams<br />
NATO Undersea Research Centre, Italy<br />
• A new, fast algorithm for the detection of<br />
underwater objects in sonar imagery is<br />
proposed.<br />
• The algorithm also detects the presence<br />
of, and estimates the orientation of, sand<br />
ripples.<br />
• The method is adaptively tailored to the<br />
environmental characteristics of the<br />
sensed data that is collected in situ.<br />
• Ways to exploit the findings and adapt<br />
AUV surveys for optimized detection<br />
performance are suggested.<br />
Synthetic aperture sonar image<br />
with man-made objects<br />
and sand ripples.<br />
15:45–16:00 ThCT3.7<br />
Pitch and Roll Control Using Independent<br />
Movable Floats for Small Underwater Robots<br />
Norimitsu Sakagami*, Tomohiro Ueda**<br />
Mizuho Shibata*** and Sadao Kawamura**<br />
*Department of Navigation and Ocean Engineering, Tokai University, Japan<br />
**Department of Robotics, Ritsumeikan University, Japan<br />
***Department of Intelligent Mechanical Engineering, Kinki University, Japan<br />
• We propose a pitch and roll control<br />
system based on a float-shift mechanism<br />
• Two independent movable floats control<br />
the center of buoyancy of a robot<br />
• Numerical results show that the proposed<br />
system contributes to change and keep<br />
the attitude of the robot<br />
• The experimental test was also conducted<br />
in a test tank<br />
Human-sized Underwater Robot<br />
• The experimental results show the equipped with an attitude control system<br />
performance of pitch and roll angle control based on a float-shift mechanism<br />
15:40–15:45 ThCT3.6<br />
Modeling, Simulation, and Performance of a<br />
Synergistically Propelled Ichthyoid<br />
Paul Strefling, Aren Hellum and Ranjan Mukherjee<br />
Department of Mechanical Engineering<br />
Michigan State University,<br />
East Lansing, Michigan, United States<br />
Images of the submersible captured over a period of one second immediately after<br />
starting from rest.<br />
Synergistically Propelled Ichthyoid is a submersible with a fluid-conveying<br />
flexible tail.<br />
Propulsion is achieved by combining jet action and oscillatory tail motion<br />
induced by the jet.<br />
Analytical and numerical methods are employed to solve two models of the<br />
Synergistically Propelled Ichthyoid.<br />
Experiments were performed which qualitatively verify the models.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–210–
Session ThCT4 Continental Ballroom 4 Thursday, September 29, <strong>2011</strong>, 14:45–16:15<br />
Symposium: (Self-)assembly from the Nano to the Macro Scale: State of the Art and Future Directions<br />
Chair Nikolaus Correll, Univ. of Colorado at Boulder<br />
Co-Chair Mark Yim, Univ. of Pennsylvania<br />
14:45–15:00 ThCT4.1*<br />
(Self-)assembly from the nano to the macro scale<br />
Nikolaus Correll, University of Colorado at Boulder<br />
M. Ani Hsieh, Drexel University<br />
Mark Yim, University of Pennsylvania<br />
15:15–15:30 ThCT4.3<br />
Self-Assembly for Maximum Yields Under<br />
Constraints<br />
Michael J. Fox and Jeff S. Shamma<br />
School of Electrical and Computer Engineering, Georgia Institute of Technology,<br />
United Stated of America<br />
• We present an algorithm that, given<br />
any target tree, synthesizes<br />
reversible self-assembly rules that<br />
provide a maximum yield in the<br />
sense of stochastic stability.<br />
• If the reversibility constraint is<br />
relaxed then the same algorithm<br />
can be trivially modified so that it<br />
converges to a maximum yield<br />
almost surely.<br />
• We examine the conservatism of<br />
this technique by considering its<br />
implications for the internal states<br />
of the system.<br />
We use graph grammars to<br />
describe our algorithm.<br />
15:00–15:15 ThCT4.2<br />
Enhanced Directional Self-Assembly<br />
based on Active Recruitment and Guidance<br />
Nithin Mathews1 , Anders Lyhne Christensen2 ,<br />
Rehan O’Grady1 , Philippe Rétornaz3 , Michael Bonani3 ,<br />
Francesco Mondada3 , and Marco Dorigo1 1IRIDIA, Université Libre de Bruxelles, Brussels, Belgium<br />
2Instituto de Telecomunicações, Lisbon, Portugal<br />
3EPFL-LSRO Ecole Polytechnique Fédérale de Lausanne, Switzerland<br />
• Self-assembling robots<br />
• Grow specific morphologies<br />
• We propose a new mechanism<br />
to create directional<br />
connections between robots<br />
• Real-robot experiments<br />
15:30–15:35 ThCT4.4<br />
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<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–211–<br />
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Session ThCT4 Continental Ballroom 4 Thursday, September 29, <strong>2011</strong>, 14:45–16:15<br />
Symposium: (Self-)assembly from the Nano to the Macro Scale: State of the Art and Future Directions<br />
Chair Nikolaus Correll, Univ. of Colorado at Boulder<br />
Co-Chair Mark Yim, Univ. of Pennsylvania<br />
15:35–15:40 ThCT4.5<br />
Self-Assembly of Modular Robots from Finite<br />
Number of Modules Using Graph Grammars<br />
Vijeth Rai and Nikolaus Correll<br />
University of Colorado at Boulder, USA<br />
Anne van Rossum<br />
Almende Research and TiCC, Tilburg University , The Netherlands<br />
• Stochastic Self-Assembly of<br />
modular robots when number of<br />
available building blocks is limited or<br />
exact<br />
• Stochastic approach provides<br />
scalability and robustness, while not<br />
requiring global localization<br />
• Combining assembly with<br />
disassembly leads to 100% yield<br />
• Broadcast communication<br />
accelerates assembly<br />
Hexapod assembly from<br />
“Replicator” modules<br />
15:45–16:00 ThCT4.7<br />
Structure synthesis on-the-fly in a modular robot<br />
Shai Revzen, Mohit Bhoite, Antonio Macasieb, and Mark Yim<br />
School of Engineering and Applied Science<br />
University of Pennsylvania, USA<br />
• Modular robots promise robots created<br />
on-the-fly to suite tasks not known in<br />
advance of deployment<br />
• We demonstrate a remote controlled mobile<br />
synthesizer capable of creating structural<br />
members for linking actuated modules into<br />
arbitrary custom robots<br />
• Material is a foam with x28 expansion;<br />
creating robots larger than synthesizer cart<br />
• Also shown application to hazard disposal<br />
and rescue / urban warfare<br />
• Videos and more details at<br />
http://www.modlabupenn.org/foambot-videos<br />
Synthesizer cart & two mobile<br />
clusters (top), a quadruped and a<br />
snake<br />
15:40–15:45 ThCT4.6<br />
Constrained Task Partitioning for<br />
Distributed Assembly<br />
James Worcester, Josh Rogoff, and M. Ani Hsieh<br />
Scalable Autonomous Systems Lab<br />
Mechanical Engineering & Mechanics<br />
Drexel University, USA<br />
• We present an algorithm to partition 2 and<br />
3-D structures into N separate assembly<br />
tasks that can be executed in parallel.<br />
• The algorithm achieves a partitioning that<br />
minimizes the workload imbalance<br />
between the robots.<br />
• The algorithm consists of three phases: 1)<br />
an initial allocation, 2) workload balance,<br />
and 3) generation of parallel assembly<br />
plans.<br />
• We present simulation and experimental<br />
results.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–212–<br />
Two robots assembling the 3-D<br />
structure (bottom) in parallel.<br />
16:00–16:15 ThCT4.8<br />
Constraint-Aware Coordinated Construction<br />
of Generic Structures<br />
David Stein, T. Ryan Schoen, Daniela Rus<br />
Massachusetts Institute of Technology, USA<br />
• Constraint-aware decentralized approach to construction<br />
• Novel algorithm for partitioning sets of point masses in<br />
• Extension of previous work to conform to physical constraints of<br />
reachability and mid-construction stability<br />
Simulated parallel construction of a model airplane by three robots<br />
conforming to physical and reachability constraints
Session ThCT5 Continental Ballroom 5 Thursday, September 29, <strong>2011</strong>, 14:45–16:15<br />
Symposium: Humanoid Applications<br />
Chair Rüdiger Dillmann, KIT Karlsruher Inst. für Tech.<br />
Co-Chair Shigeki Sugano, Waseda Univ.<br />
14:45–15:00 ThCT5.1*<br />
Semi-Plenary Invited Talk: Humanoid Robotics<br />
Research: Retrospection and Prospects<br />
Tamim Asfour, Karlsruhe Institute of Technology (KIT)<br />
15:15–15:30 ThCT5.3<br />
Actuation Requirements for Hopping and<br />
Running of the Musculoskeletal Robot<br />
BioBiped1<br />
Katayon Radkhah and Oskar von Stryk<br />
Department of Computer Science, Technische Universität Darmstadt, Germany<br />
• Approach to determine the actuation<br />
requirements for given motions for a<br />
electrically driven bipedal robot<br />
• Integration of human-like series-elastic<br />
structures to mimic the main human leg<br />
muscles<br />
• Step-by-step process to compute the<br />
control signals for computer-generated<br />
hopping and human running motions<br />
• Good agreement of the simulated ground<br />
reaction forces with the typical human<br />
single-humped patterns<br />
Rigid kinematic joint-link structure<br />
(left) versus compliant robot<br />
actuated by series elastic<br />
structures (right) performing<br />
hopping and running motions<br />
15:00–15:15 ThCT5.2<br />
Semi-Plenary Invited Talk: Humanoid Robotics<br />
Research: Retrospection and Prospects<br />
Tamim Asfour, Karlsruhe Institute of Technology (KIT)<br />
15:30–15:35 ThCT5.4<br />
Combined Intention, Activity, and Motion<br />
Recognition for a Humanoid Household Robot<br />
Dirk Gehrig1 , Peter Krauthausen1 , Lukas Rybok1 ,<br />
Hildegard Kuehne1 , Uwe D. Hanebeck1 , Tanja Schultz1 ,<br />
and Rainer Stiefelhagen1 2<br />
1 Institute for Anthropomatics,<br />
Karlsruhe Institute of Technology (KIT), Germany<br />
2 Fraunhofer IOSB, Karlsruhe, Germany<br />
• Multi-Level approach to intention,<br />
activity, and motion recognition<br />
• Video-based on-line and real-time<br />
recognition for a humanoid robot<br />
• Key ideas<br />
• Complimentary recognizers<br />
• Extensible model with consistent<br />
uncertainty treatment<br />
• Experimental validation on realworld<br />
data set of complex kitchen<br />
tasks, e.g. Lay Table, Prepare Meal<br />
Robot System<br />
State Estimation<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–213–<br />
Intention<br />
Motion Activity<br />
Human and environment<br />
Model of Human‘s Rationale<br />
Domain<br />
Knowledge<br />
Robot Control<br />
Sensors Actuators
Session ThCT5 Continental Ballroom 5 Thursday, September 29, <strong>2011</strong>, 14:45–16:15<br />
Symposium: Humanoid Applications<br />
Chair Rüdiger Dillmann, KIT Karlsruher Inst. für Tech.<br />
Co-Chair Shigeki Sugano, Waseda Univ.<br />
15:35–15:40 ThCT5.5<br />
An Expected Perception Architecture using 3D<br />
Reconstruction for a Humanoid Robot<br />
N. Moutinho 1 , N. Cauli 2 , E. Falotico 2 , R. Ferreira 1 , J. Gaspar 1 ,<br />
A. Bernardino 1 , J. Santos-Victor 1 , P. Dario 2 , C. Laschi 2<br />
1 Institute for Systems and Robotics, IST/UTL, Portugal<br />
2 The BioRobotics Institute, SSSA, Italy<br />
• Architecture: Anticipatory Visual<br />
Perception (AVP) performs Expected<br />
Perception (EP)<br />
• EP uses proprioception information to<br />
predict visual changes and detect<br />
unexpected visual events<br />
• EP allows saving computational<br />
resources by doing localized 3D<br />
reconstruction instead of full scene<br />
reconstruction<br />
15:45–16:00 ThCT5.7<br />
Development of Whole-body Humanoid "Pneumat-<br />
BS" with Pneumatic Musculoskeletal System<br />
Keita Ogawa<br />
Dept. of Adaptive Machine Systems,Osaka Univ., Japan<br />
Kenichi Narioka and Koh Hosoda<br />
Dept. of Multimedia Engineering,Osaka Univ,. Japan<br />
• We developed whole-body<br />
musculoskeletal humanoid “Pneumat-BS”<br />
• The robot is drivened by pneumatic<br />
artificial muscles<br />
• The robot has whole-body flexibility and<br />
whole-body coordination<br />
• We conducted a standing experiment<br />
using a simple feedback control and<br />
evaluated the stability<br />
• We conducted a walking experiment using<br />
a feedforward controller with human<br />
muscle activation patterns and confirmed<br />
that the robot was able to perform the<br />
dynamic task<br />
A musculoskeletal humanoid<br />
robot "Pneumat-BS"<br />
15:40–15:45 ThCT5.6<br />
Multilayer Control of Skiing Robot<br />
Tadej Petrič, Bojan Nemec, Jan Babič and Leon Žlajpah<br />
Department for automation, biocybernetics and robotics,<br />
Jožef Stefan Institute, Slovenia<br />
• Novel method for ensuring stability of a<br />
skiing robot on a previously unknown ski<br />
slope.<br />
• Multilayer control where the primary task<br />
(stability) is only observed when, the<br />
secondary task (direction) would lead into<br />
instability.<br />
• The effectiveness of the proposed method<br />
is shown in both simulation in the virtual<br />
reality environment as well as on the real<br />
robot, i.e. hardware-in-the-loop.<br />
16:00–16:15 ThCT5.8<br />
Autonomous Climbing of Spiral Staircases<br />
with Humanoids<br />
Stefan Oßwald, Attila Görög,<br />
Armin Hornung, and Maren Bennewitz<br />
Department of Computer Science, University of Freiburg, Germany<br />
• Our Nao humanoid autonomously climbs<br />
up complex staircases<br />
• The robot uses a 2D laser range finder to<br />
globally localize itself in a 3D model<br />
• The pose is refined by comparing<br />
detected edges in monocular camera<br />
images to model edges<br />
• The robot accurately aligns with the next<br />
step and reliably climbs up a spiral<br />
staircase consisting of 10 steps<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–214–
Session ThCT6 Continental Ballroom 6 Thursday, September 29, <strong>2011</strong>, 14:45–16:15<br />
Symposium: Computer Vision for Robotics<br />
Chair Dieter Fox, Univ. of Washington<br />
Co-Chair José Neira, Univ. de Zaragoza<br />
14:45–15:00 ThCT6.1*<br />
Semi-Plenary Invited Talk: Learning to Recognize<br />
Objects Despite Novel Environments and Sensors<br />
Trevor Darrell, UC Berkeley<br />
15:15–15:30 ThCT6.3<br />
RGB-D Object Discovery<br />
Via Multi-Scene Analysis<br />
Evan Herbst, Xiaofeng Ren, Dieter Fox<br />
Computer Science, University of Washington, USA<br />
Intel Research Seattle, Seattle, USA<br />
• Represent a geometric map as static<br />
background + movable objects<br />
• Point-based change detection algorithm<br />
for segmentation and matching<br />
• Improve on 2-D feature-based matching<br />
and on 3-D ICP-based matching<br />
15:00–15:15 ThCT6.2<br />
Semi-Plenary Invited Talk: Learning to Recognize<br />
Objects Despite Novel Environments and Sensors<br />
Trevor Darrell, UC Berkeley<br />
15:30–15:35 ThCT6.4<br />
Online Learning for Automatic<br />
Segmentation of 3D Data<br />
Federico Tombari Luigi Di Stefano<br />
Simone Giardino<br />
DEIS, University of Bologna, Italy<br />
• Semantic segmentation of indoor/outdoor<br />
scenes based on standard classifiers and<br />
a Markov Random Field-based grouping<br />
stage<br />
• Online learning approach: new samples<br />
are continuously selected from incoming<br />
data in an unsupervised way to improve<br />
the learning model<br />
• We experimentally demonstrate<br />
performance improvements with different<br />
classifiers (NN, SVM) as well as 3D data<br />
sources (CAD/synthetic, LIDAR, Kinect)<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–215–<br />
Semantic segmentation of a<br />
complex scene acquired by a<br />
Microsoft Kinect sensor
Session ThCT6 Continental Ballroom 6 Thursday, September 29, <strong>2011</strong>, 14:45–16:15<br />
Symposium: Computer Vision for Robotics<br />
Chair Dieter Fox, Univ. of Washington<br />
Co-Chair José Neira, Univ. de Zaragoza<br />
15:35–15:40 ThCT6.5<br />
Shape-Based Depth Image to 3D Model Matching<br />
and Classification with Inter-View Similarity<br />
Walter Wohlkinger, Markus Vincze<br />
Vision4Robotics Group,<br />
Automation and Control Institute,<br />
Vienna University of Technology, Austria<br />
• 3D Shape based matching of depth<br />
images acquired with Kinect to 3D models<br />
from the Web<br />
• Matching against multiple views<br />
significantly boosts classification rate<br />
• Adaptation and optimization of shape<br />
descriptors enables real-time classification<br />
• Easy and fast learning of new categories<br />
Classification of everyday objects<br />
for robotic applications<br />
15:45–16:00 ThCT6.7<br />
Perception for the Manipulation of Socks<br />
Ping Chuan Wang, Stephen Miller, Mario Fritz,<br />
Trevor Darrell, Pieter Abbeel<br />
University of California, Berkeley, USA<br />
• Provide perceptual tools necessary<br />
for robotic sock manipulation<br />
• Visually infer configuration of<br />
possibly bunched or inside-out socks<br />
• Combine local texture features into<br />
global model<br />
• Visually match pairs of socks<br />
• Implemented on the PR2<br />
15:40–15:45 ThCT6.6<br />
Model for Unfolding Laundry using<br />
Interactive Perception<br />
Bryan Willimon, Stan Birchfield, and Ian Walker<br />
Department of Electrical and Computer Engineering,<br />
Clemson University, USA<br />
• We present an algorithm for automatically<br />
unfolding a piece of clothing.<br />
� Our results show that, at most, the<br />
algorithm flattens out a piece of cloth with<br />
11.1% to 95.6% of the canonical position.<br />
� In this paper, we use peak ridges,<br />
continuity of a surface, and corner locations<br />
in our model to calculate grasp locations<br />
and movement orientations.<br />
16:00–16:15 ThCT6.8<br />
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<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–216–<br />
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Session ThCT7 Continental Parlor 7 Thursday, September 29, <strong>2011</strong>, 14:45–16:15<br />
Exoskeleton Robots & Gait Rehabilitation<br />
Chair Bernard Espiau, INRIA<br />
Co-Chair Constantinos Mavroidis, Northeastern Univ.<br />
14:45–15:00 ThCT7.1<br />
Design and Control of a Robotic Lower<br />
Extremity Exoskeleton for Gait Rehabilitation<br />
Ozer Unluhisarcikli, Maciej Pietrusinski,<br />
Brian Weinberg, Constantinos Mavroidis<br />
Dept. of Mechanical & Industrial Engineering, Northeastern University, USA<br />
Paolo Bonato<br />
Spaulding Rehabilitation Hospital, USA<br />
• Design and control of an active knee<br />
rehabilitation orthotic system (ANdROS) ) is<br />
presented.<br />
• ANdROS is designed as a wearable and d<br />
portable assistive tool for gait<br />
rehabilitation<br />
• A corrective force field that reinforces a<br />
desired gait pattern is applied to the<br />
patient’s impaired leg around the knee<br />
joint via an impedance controlled<br />
exoskeleton.<br />
• The impedance controller is synchronized ed<br />
with the patient’s walking phase which is s<br />
estimated from the kinematic<br />
measurements of the healthy leg.<br />
15:15–15:30 ThCT7.3<br />
Ergonomic considerations for anthropomorphic<br />
wrist exoskeletons: A simulation study on<br />
the effects of joint misalignment<br />
M. Esmaeili, K. Gamage, E. Tan and D. Campolo<br />
School of Mechanical and Aerospace Engineering<br />
Nanyang Technological University, Singapore<br />
• We consider a 2dof model with non-<br />
intersecting joints for both the human wrist<br />
and the exoskeleton<br />
• A viscoelastic attachment between the<br />
human hand and the handle of the exo is<br />
modeled via 4 non-collinear springs to<br />
allow kinematics differences between<br />
human and exoskeleton<br />
• Discomfort is quantified as the amount of<br />
potential energy stored in the deformation<br />
of the viscoelastic attachment<br />
• From the known distribution of joint offset<br />
for humans and the simulated discomfort<br />
function, we compute the optimal joint<br />
offset for the exoskeleton by solving the<br />
‘one-size-fits-all’ problem<br />
2dof kinematic model of the wrist<br />
and exoskeleton with viscoelastic<br />
hand-handle attachment<br />
15:00–15:15 ThCT7.2<br />
Active Air Mat for Comfortable and Easy to Wear<br />
a Forearm Support System<br />
Yasuhisa Hasegawa, Munenori Tayama, Takefumi Saito and<br />
Yoshiyuki Sankai<br />
Department of Intelligent Interaction Technologies, University of Tsukuba,<br />
Japan<br />
• An active air mat installed in interface<br />
parts of the exoskeleton improves<br />
comfortableness and easiness to wear<br />
an exoskeleton on a forearm.<br />
• One of the most important effects is to<br />
minimize constriction of blood flow by<br />
changing contacting areas with a<br />
human arm from the periphery to the<br />
trunk.<br />
• The active air mat is evaluated from the<br />
viewpoint of pressure distribution, blood<br />
flow, wearing time, releasing time,<br />
body-holding rigidity, and temperature<br />
and humidity of a user’s skin in resting<br />
and working states.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–217–<br />
Forearm support system and<br />
components of active air mat<br />
15:30–15:35 ThCT7.4<br />
The Development and Testing of a Human<br />
Machine Interface for a Mobile Medical<br />
Exoskeleton<br />
Katherine Strausser and H. Kazerooni<br />
Mechanical Engineering, University of California, Berkeley, USA<br />
• The Human Machine Interface (HMI)<br />
allows a user to control a mobile<br />
exoskeleton independently.<br />
• The HMI is implemented on eLEGS, a<br />
mobile exoskeleton for Spinal Cord Injury<br />
patients.<br />
• This method uses natural gestures to<br />
operate the machine easily and naturally.<br />
The Human Machine Interface is<br />
implemented on eLEGS.
Session ThCT7 Continental Parlor 7 Thursday, September 29, <strong>2011</strong>, 14:45–16:15<br />
Exoskeleton Robots & Gait Rehabilitation<br />
Chair Bernard Espiau, INRIA<br />
Co-Chair Constantinos Mavroidis, Northeastern Univ.<br />
15:35–15:40 ThCT7.5<br />
A Self-Adjusting Knee Exoskeleton<br />
for Robot-Assisted Treatment of Knee Injuries<br />
Mehmet Alper Ergin and Volkan Patoglu<br />
Faculty of Engineering and Natural Sciences<br />
Istanbul, Turkey<br />
• A novel active device for robot-assisted<br />
rehabilitation that accommodates<br />
transitional movements of the knee joint<br />
as well as its rotation<br />
• 3 Degrees of Freedom device with 2<br />
translations and one rotation<br />
• Enables both passive and active<br />
alignment of the knee joint<br />
• Impedance controller design<br />
• Characterization and tracking<br />
performance of the device are presented<br />
15:40–15:45 ThCT7.6<br />
Alterations in Body Weight Shifting during<br />
Robotic Assisted Gait Rehabilitation<br />
using NaTUre-gaits<br />
H. B. Lim, Trieu Phat Luu, K. H. Hoon, Xingda Qu, and K. H. Low<br />
School of Mechanical and Aerospace Engineering,<br />
Nanyang Technological University, Singapore<br />
Adela Tow<br />
Department of Rehabilitation Medicine, Tan Tock Seng Hospital, Singapore<br />
• Pelvic assistance for robotic assisted gait<br />
rehabilitation<br />
• Investigation of body weight shifting of<br />
human subject during gait rehabilitation<br />
with NaTUre-gaits<br />
• Study of the Center of Pressure (COP)<br />
trajectory during robotic assisted gait<br />
rehabilitation<br />
• In this study, COP trajectory is found to be<br />
affected by robotic system<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–218–<br />
Robotic Arm<br />
(right)<br />
Contact surface<br />
Robotic Arm<br />
(left)<br />
Pelvic assistance mechanism
Session ThCT8 Continental Parlor 8 Thursday, September 29, <strong>2011</strong>, 14:45–16:15<br />
Aerial Robots: Navigation, Tracking & Landing<br />
Chair Stefan Hrabar, CSIRO ICT Centre<br />
Co-Chair Mark Minor, Univ. of Utah<br />
14:45–15:00 ThCT8.1<br />
Chasing a moving target from a flying UAV<br />
Céline Teulière and Laurent Eck<br />
Interactive Robotics Unit, CEA-LIST, France<br />
Eric Marchand<br />
Lagadic Unit, Université de Rennes 1, France<br />
• Robust visual tracking system:<br />
• Multiple kernel tracking in particle<br />
filtering framework<br />
• Initialisation/reinitialisation system<br />
• Hierarchical control scheme for UAV<br />
position and yaw control<br />
• Real-time chasing experiments robust to<br />
noisy video transmission, some complete<br />
occlusions, and scale and orientation<br />
changes.<br />
UAV<br />
Internal view<br />
15:15–15:30 ThCT8.3<br />
Autonomous Miniature Blimp Navigation<br />
with Online Motion Planning and Re-planning<br />
Jörg Müller, Norman Kohler, and Wolfram Burgard<br />
Department of Computer Science, University of Freiburg, Germany<br />
• Approach to autonomous navigation<br />
for miniature indoor blimps in mapped<br />
environments<br />
• Multi-stage planning algorithm for<br />
strongly goal-directed tree-based<br />
kinodynamic motion planning<br />
• A* path generation in a 4-dimensional<br />
subspace and path-guided sampling in<br />
the full 12-dimensional state space<br />
• Navigation on a round-trip using<br />
motion capture state estimation and an<br />
LQR controller<br />
• Implemented and evaluated on a real<br />
robotic indoor blimp<br />
Our indoor blimp navigating<br />
autonomously through a narrow<br />
passage between two obstacles.<br />
15:00–15:15 ThCT8.2<br />
A Fast and Adaptive Method for Estimating UAV<br />
Attitude from the Visual Horizon<br />
Richard J. D. Moore, Saul Thurrowgood, Daniel Bland,<br />
Dean Soccol & Mandyam V. Srinivasan<br />
The Queensland Brain Institute, The University of Queensland, Australia<br />
• A classifier is used to<br />
segment wide FoV images<br />
into sky and ground<br />
regions<br />
• The classifier is trained<br />
continuously allowing onthe-fly<br />
initialisation and the<br />
ability to adapt to changing<br />
environments<br />
• Matching the classified<br />
images against a precomputed<br />
database allows<br />
estimation of aircraft<br />
attitude to within 1.5 o in<br />
2ms @ 1GHz<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–219–<br />
An illustration of the attitude estimation process. The<br />
input image (a) is classified into sky/ground regions<br />
(b), and then the row and column averages are<br />
matched against a pre-computed database (c) to<br />
retrieve the attitude, which is used to generate a<br />
training mask (d) for the classified.<br />
15:30–15:35 ThCT8.4<br />
Efficient Target Geolocation by<br />
Highly Uncertain Small Air Vehicles<br />
Ben Grocholsky, Michael Dille and Stephen Nuske<br />
Robotics Institute, Carnegie Mellon University, USA<br />
• Small UAVs have limited sensing and<br />
typically exhibit significant heading<br />
error resulting in poor target<br />
geolocation accuracy.<br />
• An estimation method is sought that<br />
can process complex nonlinear<br />
uncertainty using lightweight<br />
computing hardware.<br />
• We present an efficient parametric<br />
representation for nonlinear<br />
uncertainty estimates with significantly<br />
lower complexity than existing<br />
methods.<br />
• Visual target geolocation results are<br />
demonstrated on a small UAV platform.<br />
A typical small UAV platform and target<br />
location observation uncertainty
Session ThCT8 Continental Parlor 8 Thursday, September 29, <strong>2011</strong>, 14:45–16:15<br />
Aerial Robots: Navigation, Tracking & Landing<br />
Chair Stefan Hrabar, CSIRO ICT Centre<br />
Co-Chair Mark Minor, Univ. of Utah<br />
15:35–15:40 ThCT8.5<br />
Beyond Visual Range Obstacle Avoidance and<br />
Infrastructure Inspection by an<br />
Autonomous Helicopter<br />
Torsten Merz and Farid Kendoul<br />
ICT Centre, CSIRO, Australia<br />
• LIDAR-based perception and guidance<br />
system for autonomous helicopters<br />
operating at low altitude in unknown<br />
environments<br />
• Efficient methods for terrain following,<br />
goal-oriented obstacle avoidance, and<br />
close-range inspection<br />
• Implemented on board the CSIRO<br />
unmanned helicopter and flight tested in<br />
several different mission scenarios<br />
• Successfully completed 37 inspection<br />
missions including 2 missions beyond<br />
visual range without a backup pilot<br />
15:45–16:00 ThCT8.7<br />
Reactive Obstacle Avoidance for<br />
Rotorcraft UAVs<br />
Stefan Hrabar<br />
Australian Research Centre for Aerospace Automation (ARCAA) / CSIRO,<br />
Brisbane, Australia<br />
• A goal-oriented reactive avoidance<br />
technique suitable for Rotorcraft UAVs.<br />
• Uses a 3D occupancy map<br />
representation of sensed obstacles.<br />
• Cylindrical Safety Volume is tested for<br />
obstacles ahead of the UAV.<br />
• Efficient collision checking within the<br />
cylinder achieved by approximate<br />
nearest neighbor search in a Bkd-tree<br />
representation of occupied voxels.<br />
• Generates an ‘Escape Point’ waypoint<br />
providing a path around the obstacles.<br />
• Performs in real-time using on-board<br />
processing.<br />
• Successful obstacle avoidance results<br />
presented with an Unmanned<br />
Helicopter<br />
The CSIRO Unmanned Helicopter fitted<br />
with stereo and lidar sensors for<br />
obstacle avoidance.<br />
15:40–15:45 ThCT8.6<br />
Coordinated Landing of a Quadrotor on a Skid-<br />
Steered Ground Vehicle in the Presence of Time<br />
Delays<br />
John Daly, Yan Ma, and Steven Waslander<br />
Department of Mechanical and Mechatronics Engineering, University of<br />
Waterloo, Canada<br />
• We present a control technique to<br />
autonomously land a quadrotor on a<br />
moving ground vehicle<br />
• Feedback linearizing controllers are used<br />
for the quadrotor and ground vehicle<br />
• A joint controller to stabilize the difference<br />
in position and the velocity is designed to<br />
accomplish a rendezvous<br />
• Time delay stability analysis is performed<br />
to determine delay margin of closed loop<br />
system<br />
• Results verified through numerical<br />
simulation<br />
16:00–16:15 ThCT8.8<br />
Avian-Inspired Passive Perching Mechanism for<br />
Robotic Rotorcraft<br />
Courtney E. Doyle, Justin J. Bird, Taylor A. Isom,<br />
C. Jerald Johnson, Jason C. Kallman, Jason A. Simpson,<br />
Raymond J. King, Jake J. Abbott, and Mark. A. Minor<br />
Mechanical Engineering, University of Utah, United States<br />
• Mechanism inspired by songbirds<br />
to enable perching on cylindrical<br />
surfaces.<br />
• Compliant, underactuated, tendondriven<br />
gripper provides closure<br />
around perch.<br />
• Leg mechanism with compliant<br />
joints converts rotorcraft weight<br />
into actuation force for gripper.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–220–<br />
Passive mechanism for perching on cylindrical<br />
surfaces. LEFT: Mechanism in relaxed state;<br />
CENTER: Mechanism perching on 49mm rod;<br />
RIGHT: Mechanism perching on 33mm rod.
Session ThCT9 Continental Parlor 9 Thursday, September 29, <strong>2011</strong>, 14:45–16:15<br />
Swarms and Flocks<br />
Chair Dylan Shell, Texas A&M Univ.<br />
Co-Chair Radhika Nagpal, Harvard Univ.<br />
14:45–15:00 ThCT9.1<br />
Communication assisted navigation in robotic<br />
swarms: self-organization and cooperation<br />
F. Ducatelle, G. A. Di Caro and L. M. Gambardella<br />
IDSIA, USI/SUPSI, Switzerland<br />
C. Pinciroli<br />
IRIDIA, ULB, Belgium<br />
F. Mondada<br />
LSRO, EPFL, Switzerland<br />
• Robots guide each other’s<br />
navigation by passing wireless<br />
messages over a range and<br />
bearing communication system<br />
• The algorithm supports efficient<br />
navigation while being robust to<br />
robot failures<br />
• Multiple robots moving between<br />
two targets can self-organize into<br />
a dynamic chain (see figure)<br />
• This self-organization supports<br />
cooperation and improves<br />
navigation efficiency<br />
15:15–15:30 ThCT9.3<br />
The Distributed Co-Evolution<br />
of an Embodied Simulator and Controller<br />
for Swarm Robot Behaviours<br />
Paul O'Dowd, Alan Winfield and Matthew Studley<br />
Bristol Robotics Laboratory, University of the West of England, UK<br />
• Each robot evolves a controller solution<br />
through embodied simulator<br />
• Real world robot performance metric<br />
used to indirectly measure environment<br />
correspondence of the embodied<br />
simulator<br />
• Real world robot performance metric<br />
used to collectively and competitively<br />
evolve the environment model<br />
• Swarm of robots can adapt to<br />
environmental change<br />
Five processes on board each<br />
robot to co-evolve both robot<br />
controller and embodied<br />
simulator.<br />
15:00–15:15 ThCT9.2<br />
Effect of Sensor and Actuator Quality<br />
on Robot Swarm Algorithm Performance<br />
Nicholas Hoff, Robert Wood, and Radhika Nagpal<br />
Computer Science, Harvard University, United States<br />
• The performance of a swarm depends on<br />
the hardware quality of the robots.<br />
• We vary the quality of the sensors and<br />
actuators, and measure the effect on<br />
algorithm performance.<br />
• Large amounts of hardware error are<br />
required before performance appreciably<br />
decreases.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–221–<br />
Varying number of communication<br />
receivers.<br />
15:30–15:35 ThCT9.4<br />
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Session ThCT9 Continental Parlor 9 Thursday, September 29, <strong>2011</strong>, 14:45–16:15<br />
Swarms and Flocks<br />
Chair Dylan Shell, Texas A&M Univ.<br />
Co-Chair Radhika Nagpal, Harvard Univ.<br />
15:35–15:40 ThCT9.5<br />
Gas Source Localization in Indoor Environments<br />
using Multiple Inexpensive Robots and Stigmergy<br />
Maurizio Di Rocco<br />
DIA, Università degli Studi Roma Tre, Italy<br />
Matteo Reggente, Alessandro Saffiotti<br />
AASS, Orebro University, Sweden<br />
• Gas source localization is difficult, e.g.,<br />
because gas concentration does not have a<br />
smooth gradient due to turbulent diffusion<br />
• We generate an artificial gradient that<br />
combines gas concentration and distance,<br />
and leads to likely location of gas source<br />
• The gradient is stored in the environment<br />
(stigmergy) using a grid of RFID tags<br />
• Through stigmergy, robots only need<br />
minimal sensing and computing resources<br />
• Through stigmergy, cooperation among<br />
multiple robots emerges naturally<br />
Top: experimental set-up.<br />
Bottom: computed gas<br />
concentration map.<br />
15:45–16:00 ThCT9.7<br />
Human Swarm Modeling in Exhibition Space<br />
and Space Design<br />
Masafumi Okada, Yuichi Motegi and Ko Yamamoto<br />
Department of Mechanical Sciences and Engineering,<br />
Tokyo TECH, Japan<br />
• The purposes of this research are layout<br />
optimization of exhibits to reduce<br />
congestion and amenity space design.<br />
• Human behavior including individual<br />
characteristics is modeled by twodimensional<br />
vector field and multidimensional<br />
extension.<br />
• A Layout of exhibits is optimized based on<br />
the proposed model by minimizing<br />
collision avoidance of individuals.<br />
• The proposed optimization method is<br />
verified by simulations and experiments<br />
using swarm robots.<br />
The experiment verification using<br />
swarm robots<br />
15:40–15:45 ThCT9.6<br />
Reynolds flocking in reality with fixed-wing robots:<br />
communication range vs. maximum turning rate<br />
Sabine Hauert, Severin Leven, Maja Varga, Jean-Christophe<br />
Zufferey and Dario Floreano<br />
Laboratory of Intelligent Systems, EPFL, Switerland<br />
Fabio Ruini, Angelo Cangelosi<br />
Centre for Robotics and Neural Systems, University of Plymouth, UK<br />
• Demonstrated Reynolds flocking<br />
using swarms of 10 flying robots.<br />
• Results show tradeoff between<br />
communication range and flight<br />
dynamics on flocking performance.<br />
• Developed infrastructure enables<br />
aerial swarm experiments with a<br />
single operator.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–222–<br />
Swarm of flying robots used to<br />
perform Reynolds flocking.<br />
16:00–16:15 ThCT9.8<br />
ARGoS: a Modular, Multi-Engine Simulator for<br />
Heterogeneous Swarm Robotics<br />
Carlo Pinciroli, Vito Trianni, Rehan O’Grady, Giovanni Pini,<br />
Arne Brutschy, Manuele Brambilla, Nithin Mathews,<br />
Eliseo Ferrante and Marco Dorigo<br />
IRIDIA, CoDE, Université Libre de Bruxelles, Belgium<br />
Gianni di Caro, Frederick Ducatelle and Luca Maria Gambardella<br />
IDSIA, USI-SUPSI, Switzerland<br />
Timothy Stirling<br />
LIS, École Polythechnique Fédérale de Lausanne, Switzerland<br />
Álvaro Gutiérrez<br />
ETSI Telecomunicación, Universidad Politécnica de Madrid, Spain<br />
• A novel, open source multi-robot<br />
simulator targeted for large<br />
heterogeneous swarms of robots<br />
• ARGoS’ modular architecture is<br />
designed for scalability and<br />
extensibility<br />
• A unique feature of ARGoS is that it is<br />
possible to divide the space into subspaces<br />
managed by different physics<br />
engines<br />
• ARGoS’ architecture is multi-threaded<br />
and free of race conditions<br />
Results show that ARGoS can<br />
simulate 10,000 simple robots<br />
40% faster than real time
Session ThDT1 Continental Ballroom Thursday, September 29, <strong>2011</strong>, 16:45–17:45<br />
Forum: On Robotics Conferences: A Town Hall Meeting<br />
Chair Peter Corke, QUT<br />
Co-Chair<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–223–
Session ThDT3 Continental Parlor 3 Thursday, September 29, <strong>2011</strong>, 16:45–17:45<br />
Marine Systems II<br />
Chair Ioannis Rekleitis, McGill Univ.<br />
Co-Chair Shugen Ma, Ritsumeikan Univ.<br />
16:45–17:00 ThDT3.1<br />
ePaddle Mechanism: Towards The Development of<br />
A Versatile Amphibious Locomotion Mechanism<br />
Yi Sun and Shugen Ma<br />
Department of Robotics, Ritsumeikan University, Japan<br />
• An eccentric paddle locomotion mechanism (ePaddle) for amphibious<br />
tasks is proposed;<br />
• Three terrestrial and two aquatic gaits are introduced, including wheeled<br />
rolling, legged walking, hybrid wheeled, rotational paddling and oscillating<br />
paddling gaits;<br />
• Forward and inverse kinematic models for above gaits are derived;<br />
• Prototype design, experiments and simulations are presented to verify<br />
the idea of ePaddle mechanism.<br />
17:15–17:30 ThDT3.3<br />
MARE: Marine Autonomous Robotic Explorer<br />
Yogesh Girdhar, Anqi Xu, Bir Bikram Dey, Malika Meghjani,<br />
Florian Shkurti, Ioannis Rekleitis, and Gregory Dudek<br />
School of Computer Science, McGill University, Canada<br />
• An autonomous airboat robot suitable for<br />
exploration-oriented tasks in both closed<br />
and open water environments<br />
• A coverage-driven exploration strategy for<br />
systematically exploring a bounded region<br />
with arbitrary obstacle based on a<br />
complete and optimal coverage algorithm<br />
• A surprise-driven exploration strategy for<br />
steering the robot on a path that might<br />
lead to potentially surprising observations<br />
• A programming tool and accompanied<br />
software management system for<br />
respectively specifying and regulating<br />
concurrent robot behaviors and activities<br />
Our MARE robot being deployed<br />
to visually explore a coastal coral<br />
reef region autonomously<br />
17:00–17:15 ThDT3.2<br />
Generic Architecture for Multi-AUV Cooperation<br />
Based on a Multi-Agent Reactive<br />
Organizational Approach<br />
Nicolas Carlési, Fabien Michel,<br />
Bruno Jouvencel and Jacques Ferber<br />
LIRMM, Univ. Montpellier 2, France<br />
• Objective:<br />
Design an architecture for<br />
specifying AUV flotillas at a high<br />
level of abstraction, regardless of<br />
mission context and AUVs<br />
characteristics and skills.<br />
• Proposed approach:<br />
- A organizational model is used to<br />
ease and regulate interactions<br />
between heterogeneous AUVs.<br />
- A behavioral reactive approach is<br />
used to limit communication.<br />
• Simulation:<br />
Heterogeneous AUVs cooperate<br />
for exploring and mapping an area.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–224–<br />
REMORAS: a three-layer control architecture<br />
17:30–17:45 ThDT3.4<br />
State Estimation of an Underwater Robot Using<br />
Visual and Inertial Information<br />
F. Shkurti, I. Rekleitis, M. Scaccia, and G. Dudek<br />
School of Computer Science, McGill University, Canada<br />
• Single camera, depth sensor, and<br />
IMU used for underwater robot<br />
state estimation<br />
• Challenging lighting conditions<br />
• Unpredictable motion<br />
• EKF used to estimate the pose and<br />
velocities of the vehicle<br />
• Experimental validation using data<br />
sets collected over coral reefs<br />
Aqua swimming over the coral reef
Session ThDT4 Continental Parlor 2 Thursday, September 29, <strong>2011</strong>, 16:45–17:45<br />
Novel System Designs: Locomotion<br />
Chair François Pierrot, CNRS - LIRMM<br />
Co-Chair Pei-Chun Lin, National Taiwan Univ.<br />
16:45–17:00 ThDT4.1<br />
Shape and Location Design of Supporting Legs<br />
for a New Water Strider Robot<br />
Wu Licheng 1 , Wang Shuhui 2 , Marco Ceccarelli 3 ,<br />
Yuan Haiwen 2 and Yang Guosheng 1<br />
1 School of Information Engineering, Minzu University of China, China<br />
2 School of Automation Science & Electrical Engineering, Beihang Univ., China<br />
3 Laboratory of Robotics and Mechatronics, University of Cassino, Italy<br />
• Water Strider Robot<br />
(WSR) stands and moves<br />
on water by surface<br />
tension.<br />
• Shape and location of<br />
supporting legs for<br />
enhancing the lift force is<br />
critical to a WSR<br />
A photo of a Water Dancer II-a is standing on the water.<br />
• A supporting leg is approached as Euler-Bernoulli elastic curved beam<br />
and a method for designing its optimal shape is proposed.<br />
• A method for properly locating supporting legs on the robot body is<br />
proposed by analyzing the influence of leg location to lift force and the<br />
relationship of supporting legs’ with robot’s roll-resistant capability.<br />
• The proposed methods are used and validated on a developed Water<br />
Dance II-a as shown in the figure.<br />
17:15–17:30 ThDT4.3<br />
HAMR 3 : An Autonomous 1.7g Ambulatory Robot<br />
Andrew T. Baisch, Michael Karpelson, and Robert J. Wood<br />
School of Engineering and Applied Sciences, and the Wyss Institute for<br />
Biologically Inspired Engineering, Harvard University, USA<br />
Christian Heimlich<br />
Ecole Polytechnique Fédérale de Lausanne, Switzerland<br />
• Presentation of a 1.7g, 4.7cm<br />
hexapod robot as a platform for<br />
research on centimeter-scale<br />
walking systems.<br />
• Results during autonomous<br />
locomotion.<br />
• Description of onboard high<br />
voltage electronics to drive<br />
piezoelectric actuators.<br />
The third generation Harvard<br />
Ambulatory MicroRobot, HAMR 3<br />
17:00–17:15 ThDT4.2<br />
Locomotion Approach of REMORA: a REonfigurable<br />
MObile Robot for manufacturing Applications<br />
Hai Yang and Cédric Baradat<br />
Tecnalia, France<br />
Sébastien Krut and François Pierrot<br />
LIRMM, CNRS-Université de Montpellier II, France<br />
• A quadruped uses eight actuators for<br />
achieving manufacturing and<br />
locomotion tasks in large workspaces<br />
• There are lockers on some of the<br />
passive joints and clamping devices at<br />
the end of its limbs<br />
• The locking strategy of the robot is<br />
formulated as an optimization problem<br />
• The robot can achieve locomotion with<br />
respect to some extra constrains in the<br />
workspaces<br />
17:30–17:45 ThDT4.4<br />
Experimental Dynamics of Wing Assisted<br />
Running for a Bipedal Ornithopter<br />
Kevin Peterson and Ronald Fearing<br />
EECS, University of California, Berkeley, USA<br />
• The wing assisted dynamics of quasi-static<br />
and dynamic gaits are examined by using an<br />
onboard accelerometer.<br />
• We compare accelerations for bipedal<br />
running with and without wings and find<br />
flapping wings result in a smoother, faster<br />
dynamic gait.<br />
• With wings, peak accelerations are ±22<br />
m/s 2 and the speed is 1.5 m/s; without, the<br />
accelerations peak at ±47 m/s 2 with speed<br />
0.35 m/s.<br />
• Efficiency of running with and without wing<br />
assistance measured to be 53.1 J/kg-m and<br />
89.7 J/kg-m.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–225–<br />
The Bipedal Ornithopter for<br />
Locomotion Transitioning (BOLT)<br />
is a hybrid flying/crawling robot<br />
used to study the interactions<br />
between legs and wings, and<br />
terrestrial/aerial transitions.
Session ThDT5 Continental Parlor 1 Thursday, September 29, <strong>2011</strong>, 16:45–17:45<br />
Climbing & Brachiation<br />
Chair Fumiya Iida, ETH Zurich<br />
Co-Chair Motoji Yamamoto, Kyushu Univ.<br />
16:45–17:00 ThDT5.1<br />
CLASH: Climbing Loose Cloth<br />
Paul Birkmeyer, Ronald S. Fearing<br />
Department of Electrical Engineering and Computer Science<br />
University of California, Berkeley, United States<br />
Andrew G. Gillies<br />
Department of Mechanical Engineering<br />
University of California, Berkeley, United States<br />
• 10 cm, 15 gram hexapedal robot capable<br />
of climbing vertical loose cloth at 15 cm/s<br />
• Single drive actuator<br />
• Passive foot design allows easy<br />
engagement, disengagement of surface<br />
• Novel design manages body dynamics to<br />
improve climbing performance<br />
17:15–17:30 ThDT5.3<br />
Scaling Walls:<br />
Applying Dry Adhesives to the Real World<br />
Elliot W. Hawkes, John Ulmen, Noe Esparza,<br />
and Mark R. Cutkosky<br />
Department of Mechanical Engineering, Design Division,<br />
Stanford University, United States<br />
• We present two foot mechanisms<br />
inspired by the structures in gecko<br />
toes<br />
• Both allow almost 100% contact even<br />
with significant initial misalignment<br />
• The tendon-inspired design with large<br />
patches achieves adhesion equal to<br />
small aligned test samples<br />
• Scaling may allow a robot as large as<br />
100kg to climb<br />
Stickybot III climbing a painted<br />
metal door with a tendon-based<br />
foot design.<br />
17:00–17:15 ThDT5.2<br />
Shaping Energetically Efficient Brachiation<br />
Motion for a 24-DOF Gorilla Robot<br />
Stepan Pchelkin 1 Anton Shiriaev 1 Uwe Mettin 1 Leonid Freidovich 2<br />
1 Norwegian University of Science and Technology, Norway<br />
2 Umeå University, Sweden<br />
Tadayoshi Aoyama Zhiguo Lu Toshio Fukuda<br />
Nagoya University, Japan<br />
• Problem: Trajectory generation for a<br />
24-DOF gorilla robot with passive wrist<br />
• We present a numerically tractable<br />
procedure with iterative heuristics and<br />
rigorous tools of analytical mechanics<br />
to minimize the energetic cost of<br />
transport<br />
• (step 1) Blind Numerical Search with<br />
Different Assumptions on Geometrical<br />
Constraints<br />
• (step 2) Relaxed Numerical Search<br />
with Gradient and Hessian of Cost<br />
Function<br />
17:30–17:45 ThDT5.4<br />
A Climbing Robot Based on Hot Melt Adhesion<br />
Marc Osswald and Fumiya Iida<br />
Bio-Inspired Robotics Lab, ETH Zurich, Switzerland<br />
• Hot Melt Adhesion can provide up to<br />
150N/cm 2 shear force for climbing robot<br />
locomotion<br />
• HMA can be used to climb on different<br />
materials in the environment<br />
• Investigation of HMA thermoplasticity and<br />
controllability for climbing robot locomotion<br />
• Design and control of a climbing robot<br />
platform (body size 16cm and weight<br />
0.6kg)<br />
• Demonstration and analysis of climbing<br />
robot locomotion at 0.17m/min.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–226–<br />
The Climbing Robot Platform
Session ThDT6 Continental Parlor 9 Thursday, September 29, <strong>2011</strong>, 16:45–17:45<br />
Novel System Designs: Sensing and Manipulation<br />
Chair Raj Madhavan, UMD-CP/NIST<br />
Co-Chair Chang-Soo Han, Hanyang Univ.<br />
16:45–17:00 ThDT6.1<br />
Quadrocopter Ball Juggling<br />
Mark Müller, Sergei Lupashin and Raffaello D’Andrea<br />
Institute for Dynamic Systems and Control, ETH Zurich, Switzerland<br />
• Quadrocopters with attached rackets hitting a table<br />
tennis ball<br />
• Operating in a 10x10x10m motion capture volume<br />
• Trajectory generation, ball state estimation<br />
• Adaptation by estimation of ball’s drag coefficient ,<br />
racket characteristics and aiming bias<br />
• Three modes of operation:<br />
• Solo quadrocopter play<br />
• Quadrocopter/human play<br />
• Cooperative two quadrocopter play<br />
17:15–17:30 ThDT6.3<br />
Compact Design of a Torque Sensor using<br />
Optical Technique and its Fabrication for<br />
Wearable and Quadruped Robots<br />
Sarmad Shams, Dongik Shin, Ji Yeong Lee, Kyoosik Shin and<br />
Chang-Soo Han<br />
Dept. of Mechanical Engineering, Hanyang Univ., South Korea<br />
Jungsoo Han<br />
Dept. of Mechanical Systems Engineering, Hansung University, South Korea<br />
• We have developed a compact torque<br />
sensor for a wearable robot: dimension of<br />
Ø80�11 mm and range of 100 Nm.<br />
• The deflection is measured by a photointerrupter<br />
instead of strain gages.<br />
• A monolithic flexure mechanism made of<br />
titanium was designed verified by FEM<br />
(deflection and stress).<br />
• The performance such as linearity and<br />
repeatability was evaluated by<br />
experiments<br />
Photo-interrupter-based<br />
torque sensor<br />
17:00–17:15 ThDT6.2<br />
2-DOF Contactless Distributed Manipulation<br />
Using Superposition of Induced Air Flows<br />
Anne Delettre, Guillaume J. Laurent and Nadine Le-Fort Piat<br />
Femto-ST Institute, UFC-ENSMM-UTBM-CNRS, Besançon, France<br />
• Contactless conveyor<br />
• Original aerodynamic<br />
traction principle<br />
• Induced air flow modeling<br />
• Two dimensions position<br />
control and trajectory<br />
tracking<br />
17:30–17:45 ThDT6.4<br />
Development of a High Efficiency and High Reliable<br />
Glass Cleaning Robot with a Dirt Detect Sensor<br />
Yoshio Katsuki, Takeshi Ikeda and Motoji Yamamoto<br />
Faculty of Engineering, Kyushu University, Japan<br />
• This research proposes a trajectory tracking<br />
control method for a glass cleaning robot.<br />
• The robot can follow a desired path for glass<br />
cleaning with the proposed control method.<br />
• This research proposes a dirt detect sensor<br />
for a glass cleaning robot, too.<br />
• A motion control method to guarantee<br />
complete cleaning with the dirt detect<br />
sensor is proposed.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–227–<br />
Glass cleaning robot with a dirt detect<br />
sensor on a partly dirty glass
Session ThDT7 Continental Parlor 7 Thursday, September 29, <strong>2011</strong>, 16:45–17:45<br />
Medical Robotics: Motion Planning & State Estimation<br />
Chair Michael Zinn, Univ. of Wisconsin - Madison<br />
Co-Chair Allison M. Okamura, Stanford Univ.<br />
16:45–17:00 ThDT7.1<br />
Modeling Approach for Continuum Robotic<br />
Manipulators: Effects of Nonlinear Internal<br />
Device Friction<br />
Jinwoo Jung, Ryan Penning, Nicola Ferrier, and Michael Zinn,<br />
Department of Mechanical Engineering<br />
University of Wisconsin – Madison, USA<br />
• A new continuum robotics lumpedparameter<br />
modeling approach is<br />
presented<br />
• Effectively models internal friction from<br />
sources such as sliding control tendons<br />
and relative telescoping motion<br />
• Rresults demonstrate advantage of<br />
new approach as compared to<br />
modeling with constant curvature<br />
assumption<br />
• Comparison with experimental results<br />
demonstrates efficacy of approach<br />
Nonlinear internal friction can<br />
significantly distort tip trajectory<br />
17:15–17:30 ThDT7.3<br />
Motion Planning for Concentric Tube Robots<br />
Using Mechanics-based Models<br />
Luis G. Torres and Ron Alterovitz<br />
Deptartment of Computer Science<br />
University of North Carolina at Chapel Hill, USA<br />
• New motion planner for concentric tube<br />
robots for minimally invasive surgery<br />
• Uses mechanics-based model of robot<br />
shape and considers uncertainty in<br />
actuation and kinematics<br />
• Minimizes estimated probability of collision<br />
with anatomical obstacles<br />
• Extends sampling-based Rapidly-<br />
Exploring Roadmap with goal bias for fast<br />
performance<br />
• Apply to challenging skull base<br />
neurosurgery procedure<br />
Concentric tube robot executing<br />
motion plan to reach pituitary<br />
gland through nasal cavity<br />
17:00–17:15 ThDT7.2<br />
Inequality Constrained Kalman Filtering for the<br />
Localization and Registration of a Surgical Robot<br />
Stephen Tully<br />
Electrical and Computer Engineering, Carnegie Mellon University, USA<br />
George Kantor and Howie Choset<br />
Robotics Institute, Carnegie Mellon University, USA<br />
• For image-guided surgery, preoperative<br />
surface models provide geometric<br />
constraints on the robot path<br />
• We introduce an uncertainty projection<br />
method for inequality constrained Kalman<br />
filtering<br />
• Nonlinear constraints are handled with a<br />
pseudo-measurement update process<br />
• We demonstrate localization and<br />
registration of the HARP surgical probe<br />
using constrained filtering<br />
• Registration parameters are automatically<br />
updated during image-guidance<br />
experiments<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–228–<br />
Correcting snake robot localization<br />
for image-guided surgery<br />
17:30–17:45 ThDT7.4<br />
State Estimation and Feedforward Tremor Suppression<br />
for a Handheld Micromanipulator with a Kalman Filter<br />
Brian C. Becker, Robert A. MacLachlan, Cameron N. Riviere<br />
Robotics Institute, Carnegie Mellon University, USA<br />
• Active compensation of tremor in fully<br />
handheld micrompanipulators is limited by<br />
latencies in actuation<br />
• By modeling tremor, more effective tremor<br />
cancellation is possible by anticipating and<br />
correcting for future hand motion<br />
• A Kalman filter estimates state and<br />
models hand motion, providing tremor<br />
predictions for a feedforward model<br />
• Incorporating a feedforward model into the<br />
control loop of our Micron manipulator<br />
reduces tremor over 50% compared to the<br />
existing feedback-only control method<br />
Micron and experimental setup
Session ThDT8 Continental Parlor 8 Thursday, September 29, <strong>2011</strong>, 16:45–17:45<br />
Aerial Robots<br />
Chair Randy Beard, Brigham Young Univ.<br />
Co-Chair Mac Schwager, Univ. of Pennsylvania<br />
16:45–17:00 ThDT8.1<br />
Quadcopter Breaking the MAV Endurance<br />
Record with Laser Power Beaming<br />
Michael C. Achtelik, Jan Stumpf,<br />
Ascending Technologies GmbH, Germany<br />
Daniel Gurdan and Klaus-Michael Doth<br />
Ascending Technologies GmbH, Germany<br />
• Flexible Quadrocopter Design<br />
• Separate processor for experiments<br />
• SDK for different control levels<br />
• MATLAB/Simulink Interface<br />
• Position Control<br />
• High update rate of 1 kHz<br />
• Position estimation based on LED<br />
tracking and IMU measurements<br />
• Powered by Laser Power Beaming<br />
• Small Buffer Battery with Recharge in<br />
Flight Capability<br />
• World Endurance Record with a 1 kg UAV:<br />
• 12h 27min in 2010 in Everett, WA<br />
Laser powered MAV “AscTec Pelican”<br />
17:15–17:30 ThDT8.3<br />
Quadrocopter Performance Benchmarking<br />
Using Optimal Control<br />
Robin Ritz, Markus Hehn,<br />
Sergei Lupashin, and Raffaello D’Andrea<br />
Institute for Dynamic Systems and Control, ETH Zurich, Switzerland<br />
• Numerical method for computing<br />
quadrocopter maneuvers satisfying<br />
Pontryagin’s minimum principle for timeoptimality.<br />
• Time-optimal maneuvers are a useful<br />
benchmark to compare other control<br />
strategies to.<br />
• Structure of time-optimal maneuvers is<br />
shown for various translations in two<br />
dimensions.<br />
• Experimental results demonstrate validity<br />
of computed trajectories.<br />
Time-Optimal Maneuvers for<br />
Vertical Displacements<br />
17:00–17:15 ThDT8.2<br />
Utilizing an Improved Rotorcraft Dynamic Model<br />
in State Estimation<br />
Robert Leishman, Jeff Ferrin, and Timothy McLain<br />
Mechanical Eng. Dept., Brigham Young University, USA<br />
John Macdonald, Stephen Quebe, and Randal Beard<br />
Elec. & Comp. Eng. Dept., Brigham Young University, USA<br />
• We present an improved dynamic<br />
model for small multirotor aircraft<br />
suitable for indoor flight.<br />
• The model enables more accurate<br />
IMU-based state estimates.<br />
• The benefit is reduced need for<br />
computationally expensive<br />
exteroceptive sensor updates.<br />
• The benefit is most helpful to payload<br />
constrained vehicles such as those<br />
flying indoors.<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–229–<br />
This figure compares the truth to two<br />
estimates of pitch: based on our<br />
improved model (dotted line) & a more<br />
traditional approach (dashed line)<br />
17:30–17:45 ThDT8.4<br />
A Scalable Information Theoretic<br />
Approach to Distributed Robot Coordination<br />
Brian J. Julian* † , Michael Angermann ‡ ,<br />
Mac Schwager* � , and Daniela Rus*<br />
*CSAIL, MIT, USA<br />
† Engineering Division, MIT Lincoln Laboratory, USA<br />
‡ DLR Oberpfaffenhofen, Germany<br />
� GRASP Laboratory, UPenn, USA<br />
• Scalable information theoretic<br />
approach to infer an environment’s<br />
state by distributively controlling<br />
robots equipped with sensors.<br />
• Novel algorithm for approximating<br />
joint measurement probabilities<br />
using consensus and sampling.<br />
• Distributed controller proven to be<br />
convergent between consensus<br />
rounds and, under certain<br />
conditions, locally optimal.<br />
• Complexity constant w.r.t. number<br />
of robots, and linear w.r.t. sensor<br />
measurement and environment<br />
discretization cells.<br />
Five quad-rotor flying robots inferring<br />
the state of an environment
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–230–
Author Index<br />
C = Chair, CC = Co-Chair, O = Organizer, * = interactive presentation (beside the “teaser” in regular sessions)<br />
A<br />
Abadie, Joel.............................................................. MoAT1.7 39<br />
Abbeel, Pieter........................................................... WeAT6 CC<br />
.................................................................................. WeAT6 O<br />
.................................................................................. WeBT5.7 2646<br />
.................................................................................. WeBT6 CC<br />
.................................................................................. WeBT6 O<br />
.................................................................................. ThCT6.7 4877<br />
Abbott, Jake.............................................................. MoBT1 CC<br />
.................................................................................. MoBT1.5 445<br />
.................................................................................. TuAPT10.2 *<br />
.................................................................................. TuBT5.2 1321<br />
.................................................................................. ThCT8.8 4975<br />
Abdo, Nichola ........................................................... WeAT5.5 2180<br />
.................................................................................. WeBPT10.14 *<br />
Abeles, Peter............................................................ MoBT2.2 475<br />
Abelmann, Leon ....................................................... MoBT1.1 421<br />
Abichandani, Pramod ............................................... WeAT8.2 2306<br />
Abiko, Satoko ........................................................... MoBT6.7 625<br />
Achar, Supreeth........................................................ MoAT6.3 227<br />
.................................................................................. WeAT9.1 2352<br />
Achtelik, Markus W................................................... WeAT6.8 2242<br />
Achtelik, Michael C................................................... ThDT8.1 5166<br />
Ackerman, Jeffrey..................................................... MoAT5.5 203<br />
.................................................................................. MoBPT10.14 *<br />
Adibi, Mehrad ........................................................... WeAT9.4 2371<br />
.................................................................................. WeBPT10.25 *<br />
Adorno, Bruno Vilhena ............................................. WeBT3.5 2545<br />
.................................................................................. WeCPT10.8 *<br />
.................................................................................. ThCT1.7 4658<br />
Agamennoni, Gabriel................................................ MoAT2.7 92<br />
Agha-mohammadi, Ali-akbar.................................... ThBT3.1 4284<br />
Aghasadeghi, Navid ................................................. TuBT9.8 1561<br />
Aghili, Farhad ........................................................... ThAT1.7 3784<br />
.................................................................................. ThBT1.3 4187<br />
Aguiar, António......................................................... WeCT6.8 3166<br />
Ahlberg, Carl............................................................. TuCT2.2 1626<br />
Ahmad Sharbafi, Maziar........................................... WeBT7.2 2723<br />
Ahmadi, Mojtaba....................................................... WeAT7.3 2261<br />
Ahmed, Zubair.......................................................... WeCT3.4 2992<br />
.................................................................................. WeDPT10.7 *<br />
Ahn, Bummo............................................................. ThBT7.7 4516<br />
Akachi, Kazuhiko...................................................... ThBT5.4 4400<br />
.................................................................................. ThCPT10.13 *<br />
Akgun, Baris ............................................................. WeBT5.6 2640<br />
.................................................................................. WeCPT10.15 *<br />
Alami, Rachid ........................................................... WeCT1 CC<br />
.................................................................................. WeCT1.5 2895<br />
.................................................................................. WeDPT10.2 *<br />
Albu-Schäffer, Alin.................................................... TuCT6.6 1789<br />
.................................................................................. WeAPT10.15 *<br />
.................................................................................. WeCT4.1 3023<br />
.................................................................................. WeCT7.5 3199<br />
.................................................................................. WeDT1.5 3367<br />
.................................................................................. WeDT9.3 3708<br />
.................................................................................. WeDPT10.20 *<br />
.................................................................................. ThAPT10.2 *<br />
.................................................................................. ThBT1.2 4180<br />
.................................................................................. ThBT1.7 4215<br />
.................................................................................. ThBT5.7 4420<br />
Alequin-Ramos, Freddie........................................... MoBT5.8 588<br />
Alismail, Hatem......................................................... ThAT6.8 4020<br />
Allan, Jean-Francois................................................. TuCT7.7 1849<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–231–<br />
Allen, Peter ............................................................... TuAT6 CC<br />
.................................................................................. TuAT6 O<br />
.................................................................................. TuBT6.5 1380<br />
.................................................................................. TuCPT10.14 *<br />
.................................................................................. WeAT9.7 2390<br />
Almaddah, Amr......................................................... TuAT1.4 807<br />
.................................................................................. TuBPT10.1 *<br />
Almqvist, Håkan........................................................ ThAT8.2 4078<br />
Alterovitz, Ron .......................................................... WeAT5 C<br />
.................................................................................. WeAT5 O<br />
.................................................................................. WeDT5 CC<br />
.................................................................................. WeDT5 O<br />
.................................................................................. ThDT7.3 5153<br />
Althoff, Daniel ........................................................... WeCT5.6 3114<br />
.................................................................................. WeDPT10.15 *<br />
Amato, Nancy ........................................................... WeBT5.5 2632<br />
.................................................................................. WeCPT10.14 *<br />
.................................................................................. ThBT3 C<br />
.................................................................................. ThBT3.1 4284<br />
Ambrose, Robert ...................................................... TuBT6.1 *<br />
Ames, Aaron............................................................. TuCT5.3 1723<br />
Amirat, Yacine .......................................................... TuCT5.7 1749<br />
An, Sang-ik ............................................................... WeBT7.8 2759<br />
Andersen, Tim .......................................................... TuCT3.5 1687<br />
Anderson, Chris........................................................ WeAT6.1 *<br />
Anderson, Ross ........................................................ ThAT4.5 3917<br />
.................................................................................. ThBPT10.11 *<br />
Andersson, Sean ...................................................... WeCT5.5 3108<br />
.................................................................................. WeDPT10.14 *<br />
Ando, Noriaki ............................................................ TuAT7.5 1042<br />
.................................................................................. TuBPT10.17 *<br />
Ando, Teruhisa ......................................................... WeDT8.6 3673<br />
.................................................................................. ThAPT10.21 *<br />
Andreff, Nicolas ........................................................ WeBT9.7 2855<br />
Ang Jr, Marcelo H..................................................... ThAT4.7 3931<br />
Angeles, Jorge.......................................................... MoAT7.3 280<br />
Angermann, Michael................................................. ThDT8.4 5187<br />
Anisi, David A. .......................................................... MoAT6 CC<br />
.................................................................................. MoAT6.4 235<br />
.................................................................................. MoBT6 C<br />
.................................................................................. MoBPT10.16 *<br />
Annunziata, Salvatore .............................................. ThAT1.6 3776<br />
.................................................................................. ThBPT10.3 *<br />
Antonelli, Gianluca.................................................... WeBT8.3 2778<br />
.................................................................................. WeCT6 CC<br />
.................................................................................. WeCT6 O<br />
.................................................................................. WeCT6.8 3166<br />
.................................................................................. WeDT6 O<br />
Aoi, Shinya ............................................................... WeAT7 CC<br />
.................................................................................. WeAT7.5 2274<br />
.................................................................................. WeAT7.6 2280<br />
.................................................................................. WeBPT10.20 *<br />
.................................................................................. WeBPT10.21 *<br />
Aoki, Keiji.................................................................. ThAT8.7 4109<br />
Aoki, Kengo .............................................................. ThCT2.7 4710<br />
Aoki, Takeshi ............................................................ ThBT8.4 4550<br />
.................................................................................. ThCPT10.22 *<br />
Aoyama, Tadayoshi.................................................. ThDT5.2 5094<br />
Apker, Thomas ......................................................... MoAT3.5 125<br />
.................................................................................. MoBPT10.8 *<br />
Arabagi, Veaceslav................................................... ThAT4.8 3937<br />
Arafuka, Kazushi ...................................................... MoAT9.5 395<br />
.................................................................................. MoBPT10.26 *
Arai, Fumihito ........................................................... MoAT1 CC<br />
.................................................................................. MoAT1.3 13<br />
.................................................................................. MoBT1.4 439<br />
.................................................................................. TuAPT10.1 *<br />
.................................................................................. TuBT3.7 1309<br />
.................................................................................. TuCT3.6 1693<br />
Arai, Tamio ............................................................... ThCT2.5 4698<br />
.................................................................................. ThDPT10.5 *<br />
Arai, Tatsuo .............................................................. MoAT1.5 25<br />
.................................................................................. MoBT1 C<br />
.................................................................................. MoBT1.2 427<br />
.................................................................................. MoBPT10.2 *<br />
.................................................................................. TuAT1.4 807<br />
.................................................................................. TuBPT10.1 *<br />
Araki, Takahiro ......................................................... TuCT9.6 1946<br />
.................................................................................. WeAPT10.24 *<br />
Araki, Takaya............................................................ TuBT9.5 1540<br />
.................................................................................. TuCPT10.23 *<br />
Arata, Jumpei ........................................................... WeAT4.6 2145<br />
.................................................................................. WeBPT10.12 *<br />
Ardö, Håkan.............................................................. WeCT3.1 2971<br />
Arechavaleta, Gustavo ............................................. TuCT1.8 1612<br />
Arevalo, Juan Carlos ................................................ TuBT8.8 1507<br />
Argentieri, Sylvain..................................................... MoAT3.7 137<br />
Argiles, Alberto ......................................................... ThBT6.5 4448<br />
.................................................................................. ThCPT10.17 *<br />
Ariizumi, Ryo ............................................................ TuCT8.8 1907<br />
Arimoto, Suguru........................................................ TuCT6.1 *<br />
Armesto, Leopoldo ................................................... ThBT3.8 4335<br />
Arras, Kai Oliver ....................................................... WeAT1 C<br />
.................................................................................. WeAT1.1 1968<br />
.................................................................................. ThAT2 CC<br />
.................................................................................. ThAT2.6 3838<br />
.................................................................................. ThAT2.7 3844<br />
.................................................................................. ThBPT10.6 *<br />
Arrichiello, Filippo ..................................................... WeBT8 CC<br />
.................................................................................. WeBT8.3 2778<br />
.................................................................................. WeCT6.8 3166<br />
Arslan, Oktay............................................................ WeDT5.2 3507<br />
.................................................................................. WeDT5.6 3533<br />
.................................................................................. ThAPT10.12 *<br />
Artigas, Jordi............................................................. MoAT4.7 177<br />
.................................................................................. MoBT7.5 665<br />
.................................................................................. TuAPT10.17 *<br />
Asada, Harry............................................................. ThAT4 CC<br />
.................................................................................. ThAT4 O<br />
.................................................................................. ThAT4.7 3931<br />
.................................................................................. ThBT4 O<br />
Asadian, Ali............................................................... WeBT3.6 2551<br />
.................................................................................. WeCPT10.9 *<br />
Asama, Hajime ......................................................... WeCT7.6 3207<br />
.................................................................................. WeDPT10.21 *<br />
Asano, Fumihiko....................................................... WeAT7.1 2249<br />
.................................................................................. WeBT7.6 2747<br />
.................................................................................. WeCPT10.21 *<br />
Asano, Futoshi.......................................................... MoAT3.8 143<br />
Asatani, Minami........................................................ ThBT6.7 4463<br />
Asfour, Tamim .......................................................... TuCT6.2 1761<br />
.................................................................................. TuCT6.5 1781<br />
.................................................................................. WeAPT10.14 *<br />
.................................................................................. ThBT5 C<br />
.................................................................................. ThBT5 O<br />
.................................................................................. ThCT5 O<br />
.................................................................................. ThCT5.1 *<br />
Asplund, Lars............................................................ TuCT2.2 1626<br />
Au, Tsz-Chiu............................................................. ThBT9.3 4581<br />
Aukes, Daniel ........................................................... TuBT6.4 1373<br />
.................................................................................. TuCPT10.13 *<br />
Avci, Ebubekir........................................................... MoBT1.2 427<br />
Ayanian, Nora........................................................... WeCT5.8 3126<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–232–<br />
Ayusawa, Ko............................................................. WeDT9.2 3701<br />
Azevedo Coste, Christine ......................................... TuCT5.4 1731<br />
.................................................................................. WeAPT10.10 *<br />
Azimi, Ali................................................................... MoAT7.3<br />
B<br />
280<br />
Babic, Jan................................................................. ThCT5.6 4832<br />
.................................................................................. ThDPT10.15 *<br />
Babuska, Robert....................................................... WeBT7.3 2729<br />
Bachmann, Richard J. .............................................. MoAT5.6 209<br />
.................................................................................. MoBPT10.15 *<br />
Badino, Hernan......................................................... WeCT9.1 3283<br />
Baek, Stanley ........................................................... WeBT6.3 2674<br />
Bagutti, Lorenzo ....................................................... WeBT4.2 2578<br />
Bahls, Thomas.......................................................... TuAT7.7 1055<br />
.................................................................................. TuCT9.4 1933<br />
.................................................................................. WeAPT10.22 *<br />
Bahr, Fred................................................................. WeCT6.3 3132<br />
Bailey, Tim................................................................ TuAT2.8 886<br />
.................................................................................. ThBT9.6 4601<br />
.................................................................................. ThCPT10.27 *<br />
Baisch, Andrew......................................................... ThDT4.3 5073<br />
Bajcsy, Ruzena......................................................... WePL C<br />
Bajo, Andrea............................................................. WeAT9.7 2390<br />
Balaguer, Benjamin .................................................. TuBT7.1 1405<br />
Balasubramanian, Ravi ............................................ TuCT7 CC<br />
.................................................................................. TuCT7.3 1823<br />
Balkcom, Devin......................................................... ThBT3.6 4321<br />
.................................................................................. ThCPT10.9 *<br />
Ballard, Dana............................................................ TuBT1.4 1194<br />
.................................................................................. TuCPT10.1 *<br />
Banno, Yoshihisa...................................................... WeBT7.4 2735<br />
.................................................................................. WeCPT10.19 *<br />
Baradat, Cédric......................................................... ThDT4.2 5067<br />
Barfoot, Timothy ....................................................... MoBT6.8 631<br />
Barkby, Stephen ....................................................... TuBT2.3 1242<br />
.................................................................................. ThCT3 C<br />
Barrows, Geoffrey..................................................... TuAT8.6 1099<br />
.................................................................................. TuBPT10.21 *<br />
Bascetta, Luca.......................................................... WeCT3.1 2971<br />
Basoeki, Fransiska ................................................... WeDT3.6 3480<br />
.................................................................................. ThAPT10.9 *<br />
Bastos-Filho, Teodiano............................................. ThAT1.1 3746<br />
Bates, Terry .............................................................. WeAT9.1 2352<br />
Baum, Marcus .......................................................... WeAT2.5 2050<br />
.................................................................................. WeBPT10.5 *<br />
Bäuml, Berthold ........................................................ TuCT1.4 1585<br />
.................................................................................. WeAPT10.1 *<br />
Bauzano, Enrique ..................................................... WeAT3.8 2121<br />
Bays, Matthew .......................................................... WeCT8.1 3227<br />
Beard, Randy............................................................ WeBT6.5 2688<br />
.................................................................................. WeCPT10.17 *<br />
.................................................................................. ThDT8 C<br />
.................................................................................. ThDT8.2 5173<br />
Becattini, Gabriele .................................................... TuBT5.8 1359<br />
Becker, Aaron........................................................... TuAT9.7 1160<br />
Becker, Brian C. ....................................................... ThDT7.4 5160<br />
Beetz, Michael .......................................................... WeCT7.1 3172<br />
.................................................................................. ThAT9 C<br />
.................................................................................. ThAT9.4 4141<br />
.................................................................................. ThBT2.6 4263<br />
.................................................................................. ThBPT10.25 *<br />
.................................................................................. ThCPT10.6 *<br />
Behar, Evan.............................................................. TuCT1.2 1573<br />
Behera, Laxmidhar ................................................... ThCT2.8 4716<br />
Behkam, Bahareh..................................................... ThAT4.8 3937<br />
Bekiroglu, Yasemin................................................... TuBT9.7 1554<br />
Bekris, Kostas E. ...................................................... WeBT5 C<br />
.................................................................................. WeBT5 O<br />
.................................................................................. WeCT8.2 3235<br />
.................................................................................. WeCT8.7 3268
.................................................................................. ThBT3.2 4292<br />
Bellens, Steven......................................................... WeCT4.2 3031<br />
Bellingham, James G. .............................................. WeCT6.3 3132<br />
Belo, Felipe............................................................... ThAT1.5 3770<br />
.................................................................................. ThBPT10.2 *<br />
Belta, Calin ............................................................... WeCT5 CC<br />
.................................................................................. WeCT5 O<br />
.................................................................................. WeCT5.2 3087<br />
.................................................................................. WeCT5.5 3108<br />
.................................................................................. WeDPT10.14 *<br />
Ben Amar, Faiz......................................................... MoAT7.2 274<br />
Bender, John ............................................................ MoAT5.7 215<br />
Bengel, Matthias....................................................... MoAT6.5 241<br />
.................................................................................. MoBPT10.17 *<br />
Benhabib, Beno........................................................ ThBT8.3 4544<br />
Bennett, Bradford ..................................................... ThAT5.7 3969<br />
Bennewitz, Maren..................................................... ThCT5.8 4844<br />
Benson, Hande......................................................... WeAT8.2 2306<br />
Beranek, Richard...................................................... WeAT7.3 2261<br />
Bergbreiter, Sarah .................................................... TuCT3.4 1680<br />
.................................................................................. WeAPT10.7 *<br />
Bergström, Niklas ..................................................... TuAT1.7 827<br />
.................................................................................. WeAT2.2 2028<br />
Berkley, Jeff.............................................................. WeBT3.4 2539<br />
.................................................................................. WeCPT10.7 *<br />
Berman, Spring......................................................... WeCT8 CC<br />
.................................................................................. ThAT4.6 3923<br />
.................................................................................. ThBPT10.12 *<br />
Bernardes, Mariana Costa........................................ WeBT3.5 2545<br />
.................................................................................. WeCPT10.8 *<br />
Bernardino, Alexandre.............................................. ThCT5.5 4826<br />
.................................................................................. ThDPT10.14 *<br />
Bernstein, Matthew................................................... TuCT6.8 1804<br />
Berret, Bastien.......................................................... ThBT4.3 4354<br />
Bersch, Christian ...................................................... TuBT7.2 1413<br />
Berthoz, Alain ........................................................... WePL.1 *<br />
Bessho, Fumihiro...................................................... TuBT1.7 1214<br />
Bewley, Thomas....................................................... WeBT7.5 2741<br />
.................................................................................. WeCPT10.20 *<br />
.................................................................................. ThAT8.5 4097<br />
.................................................................................. ThBPT10.23 *<br />
Beyeler, Felix............................................................ TuAT3.7 919<br />
Bhatawadekar, Vineet .............................................. TuAT9.8 1167<br />
Bhattacharyya, Samrat............................................. TuBT6.5 1380<br />
.................................................................................. TuCPT10.14 *<br />
Bhoite, Mohit............................................................. ThCT4.7 4797<br />
Biagiotti, Luigi ........................................................... ThAT3 CC<br />
.................................................................................. ThAT3.8 3899<br />
Bialkowski, Joshua J ................................................ WeDT5.3 3513<br />
Bicchi, Antonio.......................................................... WeBT9.1 2817<br />
.................................................................................. ThAT1.5 3770<br />
.................................................................................. ThBPT10.2 *<br />
Bidaud, Philippe........................................................ WeBT7.7 2753<br />
Bierbaum, Alexander................................................ TuCT6.2 1761<br />
Biggs, Geoffrey......................................................... TuAT7 C<br />
.................................................................................. TuAT7.5 1042<br />
.................................................................................. TuBPT10.17 *<br />
Billard, Aude ............................................................. MoBT8.4 710<br />
.................................................................................. TuAPT10.19 *<br />
Bin Mohd Shariff, Ahmed Shafeeq ........................... WeDT6.3 3551<br />
Binney, Jonathan...................................................... WeCT6.5 3147<br />
.................................................................................. WeDPT10.17 *<br />
Birbach, Oliver.......................................................... WeCT9 CC<br />
.................................................................................. WeCT9.4 3305<br />
.................................................................................. WeDT2.6 3426<br />
.................................................................................. WeDPT10.25 *<br />
.................................................................................. ThAPT10.6 *<br />
Birchfield, Stan ......................................................... ThCT6.6 4871<br />
.................................................................................. ThDPT10.18 *<br />
Bird, Justin................................................................ ThCT8.8 4975<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–233–<br />
Birkmeyer, Paul ........................................................ ThDT5.1 5087<br />
Bischof, Horst ........................................................... ThAT6.4 3990<br />
.................................................................................. ThBPT10.16 *<br />
Biswas, Joydeep....................................................... MoAT2.4 73<br />
.................................................................................. MoBPT10.4 *<br />
Bjerkeng, Magnus..................................................... MoAT6.6 247<br />
.................................................................................. MoBPT10.18 *<br />
.................................................................................. ThCT2.2 4676<br />
Björkman, Mårten ..................................................... TuAT1.7 827<br />
Black, Richard J........................................................ WeBT3.8 2564<br />
Bland, Daniel Peter................................................... ThCT8.2 4935<br />
Blanke, Olaf .............................................................. ThCT1.8 4664<br />
Bleuler, Hannes ........................................................ ThCT1.8 4664<br />
Bley, Andreas ........................................................... WeBT1.5 2430<br />
Blodow, Nico............................................................. ThBT2.6 4263<br />
.................................................................................. ThCPT10.6 *<br />
Blumel, Marcus......................................................... MoAT5.7 215<br />
Bó, Antônio Padilha Lanari ....................................... ThCT1.7 4658<br />
Bo, Liefeng ............................................................... TuAT1.6 821<br />
.................................................................................. TuBPT10.3 *<br />
Bobadilla, Leonardo.................................................. WeCT5.4 3101<br />
.................................................................................. WeDPT10.13 *<br />
Bocsi, Botond ........................................................... MoBT8.2 698<br />
Bohg, Jeannette ....................................................... WeDT1.1 3342<br />
Bok, Yunsu ............................................................... ThBT6.3 4436<br />
Bolopion, Aude ......................................................... TuAT3.3 894<br />
Bonani, Michael ........................................................ ThCT4.2 4762<br />
Bonato, Paolo ........................................................... ThCT7.1 4893<br />
Book, Wayne ............................................................ ThBT8.5 4556<br />
.................................................................................. ThCPT10.23 *<br />
Bordignon, Mirko ...................................................... WeDT8.4 3659<br />
.................................................................................. ThAPT10.19 *<br />
Borges, Geovany Araujo .......................................... WeBT3.5 2545<br />
.................................................................................. WeCPT10.8 *<br />
Borst, Christoph........................................................ TuCT6.3 1768<br />
Bosse, Michael ......................................................... ThAT2.5 3830<br />
.................................................................................. ThBPT10.5 *<br />
Bouabdallah, Samir .................................................. WeAT6.5 2223<br />
.................................................................................. WeBPT10.17 *<br />
Boucher, Jean-David ................................................ WeCT1.5 2895<br />
.................................................................................. WeDPT10.2 *<br />
Boukallel, Mehdi ....................................................... TuAT3.6 913<br />
.................................................................................. TuBPT10.9 *<br />
.................................................................................. WeBT2.7 2505<br />
Boularias, Abdeslam................................................. TuBT9.6 1548<br />
.................................................................................. TuCPT10.24 *<br />
Bouraine, Sara.......................................................... WeCT3.3 2985<br />
Bourdin, Christophe.................................................. TuBT5.4 1333<br />
.................................................................................. TuCPT10.10 *<br />
Bouyarmane, Karim.................................................. ThBT5.6 4414<br />
.................................................................................. ThCPT10.15 *<br />
Boxerbaum, Alexander ............................................. MoAT5.4 197<br />
.................................................................................. MoBPT10.13 *<br />
Boyer, Frédéric ......................................................... TuCT8.7 1901<br />
Bozorg Grayeli, Alexis .............................................. WeBT3.3 2532<br />
Braman, Karen ......................................................... WeCT2.2 2928<br />
Brambilla, Manuele................................................... ThCT9.8 5027<br />
Brand, Christoph Norbert.......................................... WeCT5.6 3114<br />
.................................................................................. WeDPT10.15 *<br />
Branicky, Michael ..................................................... WeBT5.3 2620<br />
Branson, David ......................................................... TuAT8.5 1093<br />
.................................................................................. TuBPT10.20 *<br />
.................................................................................. ThAT7.5 4054<br />
.................................................................................. ThBPT10.20 *<br />
Bravo, Ignacio........................................................... ThAT1.1 3746<br />
Breitenmoser, Andreas............................................. MoAT2.5 79<br />
.................................................................................. MoBPT10.5 *<br />
Brethe, Jean-François .............................................. ThAT7 CC<br />
.................................................................................. ThAT7.7 4066<br />
Bretl, Timothy ........................................................... TuAT9 CC
.................................................................................. TuAT9.7 1160<br />
.................................................................................. TuBT9 CC<br />
.................................................................................. TuBT9.8 1561<br />
Brewer, Reuben........................................................ WeBT4.1 2570<br />
Bringout, Gael........................................................... TuBT3.3 1285<br />
Briot, Sébastien ........................................................ WeDT9.6 3728<br />
.................................................................................. ThAPT10.24 *<br />
Broeders, Ivo A. M. J................................................ TuAT5.3 937<br />
Broggi, Alberto.......................................................... TuCT1.6 1599<br />
.................................................................................. WeAPT10.3 *<br />
Brooks, David ........................................................... TuAT8.6 1099<br />
.................................................................................. TuBPT10.21 *<br />
Browning, Brett......................................................... ThAT6.8 4020<br />
Bruneau, Olivier........................................................ ThAT5.4 3951<br />
.................................................................................. ThBPT10.13 *<br />
Brunner, Christopher Joseph.................................... WeBT2.5 2489<br />
.................................................................................. WeCPT10.5 *<br />
Brutschy, Arne.......................................................... ThCT9.8 5027<br />
Bruyninckx, Herman ................................................. WeCT3.1 2971<br />
.................................................................................. WeCT4.2 3031<br />
.................................................................................. ThCT2.3 4684<br />
Bryson, Mitch............................................................ ThBT2 CC<br />
.................................................................................. ThBT2.5 4256<br />
.................................................................................. ThCPT10.5 *<br />
Bubeck, Alexander ................................................... MoAT6.5 241<br />
.................................................................................. MoBPT10.17 *<br />
Buchaillot, Lionel ...................................................... MoAT1.8 45<br />
Buchan, Austin ......................................................... TuAT8.2 1075<br />
Buehler, Martin ......................................................... WeBT7 CC<br />
Buelthoff, Heinrich H................................................. MoAT4.5 163<br />
.................................................................................. MoBPT10.11 *<br />
.................................................................................. WeAT6.4 2215<br />
.................................................................................. WeBPT10.16 *<br />
.................................................................................. WePL.1 *<br />
.................................................................................. WeCT4.3 3039<br />
Buether, John ........................................................... MoAT4.6 171<br />
.................................................................................. MoBPT10.12 *<br />
Buffinton, Keith ......................................................... MoAT5.3 190<br />
Bugajska, Magdalena............................................... MoAT3.5 125<br />
.................................................................................. MoBPT10.8 *<br />
Burch, Derek............................................................. ThBT8.2 4536<br />
Burdet, Etienne......................................................... WeBT4.2 2578<br />
.................................................................................. ThAT9.1 4121<br />
Burgard, Wolfram ..................................................... MoAT2.6 86<br />
.................................................................................. MoBPT10.6 *<br />
.................................................................................. TuBT2 C<br />
.................................................................................. TuBT2.4 1249<br />
.................................................................................. TuCPT10.4 *<br />
.................................................................................. WeAT5.5 2180<br />
.................................................................................. WeBPT10.14 *<br />
.................................................................................. WeDT9.4 3716<br />
.................................................................................. ThAPT10.22 *<br />
.................................................................................. ThBT2.4 4249<br />
.................................................................................. ThCT8.3 4941<br />
.................................................................................. ThCPT10.4 *<br />
Burgess, Stuart......................................................... ThAT7.3 4042<br />
Burgner, Jessica....................................................... WeBT3.1 2517<br />
Burguera, Antoni....................................................... WeDT6.7 3577<br />
Burschka, Darius ...................................................... TuBT1.8 1221<br />
.................................................................................. WeCT9.3 3297<br />
Burton, Lisa .............................................................. ThAT3.7 3893<br />
Buss, Martin.............................................................. WeCT5.6 3114<br />
.................................................................................. WeDPT10.15 *<br />
Butzke, Jonathan...................................................... WeCT8.5 3254<br />
.................................................................................. WeDPT10.23 *<br />
Buys, Koen ............................................................... WeCT4.2 3031<br />
Buzzoni, Michele ...................................................... TuCT1.6 1599<br />
.................................................................................. WeAPT10.3<br />
C<br />
*<br />
Caccavale, Fabrizio.................................................. WeBT8.3 2778<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–234–<br />
.................................................................................. ThBT1.4 4194<br />
.................................................................................. ThCPT10.1 *<br />
Cadeddu, Jeffrey A................................................... WeAT9.4 2371<br />
.................................................................................. WeBPT10.25 *<br />
Cai, Binghuang ......................................................... WeDT8.8 3686<br />
Cai, Chuanwu ........................................................... TuBT8.3 1473<br />
Cakmak, Maya.......................................................... WeAT1.4 1986<br />
.................................................................................. WeBPT10.1 *<br />
Caldwell, Darwin G. .................................................. MoAT8.1 318<br />
.................................................................................. MoAT9.2 378<br />
.................................................................................. MoAT9.4 390<br />
.................................................................................. MoBPT10.25 *<br />
.................................................................................. TuAT8.5 1093<br />
.................................................................................. TuBT5.8 1359<br />
.................................................................................. TuBPT10.20 *<br />
.................................................................................. WeCT4.4 3047<br />
.................................................................................. WeDT2.4 3413<br />
.................................................................................. WeDPT10.10 *<br />
.................................................................................. ThAT7.5 4054<br />
.................................................................................. ThAPT10.4 *<br />
.................................................................................. ThBPT10.20 *<br />
Calinon, Sylvain........................................................ MoAT8.1 318<br />
.................................................................................. WeCT4.4 3047<br />
.................................................................................. WeDT2 C<br />
.................................................................................. WeDT2.4 3413<br />
.................................................................................. WeDPT10.10 *<br />
.................................................................................. ThAPT10.4 *<br />
Calli, Berk ................................................................. TuAT6.6 995<br />
.................................................................................. TuBPT10.15 *<br />
Camilli, Richard ........................................................ MoAT6.8 261<br />
.................................................................................. ThCT3.2 4722<br />
Campbell, Mark ........................................................ TuCT1.7 1605<br />
Campolo, Domenico ................................................. ThCT7.3 4905<br />
Campos, Mario F. Montenegro................................. WeAT1 CC<br />
.................................................................................. WeAT1.2 1974<br />
Cangelosi, Angelo .................................................... ThCT9.6 5015<br />
.................................................................................. ThDPT10.27 *<br />
Cannata, Giorgio ...................................................... WeDT9.1 3694<br />
Cappelleri, David ...................................................... TuAT3 CC<br />
.................................................................................. TuAT3 O<br />
.................................................................................. TuAT3.8 925<br />
.................................................................................. TuBT3 C<br />
.................................................................................. TuBT3 O<br />
.................................................................................. TuCT3 CC<br />
.................................................................................. TuCT3 O<br />
Carlési, Nicolas......................................................... ThDT3.2 5041<br />
Carlevaris-Bianco, Nicholas ..................................... ThBT4.7 4378<br />
Carlson, Rolf............................................................. WeDT1.1 3342<br />
Caro, Stéphane ........................................................ ThAT7.2 4034<br />
Carpin, Stefano......................................................... TuBT7.1 1405<br />
.................................................................................. ThBT8.2 4536<br />
Carryon, Gabe .......................................................... MoBT5.7 580<br />
Castellanos, Jose A.................................................. TuBT2.5 1256<br />
.................................................................................. TuCPT10.5 *<br />
Castellini, Claudio..................................................... MoBT7.6 672<br />
.................................................................................. TuAPT10.18 *<br />
.................................................................................. WeAT3.6 2108<br />
.................................................................................. WeBPT10.9 *<br />
Castillo, Pedro .......................................................... WeBT6.4 2682<br />
.................................................................................. WeCPT10.16 *<br />
Castro, Sebastian ..................................................... WeCT5.7 3120<br />
Catoire, Laurent........................................................ ThBT7.1 4477<br />
Cauli, Nino ................................................................ ThCT5.5 4826<br />
.................................................................................. ThDPT10.14 *<br />
Cavusoglu, M. Cenk ................................................. MoAT4 CC<br />
.................................................................................. MoAT4 O<br />
.................................................................................. WeDT3 C<br />
.................................................................................. WeDT3.3 3460<br />
Ceccarelli, Marco...................................................... ThDT4.1 5061<br />
Célérier, Charlotte .................................................... WeBT3.3 2532
Censi, Andrea........................................................... WeAT2.6 2056<br />
.................................................................................. WeBPT10.6 *<br />
.................................................................................. ThBT6.8 4469<br />
Chablat, Damien....................................................... TuBT7.8 1453<br />
.................................................................................. ThAT7.2 4034<br />
Chae, Hansang......................................................... TuBT7.7 1447<br />
Chae, Yongwook ...................................................... MoBT7.8 685<br />
Chaimowicz, Luiz...................................................... ThBT4.6 4372<br />
.................................................................................. ThCPT10.12 *<br />
Chakravorty, Suman................................................. ThBT3.1 4284<br />
Chalon, Maxime........................................................ TuBT6.3 1366<br />
.................................................................................. ThBT1.7 4215<br />
Chamberlain, Lyle..................................................... MoAT6.3 227<br />
Chambers, Andrew................................................... MoAT6.3 227<br />
Chan, Ambrose......................................................... WeBT9.2 2825<br />
Chang, Bo................................................................. TuAT3.5 907<br />
.................................................................................. TuBPT10.8 *<br />
Chang, Doyoung....................................................... TuAT5.4 943<br />
.................................................................................. TuBPT10.10 *<br />
Chang-Siu, Evan ...................................................... TuCT8.5 1887<br />
.................................................................................. WeAPT10.20 *<br />
.................................................................................. WeBT2.3 2474<br />
Chao, Yi.................................................................... WeCT6.4 3140<br />
.................................................................................. WeDPT10.16 *<br />
Chapuis, Dominique ................................................. WeCT4.8 3074<br />
Charpillet, Francois................................................... ThAT8.6 4103<br />
.................................................................................. ThBPT10.24 *<br />
Charusta, Krzysztof Andrzej..................................... TuCT6.7 1797<br />
Chaumette, Francois ................................................ MoBT9.6 768<br />
.................................................................................. TuAPT10.24 *<br />
.................................................................................. TuCT1.5 1593<br />
.................................................................................. WeAPT10.2 *<br />
.................................................................................. WeBT9 C<br />
.................................................................................. WeBT9.5 2843<br />
.................................................................................. WeBT9.6 2849<br />
.................................................................................. WeCPT10.26 *<br />
.................................................................................. WeCPT10.27 *<br />
Chen, Bor-rong ......................................................... ThBT7.3 4488<br />
Chen, Fei.................................................................. ThCT2.1 4670<br />
Chen, Haoyao........................................................... MoBT1.6 451<br />
.................................................................................. TuAPT10.3 *<br />
Chen, I-Ming............................................................. TuCT7 C<br />
.................................................................................. WeCT2.3 2935<br />
Chen, S.Y. ................................................................ TuAT1.3 801<br />
Chen, Weidong......................................................... WeCT2.7 2959<br />
Chen, Weihai............................................................ MoBT9.2 744<br />
Chen, Yi-Jie .............................................................. TuCT9.8 1962<br />
Cheng, Bo................................................................. MoBT5.6 574<br />
.................................................................................. TuAPT10.12 *<br />
Cherubini, Andrea..................................................... TuCT1.5 1593<br />
.................................................................................. WeAPT10.2 *<br />
Chiba, Ryosuke ........................................................ ThCT2.5 4698<br />
.................................................................................. ThDPT10.5 *<br />
Chiel, Hillel................................................................ MoAT5.4 197<br />
.................................................................................. MoBPT10.13 *<br />
Chien, Steve............................................................. WeCT6.4 3140<br />
.................................................................................. WeDPT10.16 *<br />
Chilian, Annett .......................................................... WeBT2.6 2497<br />
Chinzei, Kiyoyuki ...................................................... WeBT4.3 2584<br />
Chipalkatty, Rahul .................................................... ThBT8.5 4556<br />
.................................................................................. ThCPT10.23 *<br />
Chirikjian, Gregory.................................................... ThAT4 O<br />
.................................................................................. ThBT4 O<br />
Chitre, Mandar.......................................................... WeDT6 CC<br />
Chitta, Sachin ........................................................... WeDT2 CC<br />
Chizeck, Howard ...................................................... WeBT4.8 2614<br />
Chli, Margarita .......................................................... WeAT6.8 2242<br />
Cho, Changhyun....................................................... TuCT7.8 1857<br />
Choi, Dong - Geol..................................................... ThBT6.3 4436<br />
Choi, Dongmin.......................................................... TuBT7.7 1447<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–235–<br />
Choi, Hyouk Ryeol.................................................... TuBT7.7 1447<br />
Chopra, Nikhil ........................................................... MoBT7 C<br />
.................................................................................. MoBT7.7 679<br />
.................................................................................. WeAT8.4 2321<br />
.................................................................................. WeBPT10.22 *<br />
Choset, Howie .......................................................... MoAT5.8 221<br />
.................................................................................. MoAT8.7 357<br />
.................................................................................. MoBT2.3 482<br />
.................................................................................. TuAT8 CC<br />
.................................................................................. TuAT8.1 1069<br />
.................................................................................. TuAT8.2 1075<br />
.................................................................................. TuBT5.7 1353<br />
.................................................................................. WeCT8.6 3260<br />
.................................................................................. WeDPT10.24 *<br />
.................................................................................. ThAT3.7 3893<br />
.................................................................................. ThDT7.2 5147<br />
Choti, Michael........................................................... WeBT3.4 2539<br />
Chou, Ya-Cheng....................................................... TuBT8.6 1493<br />
.................................................................................. TuCPT10.21 *<br />
Christensen, Anders Lyhne ...................................... ThCT4.2 4762<br />
Christensen, Henrik Iskov......................................... TuBT2.6 1264<br />
.................................................................................. TuCPT10.6 *<br />
.................................................................................. ThPT11 C<br />
Christensen, Kim Hardam ........................................ TuAT2.2 847<br />
Christensen, Quinton................................................ WeBT4.5 2596<br />
.................................................................................. WeCPT10.11 *<br />
Christopher, Ray....................................................... WeDT7.4 3608<br />
Chuang, Lewis L....................................................... WeCT4.3 3039<br />
Chung, Timothy H..................................................... ThBT8 C<br />
.................................................................................. ThBT8.2 4536<br />
Chung, Wan Kyun .................................................... WeBT3 C<br />
.................................................................................. WeBT3.2 2524<br />
Churaman, Wayne.................................................... TuCT3.4 1680<br />
.................................................................................. WeAPT10.7 *<br />
Ciliberto, Carlo.......................................................... TuBT9.3 1526<br />
.................................................................................. ThAT9.6 4154<br />
.................................................................................. ThBPT10.27 *<br />
Ciocarlie, Matei......................................................... TuAT6 C<br />
.................................................................................. TuAT6 O<br />
Civera, Javier............................................................ TuBT2 CC<br />
.................................................................................. TuBT2.8 1277<br />
.................................................................................. ThBT6.5 4448<br />
.................................................................................. ThCPT10.17 *<br />
Claassens, Jonathan ................................................ WeAT1.3 1980<br />
Clark, James Romney .............................................. TuBT5.2 1321<br />
Cognetti, Marco ........................................................ MoBT2.1 469<br />
Colas, Francis........................................................... ThAT2.4 3824<br />
.................................................................................. ThBPT10.4 *<br />
Coleman, Sonya ....................................................... ThCT2.8 4716<br />
Colgate, Edward ....................................................... WeAT4.1 *<br />
Coltin, Brian .............................................................. MoAT2.4 73<br />
.................................................................................. MoBPT10.4 *<br />
Comport, Andrew Ian................................................ ThBT2.3 4242<br />
Conradt, Jorg............................................................ MoBT8 CC<br />
Conti, Francois ......................................................... WeCT4.1 3023<br />
Cook IV, Atlas F........................................................ WeDT5.5 3526<br />
.................................................................................. ThAPT10.11 *<br />
Cordes, Florian ......................................................... WeDT8.3 3653<br />
Corke, Peter ............................................................. ThAT3.3 3868<br />
.................................................................................. ThBT6.1 *<br />
Correll, Nikolaus ....................................................... ThCT4 C<br />
.................................................................................. ThCT4 O<br />
.................................................................................. ThCT4.1 *<br />
.................................................................................. ThCT4.5 4783<br />
.................................................................................. ThDPT10.11 *<br />
Corso, Jason ............................................................ TuCT2.3 1632<br />
Cortez, Andres.......................................................... WeAT8.6 2333<br />
.................................................................................. WeBPT10.24 *<br />
Cosgun, Akansel ...................................................... ThCT1.2 4627<br />
Costa, Lino ............................................................... WeAT7.7 2286
Coste, Michel............................................................ TuBT7.8 1453<br />
Cotin, Stephane........................................................ WeBT4.7 2608<br />
Coutard, Laurent....................................................... WeBT9.5 2843<br />
.................................................................................. WeCPT10.26 *<br />
Cowlagi, Raghvendra ............................................... WeDT5.1 3501<br />
Cowley, Anthony....................................................... TuAT7.6 1048<br />
.................................................................................. TuBPT10.18 *<br />
.................................................................................. ThCT4.4 4776<br />
.................................................................................. ThDPT10.10 *<br />
Creed, Ross.............................................................. ThAT2.2 3808<br />
Croft, Elizabeth......................................................... WeAT1.5 1994<br />
.................................................................................. WeBT9.2 2825<br />
.................................................................................. WeBPT10.2 *<br />
.................................................................................. WeDT1 CC<br />
.................................................................................. WeDT1.4 3361<br />
.................................................................................. ThAPT10.1 *<br />
Csató, Lehel ............................................................. MoBT8.2 698<br />
Cutkosky, Mark......................................................... MoBT9.7 774<br />
.................................................................................. TuAT6.1 *<br />
.................................................................................. TuBT6.4 1373<br />
.................................................................................. TuCPT10.13 *<br />
.................................................................................. WeBT3.8 2564<br />
.................................................................................. WeCT3.4 2992<br />
.................................................................................. WeCT3.5 2998<br />
.................................................................................. WeDPT10.7 *<br />
.................................................................................. WeDPT10.8 *<br />
.................................................................................. ThDT5.3 5100<br />
Czarnowski, Justin.................................................... WeCT5.4 3101<br />
.................................................................................. WeDPT10.13<br />
D<br />
*<br />
D'Ambrosio, David.................................................... WeBT8.7 2802<br />
D'Andrea, Raffaello .................................................. ThDT6.1 5113<br />
.................................................................................. ThDT8.3 5179<br />
Daepp, Hannes......................................................... ThBT8.5 4556<br />
.................................................................................. ThCPT10.23 *<br />
Dagnino, Giulio......................................................... TuBT5.8 1359<br />
Dahl, Kristen............................................................. WeCT6.4 3140<br />
.................................................................................. WeDPT10.16 *<br />
Dahmen, Christian.................................................... MoAT1.4 19<br />
.................................................................................. MoBPT10.1 *<br />
.................................................................................. TuBT3.5 1297<br />
.................................................................................. TuCPT10.8 *<br />
Dalamagkidis, Konstantinos ..................................... TuAT7.8 1063<br />
.................................................................................. ThAT9.5 4148<br />
.................................................................................. ThBPT10.26 *<br />
DallaLibera, Fabio .................................................... WeDT3.6 3480<br />
.................................................................................. ThAPT10.9 *<br />
Dallej, Tej.................................................................. WeBT9.7 2855<br />
Daly, John Michael ................................................... ThCT8.6 4961<br />
.................................................................................. ThDPT10.24 *<br />
Damani, Aayush ....................................................... MoBT1.5 445<br />
.................................................................................. TuAPT10.2 *<br />
Danès, Patrick .......................................................... MoAT3 C<br />
.................................................................................. MoAT3 O<br />
.................................................................................. MoAT3.1 *<br />
.................................................................................. MoAT3.7 137<br />
.................................................................................. MoBT3 O<br />
Daniel, Bruce............................................................ WeBT3.8 2564<br />
Daniilidis, Kostas ...................................................... ThBT6.8 4469<br />
Dansereau, Donald Gilbert....................................... ThBT6.6 4455<br />
.................................................................................. ThCPT10.18 *<br />
Dario, Paolo.............................................................. TuBT3.1 *<br />
.................................................................................. ThCT5.5 4826<br />
.................................................................................. ThDPT10.14 *<br />
Darrell, Trevor........................................................... TuAT1 C<br />
.................................................................................. TuAT1.2 793<br />
.................................................................................. ThCT6.1 *<br />
.................................................................................. ThCT6.7 4877<br />
Das, Aditya ............................................................... TuCT3.7 1699<br />
Das, Aveek ............................................................... ThAT3.4 3874<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–236–<br />
.................................................................................. ThBPT10.7 *<br />
Das, Colin ................................................................. TuAT9.7 1160<br />
Das, Gautham .......................................................... ThCT2.8 4716<br />
Das, Jnaneshwar...................................................... WeCT6.3 3132<br />
Dastoor, Sanjay ........................................................ MoBT9.7 774<br />
David, Philip.............................................................. MoBT2.5 494<br />
.................................................................................. TuAPT10.5 *<br />
.................................................................................. TuCT2.3 1632<br />
Davison, Andrew J.................................................... ThCT6 O<br />
Dawids, Steen .......................................................... TuAT2.2 847<br />
de Almeida, Anibal.................................................... ThBT2.8 4277<br />
de Gea Fernandez, Jose .......................................... ThAT9.3 4134<br />
De Laet, Tinne .......................................................... WeCT4.2 3031<br />
De Lorenzo, Danilo................................................... WeBT4.3 2584<br />
De Luca, Alessandro ................................................ WeCT7 CC<br />
.................................................................................. WeCT7.4 3192<br />
.................................................................................. WeDPT10.19 *<br />
.................................................................................. ThAT7.1 4026<br />
de Menezes Pereira , Arvind A................................. WeCT6.5 3147<br />
de Menezes Pereira, Arvind A.................................. WeDPT10.17 *<br />
de Oliveira, Mauricio................................................. ThBT3.7 4329<br />
De Schutter, Bart ...................................................... WeBT7.3 2729<br />
De Schutter, Joris ..................................................... WeCT4.2 3031<br />
.................................................................................. ThCT2.3 4684<br />
de Vries, Jeroen ....................................................... MoBT1.1 421<br />
Debain, Christophe................................................... ThBT9.1 4569<br />
Decré, Wilm .............................................................. WeCT4.2 3031<br />
Del Prete, Andrea ..................................................... WeDT9.1 3694<br />
Delaney, John........................................................... WeCT6.1 *<br />
Delettre, Anne........................................................... ThDT6.2 5121<br />
Dellaert, Frank .......................................................... WeAT6.8 2242<br />
Dellepiane, Massimo ................................................ TuBT5.8 1359<br />
Delmerico, Jeffrey..................................................... TuCT2.3 1632<br />
DelPreto, Joseph ...................................................... TuBT6.5 1380<br />
.................................................................................. TuCPT10.14 *<br />
Demeester, Eric........................................................ WeCT3.1 2971<br />
Demiris, Yiannis........................................................ WeAT1.3 1980<br />
Demonceaux, Cédric ................................................ ThAT6.6 4006<br />
.................................................................................. ThBPT10.18 *<br />
Denei, Simone .......................................................... WeDT9.1 3694<br />
Deng, Xinyan ............................................................ MoAT5 CC<br />
.................................................................................. MoAT5 O<br />
.................................................................................. MoBT5 O<br />
.................................................................................. MoBT5.6 574<br />
.................................................................................. TuAPT10.12 *<br />
Denny, Jory .............................................................. WeBT5.5 2632<br />
.................................................................................. WeCPT10.14 *<br />
Derenick, Jason........................................................ WeCT8.4 3248<br />
.................................................................................. WeDT8.5 3667<br />
.................................................................................. WeDPT10.22 *<br />
.................................................................................. ThAPT10.20 *<br />
Dermitzakis, Konstantinos ........................................ WeDT3.2 3454<br />
Desai, Jaydev P........................................................ TuAT5 C<br />
.................................................................................. TuAT5 O<br />
.................................................................................. TuBT5 CC<br />
.................................................................................. TuBT5 O<br />
.................................................................................. TuBT5.1 *<br />
.................................................................................. TuCT5 C<br />
.................................................................................. TuCT5 O<br />
Desmaele, Denis ...................................................... TuAT3.6 913<br />
.................................................................................. TuBPT10.9 *<br />
Detry, Renaud .......................................................... TuBT9.7 1554<br />
Dettmann, Alexander................................................ WeDT8.3 3653<br />
Dey, Bir Bikram......................................................... ThDT3.3 5048<br />
Di Caro, Gianni A...................................................... ThCT9.1 4981<br />
.................................................................................. ThCT9.8 5027<br />
Di Lello, Enrico ......................................................... WeCT3.1 2971<br />
Di Mario, Ezequiel .................................................... ThBT4.1 4341<br />
Di Rocco, Maurizio ................................................... ThCT9.5 5007<br />
.................................................................................. ThDPT10.26 *
Di Stefano, Luigi ....................................................... ThCT6.4 4857<br />
.................................................................................. ThDPT10.16 *<br />
Dickmann, Jürgen..................................................... ThBT9.4 4587<br />
.................................................................................. ThCPT10.25 *<br />
Dietrich, Alexander ................................................... WeCT7.5 3199<br />
.................................................................................. WeDPT10.20 *<br />
Dille, Michael ............................................................ ThCT8.4 4947<br />
.................................................................................. ThDPT10.22 *<br />
Diller, Eric D.............................................................. TuBT3.4 1291<br />
.................................................................................. TuCT3.5 1687<br />
.................................................................................. TuCPT10.7 *<br />
.................................................................................. WeAPT10.8 *<br />
Dillmann, Rüdiger..................................................... TuCT6.2 1761<br />
.................................................................................. TuCT6.5 1781<br />
.................................................................................. WeAPT10.14 *<br />
.................................................................................. ThCT1.3 4633<br />
.................................................................................. ThCT5 C<br />
Dimitrov, Dimitar Nikolaev ........................................ TuCT6.7 1797<br />
.................................................................................. WeAT7.8 2292<br />
Ding, Xu Chu ............................................................ WeCT5.2 3087<br />
Dinont, Cédric........................................................... WeDT5.4 3519<br />
.................................................................................. ThAPT10.10 *<br />
Dissanayake, Gamini................................................ TuCT2.4 1640<br />
.................................................................................. WeAPT10.4 *<br />
.................................................................................. WeDT8.8 3686<br />
.................................................................................. ThBT9.7 4608<br />
Dittes, Benjamin ....................................................... TuAT7.1 1015<br />
Dobrzynski, Michal Karol.......................................... TuCT9.1 1913<br />
Doitsidis, Lefteris ...................................................... TuCT2.7 1661<br />
Dolha, Mihai Emanuel .............................................. WeCT7.1 3172<br />
Dollar, Aaron............................................................. TuBT6 C<br />
.................................................................................. TuBT6 O<br />
.................................................................................. TuBT7.3 1420<br />
.................................................................................. TuCT6 O<br />
.................................................................................. TuCT7.3 1823<br />
.................................................................................. WeBT6.1 2660<br />
Dominey, Peter Ford ................................................ WeCT1.5 2895<br />
.................................................................................. WeDPT10.2 *<br />
Dong, Lixin................................................................ TuCT3.8 1705<br />
Dorigo, Marco........................................................... ThCT4.2 4762<br />
.................................................................................. ThCT9.8 5027<br />
Dörr, Jonas............................................................... WeDT3.5 3474<br />
.................................................................................. ThAPT10.8 *<br />
Doth, Klaus-Michael ................................................. ThDT8.1 5166<br />
Douillard, Bertrand.................................................... MoAT6.7 255<br />
Doyle, Courtney........................................................ ThCT8.8 4975<br />
Droge, Greg.............................................................. TuAT9.3 1134<br />
Du Toit, Noel E. ........................................................ WeCT8 C<br />
Ducatelle, Frederick.................................................. ThCT9.1 4981<br />
.................................................................................. ThCT9.8 5027<br />
Duchaine, Vincent .................................................... TuBT6.4 1373<br />
.................................................................................. TuCPT10.13 *<br />
Dudek, Gregory ........................................................ ThDT3.3 5048<br />
.................................................................................. ThDT3.4 5054<br />
Duff, Armin................................................................ TuAT8.8 1115<br />
Duhamel, Pierre-Emile ............................................. TuAT8.6 1099<br />
.................................................................................. TuBPT10.21 *<br />
Dupont, Pierre .......................................................... WeAT3.2 2083<br />
.................................................................................. ThBT7 C<br />
.................................................................................. ThBT7.6 4508<br />
.................................................................................. ThCPT10.21 *<br />
Dupuis, Erick ............................................................ MoBT6.8 631<br />
Duriez, Christian....................................................... WeBT4.7 2608<br />
Durrant-Whyte, Hugh................................................ TuAT2.8 886<br />
.................................................................................. ThBT2.2<br />
E<br />
4236<br />
Eathakota, Vijay........................................................ ThBT3.5 4314<br />
.................................................................................. ThCPT10.8 *<br />
Eck, Laurent ............................................................. ThCT8.1 4929<br />
Edsinger, Aaron........................................................ TuBT6 CC<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–237–<br />
.................................................................................. TuBT6 O<br />
.................................................................................. TuCT6 O<br />
Egerstedt, Magnus ................................................... TuAT9.3 1134<br />
.................................................................................. WeBT8.2 2772<br />
.................................................................................. ThBT8.5 4556<br />
.................................................................................. ThCPT10.23 *<br />
Einhorn, Erik ............................................................. WeBT1.5 2430<br />
Ek, Carl Henrik ......................................................... TuAT6.4 980<br />
.................................................................................. TuBPT10.13 *<br />
.................................................................................. WeAT2.2 2028<br />
Ekaterinaris, John A. ................................................ ThAT7.5 4054<br />
.................................................................................. ThBPT10.20 *<br />
Ekstrand, Fredrik ...................................................... TuCT2.2 1626<br />
Ekström, Mikael ........................................................ TuCT2.2 1626<br />
Elbrechter, Christof................................................... TuBT7.4 1427<br />
.................................................................................. TuCPT10.16 *<br />
Elkmann, Norbert...................................................... WeDT1.3 3355<br />
Emeli, Victor ............................................................. ThCT1.2 4627<br />
Ende, Tobias ............................................................ WeDT1.5 3367<br />
.................................................................................. ThAPT10.2 *<br />
Endo, Gen ................................................................ WeBT1.4 2423<br />
.................................................................................. WeCPT10.1 *<br />
Endo, Mitsuru ........................................................... ThBT9.5 4593<br />
.................................................................................. ThCPT10.26 *<br />
Eng, Dillon ................................................................ TuCT5.1 1711<br />
Engeberg, Erik Daniel............................................... ThBT1.1 4174<br />
Engel, Paulo ............................................................. TuAT9.1 1122<br />
Englsberger, Johannes............................................. ThBT5.7 4420<br />
Ergin, Mehmet Alper................................................. ThCT7.5 4917<br />
.................................................................................. ThDPT10.20 *<br />
Eruhimov, Victor ....................................................... WeCT2.4 2941<br />
.................................................................................. WeDPT10.4 *<br />
Esmaeili Malekabadi, Mohammad............................ ThCT7.3 4905<br />
Esparza, Noe............................................................ ThDT5.3 5100<br />
Espiau, Bernard........................................................ ThCT7 C<br />
Espinosa, Felipe ....................................................... ThAT1.1 3746<br />
Espinoza León, Judith .............................................. ThBT8.1 4528<br />
Esposito, Joel ........................................................... WeAT5.7 2192<br />
Estebanez, Belen ..................................................... WeAT3.8 2121<br />
Etoundi, Appolinaire C.............................................. ThAT7.3 4042<br />
Eustice, Ryan ........................................................... TuCT2 C<br />
.................................................................................. TuCT2.5 1647<br />
.................................................................................. WeAPT10.5 *<br />
.................................................................................. ThBT4.7 4378<br />
Evans, Nathan .......................................................... ThCT1.8 4664<br />
Even, Jani................................................................. MoBT3.6 536<br />
.................................................................................. TuAPT10.9<br />
F<br />
*<br />
Falcó Montesinos, Antonio ....................................... TuCT1.1 1567<br />
Falotico, Egidio ......................................................... ThCT5.5 4826<br />
.................................................................................. ThDPT10.14 *<br />
Fan, Zheng ............................................................... TuCT3.8 1705<br />
Fan, Zhun ................................................................. TuAT2.2 847<br />
Fankhauser, Peter .................................................... WeAT6.5 2223<br />
.................................................................................. WeBPT10.17 *<br />
Fantuzzi, Cesare ...................................................... ThBT9.8 4615<br />
Faria, Ricardo ........................................................... ThBT2.8 4277<br />
Fatikow, Sergej......................................................... MoAT1.4 19<br />
.................................................................................. MoBPT10.1 *<br />
.................................................................................. TuAT3.3 894<br />
.................................................................................. TuBT3.5 1297<br />
.................................................................................. TuCPT10.8 *<br />
Fatovic, Michael........................................................ TuAT3.8 925<br />
Fearing, Ronald ........................................................ WeBT6.3 2674<br />
.................................................................................. ThDT4.4 5080<br />
.................................................................................. ThDT5.1 5087<br />
Felekis, Dimitrios ...................................................... TuAT3.7 919<br />
Felfoul, Ouajdi .......................................................... TuBT3.6 1304<br />
.................................................................................. TuCPT10.9 *<br />
Felisa, Mirko ............................................................. TuCT1.6 1599
.................................................................................. WeAPT10.3 *<br />
Ferber, Jacques........................................................ ThDT3.2 5041<br />
Fernandez, Benito R................................................. WeAT7.4 2267<br />
.................................................................................. WeBPT10.19 *<br />
Ferrante, Eliseo ........................................................ ThCT9.8 5027<br />
Ferrary, Evelyne ....................................................... WeBT3.3 2532<br />
Ferreira, Antoine....................................................... TuBT3.5 1297<br />
.................................................................................. TuBT3.8 1315<br />
.................................................................................. TuCPT10.8 *<br />
Ferreira, Manuel ....................................................... WeAT7.7 2286<br />
Ferreira, Ricardo....................................................... ThCT5.5 4826<br />
.................................................................................. ThDPT10.14 *<br />
Ferretti, Gianni.......................................................... WeCT3.1 2971<br />
Ferrier, Nicola ........................................................... ThDT7.1 5139<br />
Ferrin, Jeffrey ........................................................... WeBT6.5 2688<br />
.................................................................................. WeCPT10.17 *<br />
.................................................................................. ThDT8.2 5173<br />
Ficuciello, Fanny....................................................... TuCT6.4 1775<br />
.................................................................................. WeAPT10.13 *<br />
Fierro, Rafael............................................................ WeAT8.6 2333<br />
.................................................................................. WeBPT10.24 *<br />
Fine, Benjamin.......................................................... ThCT9.4 5001<br />
.................................................................................. ThDPT10.25 *<br />
Finio, Benjamin......................................................... MoAT1.6 31<br />
.................................................................................. MoAT9.3 384<br />
.................................................................................. MoBPT10.3 *<br />
.................................................................................. TuAT8.6 1099<br />
.................................................................................. TuAT8.7 1107<br />
.................................................................................. TuBPT10.21 *<br />
Fink, Jonathan.......................................................... WeCT8.4 3248<br />
.................................................................................. WeDPT10.22 *<br />
Fiorini, Paolo............................................................. TuAT5 CC<br />
.................................................................................. TuAT5 O<br />
.................................................................................. TuAT5.1 *<br />
.................................................................................. TuBT5 C<br />
.................................................................................. TuBT5 O<br />
.................................................................................. TuCT5 CC<br />
.................................................................................. TuCT5 O<br />
Firman, Michael David.............................................. ThAT2.8 3850<br />
Fischer, Jan .............................................................. WeAT9.3 2365<br />
Fisher, Charles ......................................................... MoAT6.8 261<br />
Fjerdingen, Sigurd Aksnes ....................................... MoAT6.6 247<br />
.................................................................................. MoBPT10.18 *<br />
Flacco, Fabrizio ........................................................ ThAT7.1 4026<br />
Flemming, Leslie ...................................................... MoBT9.8 780<br />
Fleps, Michael .......................................................... WeCT9.3 3297<br />
Floreano, Dario......................................................... TuCT9 C<br />
.................................................................................. TuCT9.1 1913<br />
.................................................................................. ThCT9.6 5015<br />
.................................................................................. ThDPT10.27 *<br />
Folio, David............................................................... TuBT3.5 1297<br />
.................................................................................. TuCPT10.8 *<br />
Fontaine, Jean-Guy.................................................. ThAT5.4 3951<br />
.................................................................................. ThBPT10.13 *<br />
Forgoston, Eric ......................................................... ThAT4.3 3905<br />
Forlizzi, Jodi.............................................................. WeAT1.4 1986<br />
.................................................................................. WeBPT10.1 *<br />
Forsman, Pekka ....................................................... MoAT2.2 59<br />
Fourquet, Jean-Yves ................................................ ThAT9.2 4127<br />
Fox, Dieter................................................................ TuAT1.6 821<br />
.................................................................................. TuBPT10.3 *<br />
.................................................................................. ThAT6 CC<br />
.................................................................................. ThBT6 CC<br />
.................................................................................. ThCT6 C<br />
.................................................................................. ThCT6 O<br />
.................................................................................. ThCT6.3 4850<br />
Fox, Michael ............................................................. ThCT4.3 4770<br />
Fraichard, Thierry ..................................................... WeCT3.3 2985<br />
Fraisse, Philippe....................................................... ThCT1.7 4658<br />
Franchi, Antonio ....................................................... MoAT2 CC<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–238–<br />
.................................................................................. MoAT4.5 163<br />
.................................................................................. MoBT2.1 469<br />
.................................................................................. MoBPT10.11 *<br />
.................................................................................. WeAT6.4 2215<br />
.................................................................................. WeBPT10.16 *<br />
.................................................................................. WeCT4.3 3039<br />
Francisco, Gerald ..................................................... TuCT5.1 1711<br />
Frank, Barbara.......................................................... WeAT5.5 2180<br />
.................................................................................. WeBPT10.14 *<br />
Fraundorfer, Friedrich............................................... TuCT2.6 1655<br />
.................................................................................. WeAPT10.6 *<br />
.................................................................................. ThAT6.7 4012<br />
Frazzoli, Emilio ......................................................... MoAT7.6 298<br />
.................................................................................. MoBPT10.21 *<br />
.................................................................................. WeBT5.1 *<br />
.................................................................................. WeDT5.3 3513<br />
.................................................................................. ThBT3.4 4307<br />
.................................................................................. ThCPT10.7 *<br />
Freidovich, Leonid .................................................... ThDT5.2 5094<br />
Frese, Udo................................................................ TuCT1.4 1585<br />
.................................................................................. WeAPT10.1 *<br />
.................................................................................. WeCT9.4 3305<br />
.................................................................................. WeDT2.6 3426<br />
.................................................................................. WeDPT10.25 *<br />
.................................................................................. ThAPT10.6 *<br />
Friedl, Werner........................................................... TuBT6.3 1366<br />
.................................................................................. TuCT7.5 1836<br />
.................................................................................. WeAPT10.17 *<br />
.................................................................................. ThBT1.7 4215<br />
Friedman, Ariell ........................................................ TuBT9.4 1533<br />
.................................................................................. TuCPT10.22 *<br />
Fritz, Mario................................................................ TuAT1.2 793<br />
.................................................................................. ThCT6.7 4877<br />
Fu, Michael J. ........................................................... WeDT3.3 3460<br />
Fu, Yu ....................................................................... MoBT2.3 482<br />
Fu, Zhenbo ............................................................... TuAT3.8 925<br />
Fuchiwaki, Ohmi ....................................................... MoAT9.5 395<br />
.................................................................................. MoBT9 CC<br />
.................................................................................. MoBPT10.26 *<br />
Fujie, Masakatsu G................................................... TuAT5.6 955<br />
.................................................................................. TuBPT10.12 *<br />
Fujiki, Soichiro .......................................................... WeAT7.5 2274<br />
.................................................................................. WeAT7.6 2280<br />
.................................................................................. WeBPT10.20 *<br />
.................................................................................. WeBPT10.21 *<br />
Fujimoto, Hideo ........................................................ WeAT4.6 2145<br />
.................................................................................. WeBPT10.12 *<br />
Fujita, Emi................................................................. TuCT9.6 1946<br />
.................................................................................. WeAPT10.24 *<br />
Fukuda, Toshio......................................................... MoAT1.1 1<br />
.................................................................................. MoBT1.3 433<br />
.................................................................................. MoBT1.4 439<br />
.................................................................................. MoBT1.7 457<br />
.................................................................................. TuAT3.1 *<br />
.................................................................................. TuAPT10.1 *<br />
.................................................................................. TuCT3.6 1693<br />
.................................................................................. WeAT3.7 2115<br />
.................................................................................. WeAPT10.9 *<br />
.................................................................................. ThCT2.1 4670<br />
.................................................................................. ThDT5.2 5094<br />
Fukui, Rui ................................................................. WeDT2.5 3419<br />
.................................................................................. ThAPT10.5 *<br />
Fukushima, Edwardo F............................................. WeBT1.4 2423<br />
.................................................................................. WeCPT10.1 *<br />
Fukushima, Hiroaki................................................... TuCT8.8 1907<br />
Full, Robert ............................................................... MoAT5.1 *<br />
.................................................................................. TuCT8.5 1887<br />
.................................................................................. WeAPT10.20 *<br />
Fumagalli, Matteo ..................................................... ThAT9.7 4161<br />
Funakoshi, Kotaro .................................................... TuBT9.5 1540
.................................................................................. TuCPT10.23 *<br />
Fung, Henry.............................................................. WeAT7.3 2261<br />
Furtuna, Andrei......................................................... ThBT3.6 4321<br />
.................................................................................. ThCPT10.9 *<br />
Furuta, Takayuki....................................................... WeBT2.2<br />
G<br />
2466<br />
Gaboury, Louis ......................................................... TuBT3.6 1304<br />
.................................................................................. TuCPT10.9 *<br />
Gadre, Aditya............................................................ TuAT9.4 1140<br />
.................................................................................. TuBPT10.22 *<br />
Gaillard, François ..................................................... WeDT5.4 3519<br />
.................................................................................. ThAPT10.10 *<br />
Gal, Oren.................................................................. WeDT5.7 3539<br />
Galloway, Kevin........................................................ MoAT1.6 31<br />
.................................................................................. MoBPT10.3 *<br />
Galvez Lopez, Dorian............................................... MoAT2.1 51<br />
.................................................................................. TuBT2.5 1256<br />
.................................................................................. TuBT2.8 1277<br />
.................................................................................. TuCPT10.5 *<br />
Gambardella, Luca ................................................... ThCT9.1 4981<br />
.................................................................................. ThCT9.8 5027<br />
Gans, Nicholas ......................................................... TuBT1 C<br />
.................................................................................. TuBT1.2 1180<br />
Gao, Ce .................................................................... WeAT9.2 2359<br />
Gao, Yixin................................................................. WeBT3.4 2539<br />
.................................................................................. WeCPT10.7 *<br />
Garabini, Manolo ...................................................... ThAT1.5 3770<br />
.................................................................................. ThBPT10.2 *<br />
Garcia, Elena............................................................ TuBT8 C<br />
.................................................................................. TuBT8.8 1507<br />
Garcia Bermudez, Fernando .................................... WeBT6.3 2674<br />
Garney, Ben ............................................................. ThAT2.3 3816<br />
Gaspar, Jose ............................................................ ThCT5.5 4826<br />
.................................................................................. ThDPT10.14 *<br />
Gassert, Roger ......................................................... WeCT4.8 3074<br />
Gattupalli, Aditya ...................................................... ThBT3.5 4314<br />
.................................................................................. ThCPT10.8 *<br />
Gautier, Maxime ....................................................... WeDT9.6 3728<br />
.................................................................................. ThAPT10.24 *<br />
Gayet, Brice.............................................................. TuBT5.4 1333<br />
.................................................................................. TuBT5.5 1339<br />
.................................................................................. TuCPT10.10 *<br />
.................................................................................. TuCPT10.11 *<br />
Gehrig, Dirk .............................................................. ThCT5.4 4819<br />
.................................................................................. ThDPT10.13 *<br />
Geiger, James .......................................................... TuAT5.7 961<br />
Georgiev, Kristiyan ................................................... ThAT2.2 3808<br />
Geraerts, Roland ...................................................... WeDT5.5 3526<br />
.................................................................................. ThAPT10.11 *<br />
German, Christopher R. ........................................... MoAT6.8 261<br />
Gerratt, Aaron P. ...................................................... TuCT3.4 1680<br />
.................................................................................. WeAPT10.7 *<br />
Ghadiok, Vaibhav ..................................................... ThCT1.5 4645<br />
.................................................................................. ThDPT10.2 *<br />
Ghaffari Toiserkan, Kamran ..................................... WeDT9.8 3740<br />
Ghommam, Jawhar .................................................. WeAT8.7 2340<br />
Ghosh, Madhumita ................................................... WeBT1.2 2409<br />
Giardino, Simone...................................................... ThCT6.4 4857<br />
.................................................................................. ThDPT10.16 *<br />
Giertler, Bogumil....................................................... ThAT8.8 4115<br />
Gilbert, Hunter B....................................................... WeBT3.1 2517<br />
Gillies, Andrew G...................................................... ThDT5.1 5087<br />
Gillula, Jeremy.......................................................... WeCT3.2 2979<br />
Girbés, Vicent........................................................... ThBT3.8 4335<br />
Girdhar, Yogesh ....................................................... ThDT3.3 5048<br />
Giri, Nivedhitha......................................................... ThAT7.6 4060<br />
.................................................................................. ThBPT10.21 *<br />
Glasauer, Stefan....................................................... WeDT1.6 3375<br />
.................................................................................. ThAPT10.3 *<br />
Godage, Isuru S. ...................................................... TuAT8.5 1093<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–239–<br />
.................................................................................. TuBPT10.20 *<br />
Godin, Mike .............................................................. WeCT6.3 3132<br />
Goerick, Christian ..................................................... TuAT7.1 1015<br />
Goerner, Martin ........................................................ WeBT2.6 2497<br />
.................................................................................. WeCPT10.6 *<br />
Goeroeg, Attila.......................................................... ThCT5.8 4844<br />
Goldberg, Ken .......................................................... WeBT5.7 2646<br />
Goldfield, Eugene ..................................................... ThBT7.3 4488<br />
Goldin, Jeremy ......................................................... ThCT1.5 4645<br />
.................................................................................. ThDPT10.2 *<br />
Goldman, Roger E.................................................... WeAT9.7 2390<br />
Golombek, Raphael.................................................. WeCT3.7 3011<br />
Gomes, Kevin ........................................................... WeCT6.3 3132<br />
Gong, Zheng............................................................. MoBT3.5 530<br />
.................................................................................. TuAPT10.8 *<br />
Gonzalez, Yolanda ................................................... WeDT6.7 3577<br />
Gonzalez de Santos, Pablo ...................................... TuBT8.8 1507<br />
Goron, Lucian Cosmin.............................................. ThBT2.6 4263<br />
.................................................................................. ThCPT10.6 *<br />
Gosselin, Frederick P. .............................................. TuAT3.4 901<br />
.................................................................................. TuBPT10.7 *<br />
Goswami, Ambarish ................................................. ThAT5 C<br />
.................................................................................. ThAT5 O<br />
.................................................................................. ThAT5.3 3943<br />
Goto, Masataka ........................................................ WeAT1.6 2000<br />
.................................................................................. WeBPT10.3 *<br />
Gouttefarde, Marc..................................................... WeBT9.7 2855<br />
Gowal, Sven ............................................................. WeBT8.1 2765<br />
.................................................................................. WeCT9.5 3313<br />
.................................................................................. WeDPT10.26 *<br />
Graciano Santos, Vinicius ........................................ ThBT4.6 4372<br />
.................................................................................. ThCPT10.12 *<br />
Grand, Christophe .................................................... WeBT7.7 2753<br />
.................................................................................. ThBT4.4 4360<br />
.................................................................................. ThCPT10.10 *<br />
Grange, Sebastien.................................................... WeCT4.1 3023<br />
Gräser, Axel.............................................................. TuAT1.1 786<br />
Grebenstein, Markus ................................................ TuBT6.3 1366<br />
Gregory, John........................................................... WeAT4.4 2133<br />
.................................................................................. WeBPT10.10 *<br />
Grimmer, Martin........................................................ TuCT7.1 1811<br />
Grira, Aymen ............................................................ MoAT1.8 45<br />
Grisetti, Giorgio......................................................... MoAT2.6 86<br />
.................................................................................. MoBPT10.6 *<br />
.................................................................................. TuAT2.5 865<br />
.................................................................................. TuBPT10.5 *<br />
.................................................................................. WeDT9.4 3716<br />
.................................................................................. ThAPT10.22 *<br />
Grocholsky, Ben ....................................................... ThCT8.4 4947<br />
.................................................................................. ThDPT10.22 *<br />
Groeger, Martin ........................................................ ThBT7.5 4502<br />
.................................................................................. ThCPT10.20 *<br />
Gross, Horst-Michael................................................ WeBT1.5 2430<br />
.................................................................................. WeCPT10.2 *<br />
Grunberg, David ....................................................... WeCT1.8 2916<br />
Grupen, Rod ............................................................. WeDT1.2 3349<br />
Grzonka, Slawomir ................................................... TuBT2.4 1249<br />
.................................................................................. TuCPT10.4 *<br />
Gu, Ye ...................................................................... WeCT1.2 2873<br />
Guan, Yisheng.......................................................... TuBT8.3 1473<br />
Guerin, Kelleher........................................................ TuBT5.6 1346<br />
.................................................................................. TuCPT10.12 *<br />
Gueta, Lounell B....................................................... ThCT2.5 4698<br />
.................................................................................. ThDPT10.5 *<br />
Guglielmino, Emanuele ............................................ TuAT8.5 1093<br />
.................................................................................. TuBPT10.20 *<br />
.................................................................................. ThAT7.5 4054<br />
.................................................................................. ThBPT10.20 *<br />
Guitton, Julien........................................................... WeCT1.5 2895<br />
.................................................................................. WeDPT10.2 *
Guo, Shijie................................................................ WeBT1.7 2445<br />
Gupta, S.K................................................................ TuAT9 C<br />
Gupta, Satyandra K.................................................. TuAT9.6 1154<br />
.................................................................................. TuBPT10.24 *<br />
Gurdan, Daniel ......................................................... ThDT8.1 5166<br />
Gustafson, Joakim.................................................... WeDT1.1 3342<br />
Gutierrez, Alvaro....................................................... ThCT9.8<br />
H<br />
5027<br />
Ha, Hyowon.............................................................. WeCT9.2 3290<br />
Ha, Inyong ................................................................ WeCT7.6 3207<br />
.................................................................................. WeDPT10.21 *<br />
Haarnoja, Tuomas .................................................... WeAT7.2 2255<br />
Haberland, Matt........................................................ ThAT5.5 3957<br />
.................................................................................. ThBPT10.14 *<br />
Hach, Oliver.............................................................. ThAT8.1 4072<br />
Hacker, Franz........................................................... TuCT9.4 1933<br />
.................................................................................. WeAPT10.22 *<br />
Haddadin, Sami........................................................ TuCT6.6 1789<br />
.................................................................................. WeAPT10.15 *<br />
.................................................................................. WeDT1.5 3367<br />
.................................................................................. ThAPT10.2 *<br />
Hager, Gregory......................................................... WeAT3.5 2102<br />
.................................................................................. WeBT3.4 2539<br />
.................................................................................. WeBPT10.8 *<br />
.................................................................................. WeCT2.6 2953<br />
.................................................................................. WeCPT10.7 *<br />
.................................................................................. WeDPT10.6 *<br />
Hagita, Norihiro......................................................... MoBT3.6 536<br />
.................................................................................. MoBT3.8 550<br />
.................................................................................. TuAPT10.9 *<br />
.................................................................................. WeDT7.1 3589<br />
Hagiwara, Ichiro........................................................ MoBT3.5 530<br />
.................................................................................. TuAPT10.8 *<br />
Hagiwara, Masaya.................................................... TuBT3.7 1309<br />
Hagn, Ulrich.............................................................. WeCT4.1 3023<br />
Haines, Justin........................................................... MoAT6.3 227<br />
Halasz, Adam ........................................................... ThAT4.6 3923<br />
.................................................................................. ThBPT10.12 *<br />
Haliyo, Dogan Sinan................................................. TuAT3.3 894<br />
Halme, Aarne J......................................................... WeAT7.2 2255<br />
Hamann, Katharina................................................... WeCT1.5 2895<br />
.................................................................................. WeDPT10.2 *<br />
Hamano, Seiya......................................................... WeDT2.2 3401<br />
Hammer, Peter ......................................................... TuBT5.3 1327<br />
Han, Chang-Soo....................................................... ThDT6 CC<br />
.................................................................................. ThDT6.3 5127<br />
Han, Jungsoo ........................................................... ThDT6.3 5127<br />
Han, Misop ............................................................... TuAT5.4 943<br />
.................................................................................. TuBPT10.10 *<br />
Han, Shuo................................................................. WeCT3.8 3017<br />
Han, Zhou................................................................. MoBT5.5 568<br />
.................................................................................. TuAPT10.11 *<br />
Handzhiyski, Nikolay ................................................ WeDT5.8 3545<br />
Häne, Christian......................................................... TuCT2.1 1618<br />
Hanebeck, Uwe D..................................................... WeAT2.5 2050<br />
.................................................................................. WeBPT10.5 *<br />
.................................................................................. WeCT4.5 3053<br />
.................................................................................. WeDPT10.11 *<br />
.................................................................................. ThCT5.4 4819<br />
.................................................................................. ThDPT10.13 *<br />
Hanheide, Marc ........................................................ WeCT3.7 3011<br />
.................................................................................. WeDT1.8 3387<br />
Hansen, Peter........................................................... ThAT6.8 4020<br />
Hanson, Allen ........................................................... WeDT1.2 3349<br />
Hanus, Jean Luc....................................................... TuBT3.8 1315<br />
Hao, Ning.................................................................. WeCT2.2 2928<br />
Hara, Masayuki......................................................... ThCT1.8 4664<br />
Harada, Tatsuya....................................................... TuBT1.7 1214<br />
Haraguchi, Daisuke .................................................. TuAT5.2 931<br />
Harata, Yuji............................................................... WeBT7.4 2735<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–240–<br />
.................................................................................. WeCPT10.19 *<br />
Hardy, Jason ............................................................ TuCT1.7 1605<br />
Haschke, Robert....................................................... TuBT7.4 1427<br />
.................................................................................. TuCPT10.16 *<br />
.................................................................................. WeCT2.5 2947<br />
.................................................................................. WeDPT10.5 *<br />
Hasegawa, Hiroaki ................................................... TuCT9.7 1954<br />
Hasegawa, Osamu ................................................... ThAT6.5 3998<br />
.................................................................................. ThBPT10.17 *<br />
Hasegawa, Tsutomu................................................. WeAT2.1 2020<br />
.................................................................................. ThBT1.5 4201<br />
.................................................................................. ThCPT10.2 *<br />
Hasegawa, Yasuhisa................................................ TuCT5.5 1737<br />
.................................................................................. WeAPT10.11 *<br />
.................................................................................. ThCT7.2 4899<br />
Häselich, Marcel ....................................................... WeDT5.8 3545<br />
Hashimoto, Kenji ...................................................... WeCT7.8 3221<br />
Hashimoto, Koichi..................................................... MoAT1 C<br />
.................................................................................. MoAT1.2 7<br />
Hassan Zahraee, Ali ................................................. TuBT5.4 1333<br />
.................................................................................. TuCPT10.10 *<br />
Hassen, Salhi ........................................................... WeCT3.3 2985<br />
Hassenzahl, Marc..................................................... WeDT1.5 3367<br />
Hatakeyama, Tomofumi ........................................... ThCT1.6 4652<br />
.................................................................................. ThDPT10.3 *<br />
Hatton, Ross............................................................. ThAT3.7 3893<br />
Hauert, Sabine.......................................................... ThCT9.6 5015<br />
.................................................................................. ThDPT10.27 *<br />
Hauser, Helmut......................................................... ThAT9.8 4168<br />
Hausknecht, Matthew ............................................... ThBT9.3 4581<br />
Hawes, Nick.............................................................. WeDT1.8 3387<br />
Hawkes, Elliot Wright................................................ ThDT5.3 5100<br />
Hayakawa, Yoshikazu .............................................. WeBT1.7 2445<br />
Hayashi, Atsushi....................................................... ThBT5.4 4400<br />
.................................................................................. ThCPT10.13 *<br />
Hayashi, Yoshiaki ..................................................... TuCT5.8 1755<br />
Hayashibe, Mitsuhiro ................................................ TuCT5.4 1731<br />
.................................................................................. WeAPT10.10 *<br />
.................................................................................. WeDT9.2 3701<br />
Healey, Anthony J. ................................................... WeCT6.6 3154<br />
.................................................................................. WeDPT10.18 *<br />
Heckmann, Martin .................................................... WeCT3.7 3011<br />
Hehn, Markus ........................................................... ThDT8.3 5179<br />
Heidarsson, Hordur K ............................................... WeCT6.7 3160<br />
Heimlich, Christian.................................................... ThDT4.3 5073<br />
Hellum, Aren............................................................. ThCT3.6 4749<br />
.................................................................................. ThDPT10.9 *<br />
Helmer, Patrick ......................................................... WeCT4.1 3023<br />
Heng, Lionel ............................................................. ThAT6.7 4012<br />
Hengst, Bernhard ..................................................... MoBT8.7 732<br />
Hennes, Daniel ......................................................... ThBT2.4 4249<br />
.................................................................................. ThCPT10.4 *<br />
Heracleous, Panikos................................................. MoBT3.6 536<br />
.................................................................................. TuAPT10.9 *<br />
Herbst, Evan............................................................. ThCT6.3 4850<br />
Herman, Benoît ........................................................ TuBT5.4 1333<br />
.................................................................................. TuBT5.5 1339<br />
.................................................................................. TuCPT10.10 *<br />
.................................................................................. TuCPT10.11 *<br />
Hermans, Tucker ...................................................... ThCT1.2 4627<br />
Hernandez Arieta, Alejandro .................................... WeDT3.2 3454<br />
Hernandez-Gutierrez, Andres................................... ThBT9.6 4601<br />
.................................................................................. ThCPT10.27 *<br />
Hershberger, Andrew D............................................ WeDT3.3 3460<br />
Hershey, John R....................................................... ThCT2.4 4690<br />
.................................................................................. ThDPT10.4 *<br />
Hertkorn, Katharina .................................................. TuCT6.3 1768<br />
Heyer, Clint............................................................... MoAT6.4 235<br />
.................................................................................. MoBPT10.16 *<br />
Heyneman, Barrett ................................................... TuBT6.4 1373
.................................................................................. TuCPT10.13 *<br />
Higashimori, Mitsuru................................................. TuBT6.6 1386<br />
.................................................................................. TuBT6.7 1392<br />
.................................................................................. TuCPT10.15 *<br />
.................................................................................. ThAT7.4 4048<br />
.................................................................................. ThBPT10.19 *<br />
Higuchi, Toshiro........................................................ ThCT1.8 4664<br />
Hilario Pérez, Lucía .................................................. TuCT1.1 1567<br />
Hilsenbeck, Barbara ................................................. WeCT1.6 2903<br />
.................................................................................. WeDPT10.3 *<br />
Himmelsbach, Michael ............................................. ThBT8.6 4562<br />
.................................................................................. ThCPT10.24 *<br />
Hirai, Hiroaki............................................................. ThBT7.4 4496<br />
.................................................................................. ThCPT10.19 *<br />
Hirai, Shinichi............................................................ TuCT9.6 1946<br />
.................................................................................. WeAPT10.24 *<br />
Hirano, Shinya.......................................................... WeBT1.7 2445<br />
Hirata, Yasuhisa ....................................................... MoAT7 C<br />
.................................................................................. MoAT7.8 311<br />
.................................................................................. ThBT9.5 4593<br />
.................................................................................. ThCPT10.26 *<br />
Hirche, Sandra.......................................................... MoAT8.5 344<br />
.................................................................................. MoBPT10.23 *<br />
.................................................................................. WeBT1.3 2416<br />
Hirose, Shigeo.......................................................... MoAT5.2 *<br />
.................................................................................. TuAT8.3 1081<br />
.................................................................................. TuPL.1 *<br />
.................................................................................. WeBT1.4 2423<br />
.................................................................................. WeCPT10.1 *<br />
Hirschmüller, Heiko .................................................. WeBT2.6 2497<br />
.................................................................................. WeCPT10.6 *<br />
Hirzinger, Gerd ......................................................... MoAT4.7 177<br />
.................................................................................. TuAT6.3 973<br />
.................................................................................. TuAT7.7 1055<br />
.................................................................................. TuPL.1 *<br />
.................................................................................. TuCT6.3 1768<br />
.................................................................................. TuCT7.5 1836<br />
.................................................................................. WeAT4.8 2158<br />
.................................................................................. WeAPT10.17 *<br />
.................................................................................. WeCT4.1 3023<br />
.................................................................................. WeDT9.3 3708<br />
.................................................................................. ThBT5.7 4420<br />
.................................................................................. ThBT7.5 4502<br />
.................................................................................. ThCPT10.20 *<br />
Ho, Sean................................................................... MoBT2.5 494<br />
.................................................................................. TuAPT10.5 *<br />
Ho, Van..................................................................... TuCT9.6 1946<br />
.................................................................................. WeAPT10.24 *<br />
Hochgeschwender, Nico........................................... TuAT7.3 1030<br />
Hodgson, Sean......................................................... MoBT9.1 738<br />
Hoepflinger, Mark ..................................................... MoBT5.4 562<br />
.................................................................................. TuAPT10.10 *<br />
Hoeppner, Hannes ................................................... TuCT7.5 1836<br />
.................................................................................. WeAPT10.17 *<br />
Hoff, Nicholas ........................................................... ThCT9.2 4989<br />
Hoffman, Judy .......................................................... WeBT5.7 2646<br />
Hoffman, Katie.......................................................... TuBT8.4 1479<br />
.................................................................................. TuCPT10.19 *<br />
Holenstein, Claude ................................................... ThAT2.5 3830<br />
.................................................................................. ThBPT10.5 *<br />
Hollinger, Geoffrey.................................................... WeDT6.5 3564<br />
.................................................................................. ThAPT10.14 *<br />
.................................................................................. ThBT8 CC<br />
Holz, Dirk.................................................................. ThBT2.4 4249<br />
.................................................................................. ThCPT10.4 *<br />
Homma, Michio......................................................... MoAT1.1 1<br />
.................................................................................. TuCT3.6 1693<br />
.................................................................................. WeAPT10.9 *<br />
Hong, Dennis............................................................ ThAT5.6 3963<br />
.................................................................................. ThBPT10.15 *<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–241–<br />
Hoon, Kay Hiang ...................................................... ThCT7.6 4923<br />
.................................................................................. ThDPT10.21 *<br />
Hoover, Randy.......................................................... WeCT2.2 2928<br />
Horchler, Andrew...................................................... MoAT5.4 197<br />
.................................................................................. MoBPT10.13 *<br />
Hornung, Armin ........................................................ ThCT5.8 4844<br />
Hoshino, Satoshi ...................................................... WeAT8.3 2314<br />
.................................................................................. WeBT8.8 2810<br />
Hosoda, Koh............................................................. ThCT5.7 4838<br />
Hosoi, Anette ............................................................ ThAT3.7 3893<br />
Howard, Andrew ....................................................... ThBT6.2 *<br />
Howe, Robert D. ....................................................... TuBT5.3 1327<br />
.................................................................................. WeDT3.4 3468<br />
.................................................................................. ThAPT10.7 *<br />
Hrabar, Stefan .......................................................... ThCT8 C<br />
.................................................................................. ThCT8.7 4967<br />
Hsieh, M. Ani ............................................................ ThAT4 C<br />
.................................................................................. ThAT4 O<br />
.................................................................................. ThBT4 C<br />
.................................................................................. ThBT4 O<br />
.................................................................................. ThCT4 O<br />
.................................................................................. ThCT4.1 *<br />
.................................................................................. ThCT4.6 4790<br />
.................................................................................. ThDPT10.12 *<br />
Hu, Chengzhi............................................................ MoBT1.4 439<br />
.................................................................................. TuAPT10.1 *<br />
Hu, Tianjiang ............................................................ MoBT5.5 568<br />
.................................................................................. TuAPT10.11 *<br />
Hu, Yonghui.............................................................. TuCT8.1 1863<br />
Hu, Zheng................................................................. MoBT5.6 574<br />
.................................................................................. TuAPT10.12 *<br />
Huang, Guoquan ...................................................... MoAT2.3 65<br />
Huang, Han-Pang..................................................... MoAT9 C<br />
.................................................................................. MoAT9.1 372<br />
Huang, Jian .............................................................. ThCT2.1 4670<br />
Huang, Ke................................................................. MoAT4.3 149<br />
Huang, Ke Jung........................................................ TuBT8.6 1493<br />
.................................................................................. TuCPT10.21 *<br />
Huang, Shoudong..................................................... WeDT8.8 3686<br />
Huang, Shuo............................................................. TuCT2.8 1668<br />
Huang, Tzu-Hao ....................................................... MoAT9.1 372<br />
Huang, Yanjiang ....................................................... ThCT2.5 4698<br />
.................................................................................. ThDPT10.5 *<br />
Huang, Yanlong........................................................ WeDT2.7 3434<br />
Huang, Yazhou......................................................... WeBT5.8 2653<br />
Huber, Daniel............................................................ WeCT9.1 3283<br />
Huber, Markus .......................................................... WeDT1.6 3375<br />
.................................................................................. ThAPT10.3 *<br />
Hudas, Greg ............................................................. ThAT8.4 4091<br />
.................................................................................. ThBPT10.22 *<br />
Huebner, Kai............................................................. TuAT6.4 980<br />
.................................................................................. TuBPT10.13 *<br />
Huhle, Benjamin ....................................................... ThBT2.1 4229<br />
Huihua, Zhao ............................................................ TuCT5.3 1723<br />
Hunt, Alexander J ..................................................... MoAT5.6 209<br />
.................................................................................. MoBPT10.15 *<br />
Huntsberger, Terry ................................................... ThCT3 CC<br />
.................................................................................. ThCT3.3 4728<br />
Huo, Xiaoming .......................................................... WeDT5.2 3507<br />
.................................................................................. WeDT5.6 3533<br />
.................................................................................. ThAPT10.12 *<br />
Hurst, Jonathan ........................................................ ThAT1 CC<br />
.................................................................................. ThAT1.3 3758<br />
Hürzeler, Christoph................................................... WeBT6.6 2694<br />
.................................................................................. WeCPT10.18 *<br />
Hutchinson, Seth ...................................................... WeAT2.8 2070<br />
.................................................................................. WeBT9 CC<br />
.................................................................................. WeBT9.1 2817<br />
Hutter, Marco............................................................ MoBT5.4 562<br />
.................................................................................. TuAPT10.10 *
Hyon, Sang-Ho......................................................... ThAT5.8<br />
I<br />
3975<br />
Iagnemma, Karl ........................................................ MoAT7.6 298<br />
.................................................................................. MoBPT10.21 *<br />
.................................................................................. ThAT8 C<br />
.................................................................................. ThAT8.4 4091<br />
.................................................................................. ThBPT10.22 *<br />
Ichikawa, Akihiko ...................................................... MoAT1.5 25<br />
.................................................................................. MoBPT10.2 *<br />
Ideta, Aiko................................................................. WeBT7.1 2715<br />
Igarashi, Yasunobu................................................... MoAT1.2 7<br />
Iida, Fumiya .............................................................. ThDT5 C<br />
.................................................................................. ThDT5.4 5107<br />
Iimura, Taiki .............................................................. ThBT7.4 4496<br />
.................................................................................. ThCPT10.19 *<br />
Ijspeert, Auke............................................................ TuCT8.7 1901<br />
Ikeda, Atsutoshi........................................................ WeAT4.3 2127<br />
Ikeda, Seiichi ............................................................ MoBT1.4 439<br />
.................................................................................. TuAPT10.1 *<br />
.................................................................................. WeAT3.7 2115<br />
Ikeda, Takeshi .......................................................... ThDT6.4 5133<br />
Ikeda, Tetsushi ......................................................... WeDT7.1 3589<br />
Ikedo, Norio .............................................................. WeAT4.6 2145<br />
.................................................................................. WeBPT10.12 *<br />
Iliev, Boyko ............................................................... TuCT6.7 1797<br />
Iliopoulos, Konstantinos............................................ WeAT9.7 2390<br />
Imamura, Nobuaki .................................................... TuCT5.2 1717<br />
Imura, Jun-ichi .......................................................... MoAT3.2 106<br />
.................................................................................. MoAT3.6 131<br />
.................................................................................. MoBPT10.9 *<br />
Inahara, Tomoyuki.................................................... TuBT6.7 1392<br />
Inamura, Tetsunari ................................................... WeDT1 C<br />
.................................................................................. WeDT1.7 3381<br />
Ince, Gokhan ............................................................ MoAT3.2 106<br />
.................................................................................. MoAT3.6 131<br />
.................................................................................. MoAT3.8 143<br />
.................................................................................. MoBPT10.9 *<br />
Inoue, Keita .............................................................. ThBT7.4 4496<br />
.................................................................................. ThCPT10.19 *<br />
Inoue, Ryosuke......................................................... WeBT7.6 2747<br />
.................................................................................. WeCPT10.21 *<br />
Iocchi, Luca .............................................................. WeBT8.6 2796<br />
.................................................................................. WeCPT10.24 *<br />
Iribe, Masatsugu ....................................................... WeBT1.4 2423<br />
.................................................................................. WeCPT10.1 *<br />
Ishi, Carlos Toshinori................................................ MoBT3.6 536<br />
.................................................................................. MoBT3.8 550<br />
.................................................................................. TuAPT10.9 *<br />
Ishigami, Genya........................................................ MoBT6.3 601<br />
.................................................................................. ThAT8.4 4091<br />
.................................................................................. ThBPT10.22 *<br />
Ishiguro, Akio............................................................ TuCT8 C<br />
.................................................................................. TuCT8.3 1875<br />
.................................................................................. TuCT8.4 1881<br />
.................................................................................. TuCT8.6 1895<br />
.................................................................................. WeAPT10.19 *<br />
.................................................................................. WeAPT10.21 *<br />
Ishiguro, Hiroshi........................................................ MoBT3.8 550<br />
.................................................................................. WeCT1.1 2867<br />
.................................................................................. WeDT3.6 3480<br />
.................................................................................. ThAPT10.9 *<br />
Ishii, Idaku ................................................................ TuBT1.6 1208<br />
.................................................................................. TuCPT10.3 *<br />
Ishikawa, Jun............................................................ WeAT4.7 2151<br />
Ishikawa, Masatoshi ................................................. TuCT9.7 1954<br />
Iskakov, Renat.......................................................... ThBT7.5 4502<br />
.................................................................................. ThCPT10.20 *<br />
Isler, Volkan.............................................................. MoBT2.4 488<br />
.................................................................................. TuAPT10.4 *<br />
Isom, Taylor.............................................................. ThCT8.8 4975<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–242–<br />
Ito, Masaki ................................................................ MoBT1.3 433<br />
Itohara, Tatsuhiko..................................................... MoAT3.4 118<br />
.................................................................................. MoBPT10.7 *<br />
Itti, Laurent................................................................ WeAT9.8 2397<br />
Ivaldi, Serena............................................................ ThBT4.3 4354<br />
Iwahashi, Naoto........................................................ MoAT8.6 350<br />
.................................................................................. MoBPT10.24 *<br />
.................................................................................. TuBT9.2 1520<br />
.................................................................................. TuBT9.5 1540<br />
.................................................................................. TuCPT10.23 *<br />
Iwasa, Shingo ........................................................... TuCT7.6 1843<br />
.................................................................................. WeAPT10.18 *<br />
Iwashita, Yumi .......................................................... WeAT2.1 2020<br />
Iwata, Hiroyasu......................................................... WeDT9.7 3734<br />
Izutsu, Masaki........................................................... WeAT4.7<br />
J<br />
2151<br />
Jääskeläinen, Mirva.................................................. TuAT3.5 907<br />
.................................................................................. TuBPT10.8 *<br />
Jahn, Benjamin......................................................... TuAT6.3 973<br />
Jahya, Alex ............................................................... WeBT3.7 2557<br />
Jaillet, Leonard ......................................................... WeBT5.7 2646<br />
Jäkel, Rainer............................................................. ThCT1.3 4633<br />
Jakuba, Michael........................................................ MoAT6.8 261<br />
.................................................................................. TuBT2.3 1242<br />
.................................................................................. ThCT3.2 4722<br />
Jaleel, Hassan .......................................................... WeBT8.2 2772<br />
Janicek, Miroslav ...................................................... WeDT1.8 3387<br />
Janoch, Allison ......................................................... TuAT1.2 793<br />
Jäntsch, Michael....................................................... TuAT7.8 1063<br />
.................................................................................. ThAT9.5 4148<br />
.................................................................................. ThBPT10.26 *<br />
Jarquín, Gerardo ...................................................... TuCT1.8 1612<br />
Jarrault, Pierre .......................................................... WeBT7.7 2753<br />
Jens, Kristensen ....................................................... TuAT2.2 847<br />
Jensfelt, Patric .......................................................... TuBT9.1 1513<br />
Jentoft, Leif ............................................................... WeDT3.4 3468<br />
Jentoft, Leif P............................................................ ThAPT10.7 *<br />
Jeong, Jaeseung ...................................................... MoBT7.8 685<br />
Jeong, Jin Hyeok ...................................................... TuAT5.8 967<br />
Jeong, Yekeun.......................................................... WeCT9.2 3290<br />
.................................................................................. ThBT6.3 4436<br />
Jia, Peifa................................................................... WeAT9.2 2359<br />
Jia, Yangqing............................................................ TuAT1.2 793<br />
Jia, Yunyi .................................................................. MoAT4.6 171<br />
.................................................................................. MoAT4.8 184<br />
.................................................................................. MoBPT10.12 *<br />
Jia, Zhenzhong ......................................................... WeCT3.6 3004<br />
.................................................................................. WeDPT10.9 *<br />
Jiang, Jinn-Feng ....................................................... TuCT9.8 1962<br />
Jiang, Li .................................................................... TuBT8.3 1473<br />
Jo, Sungho ............................................................... MoBT7.8 685<br />
Joerg, Stefan ............................................................ TuAT7.7 1055<br />
Jog, Amod ................................................................ WeBT3.4 2539<br />
.................................................................................. WeCPT10.7 *<br />
Johansson, Rolf........................................................ ThCT2 CC<br />
.................................................................................. ThCT2.6 4704<br />
.................................................................................. ThDPT10.6 *<br />
Johns, Edward.......................................................... MoBT3.7 542<br />
Johnson, Chris.......................................................... ThCT8.8 4975<br />
Johnson, David......................................................... MoBT9.8 780<br />
Johnson-Roberson, Matthew.................................... WeDT1.1 3342<br />
Joly, Cyril .................................................................. WeCT9.6 3320<br />
.................................................................................. WeDPT10.27 *<br />
Jones, Burton ........................................................... WeCT6.5 3147<br />
.................................................................................. WeDPT10.17 *<br />
Jonker, Pieter ........................................................... TuAT6.6 995<br />
.................................................................................. TuBPT10.15 *<br />
Jørgensen, Jimmy Alison ......................................... TuAT6.5 987<br />
.................................................................................. TuBPT10.14 *<br />
Jouvencel, Bruno...................................................... WeDT6.4 3558
.................................................................................. ThDT3.2 5041<br />
Ju, Wendy................................................................. WeBT1.8 2452<br />
Julian, Brian.............................................................. ThDT8.4 5187<br />
Julier, Simon Justin .................................................. ThAT2.8 3850<br />
Jung, Eui-jung........................................................... WeDT7.2 3595<br />
Jung, Jinwoo............................................................. ThDT7.1 5139<br />
Jung, Jiyoung ........................................................... WeCT9.2<br />
K<br />
3290<br />
Kadivar, Zahra.......................................................... TuCT5.1 1711<br />
Kagami, Satoshi ....................................................... ThBT5 O<br />
.................................................................................. ThCT5 O<br />
Kahn, Jeff ................................................................. MoBT5.7 580<br />
Kajita, Shuuji............................................................. WeAT1.6 2000<br />
.................................................................................. WeBPT10.3 *<br />
.................................................................................. ThAT5.1 *<br />
.................................................................................. ThBT5.3 4392<br />
.................................................................................. ThBT5.8 4428<br />
Kalakrishnan, Mrinal................................................. MoAT8.2 325<br />
.................................................................................. MoAT8.8 365<br />
.................................................................................. ThCT1.4 4639<br />
.................................................................................. ThDPT10.1 *<br />
Kallem, Vinutha ........................................................ WeCT5.8 3126<br />
Kallman, Jason......................................................... ThCT8.8 4975<br />
Kallmann, Marcelo.................................................... WeBT5.8 2653<br />
Kam, Moshe ............................................................. WeAT8.2 2306<br />
Kamamichi, Norihiro ................................................. WeAT4.7 2151<br />
Kamezaki, Mitsuhiro ................................................. WeDT9.7 3734<br />
Kammel, Sören......................................................... TuBT7.2 1413<br />
Kanade, Takeo ......................................................... WeCT9.1 3283<br />
Kanani, Keyvan ........................................................ MoBT6.6 619<br />
.................................................................................. TuAPT10.15 *<br />
Kanbayashi, Takashi ................................................ ThBT9.5 4593<br />
.................................................................................. ThCPT10.26 *<br />
Kanehira, Noriyuki .................................................... ThBT5.4 4400<br />
.................................................................................. ThCPT10.13 *<br />
Kanehiro, Fumio ....................................................... ThBT5.3 4392<br />
.................................................................................. ThBT5.4 4400<br />
.................................................................................. ThBT5.8 4428<br />
.................................................................................. ThCPT10.13 *<br />
Kaneko, Kenji ........................................................... ThBT5.3 4392<br />
.................................................................................. ThBT5.4 4400<br />
.................................................................................. ThBT5.8 4428<br />
.................................................................................. ThCPT10.13 *<br />
Kaneko, Makoto........................................................ TuBT6.6 1386<br />
.................................................................................. TuBT6.7 1392<br />
.................................................................................. TuCPT10.15 *<br />
.................................................................................. ThAT7.4 4048<br />
.................................................................................. ThBPT10.19 *<br />
Kang, Rongjie........................................................... ThAT7.5 4054<br />
.................................................................................. ThBPT10.20 *<br />
Kang, Sungchul ........................................................ TuCT7.8 1857<br />
Kano, Takeshi........................................................... TuCT8.3 1875<br />
.................................................................................. TuCT8.4 1881<br />
.................................................................................. TuCT8.6 1895<br />
.................................................................................. WeAPT10.19 *<br />
.................................................................................. WeAPT10.21 *<br />
Kanou, Kazuki .......................................................... TuAT5.6 955<br />
.................................................................................. TuBPT10.12 *<br />
Kanoulas, Dimitrios................................................... TuBT7.6 1439<br />
.................................................................................. TuCPT10.18 *<br />
Kantor, George......................................................... MoBT2.3 482<br />
.................................................................................. TuBT5.7 1353<br />
.................................................................................. ThDT7.2 5147<br />
Kao, Ching-Chung.................................................... WeCT2.8 2965<br />
Kapadia, Apoorva..................................................... TuAT8.4 1087<br />
.................................................................................. TuBPT10.19 *<br />
Kapoor, Ankur........................................................... MoBT7.1 639<br />
Kappler, Daniel......................................................... TuCT6.2 1761<br />
Karaman, Sertac....................................................... WeDT5.3 3513<br />
.................................................................................. ThBT3.4 4307<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–243–<br />
.................................................................................. ThCPT10.7 *<br />
Karaoguz, Cem......................................................... TuBT1.3 1187<br />
Karayev, Sergey ....................................................... TuAT1.2 793<br />
Karino, Takahiro ....................................................... ThBT8.4 4550<br />
.................................................................................. ThCPT10.22 *<br />
Karpelson, Michael ................................................... ThDT4.3 5073<br />
Karssen, Daniël ........................................................ ThAT5.5 3957<br />
.................................................................................. ThBPT10.14 *<br />
Kashioka, Hideki....................................................... MoAT8.6 350<br />
.................................................................................. MoBPT10.24 *<br />
Kashiwazaki, Koshi................................................... ThBT9.5 4593<br />
.................................................................................. ThCPT10.26 *<br />
Katayama, Daiki ....................................................... WeAT7.6 2280<br />
.................................................................................. WeBPT10.21 *<br />
Kato, Shin ................................................................. ThAT8.7 4109<br />
Katsev, Max.............................................................. WeBT2.8 2511<br />
Katsuki, Yoshio......................................................... ThDT6.4 5133<br />
Kavraki, Lydia ........................................................... ThCT1.1 4621<br />
Kawahara, Tomohiro ................................................ TuBT3.7 1309<br />
Kawakami, Daiki ....................................................... MoAT1.5 25<br />
.................................................................................. MoBPT10.2 *<br />
Kawamura, Akihiro ................................................... ThBT1.5 4201<br />
.................................................................................. ThCPT10.2 *<br />
Kawamura, Sadao .................................................... ThCT3.7 4756<br />
Kawashima, Kenji ..................................................... TuAT5.2 931<br />
Kawato, Mitsuo ......................................................... ThAT5.8 3975<br />
Kazakidi, Asimina ..................................................... ThAT7.5 4054<br />
.................................................................................. ThBPT10.20 *<br />
Kazanzides, Peter .................................................... MoBT7.1 639<br />
Kazerooni, Homayoon .............................................. ThCT7.4 4911<br />
.................................................................................. ThDPT10.19 *<br />
Kazmitcheff, Guillaume............................................. WeBT3.3 2532<br />
Keith, François.......................................................... ThAT3.6 3887<br />
.................................................................................. ThBPT10.9 *<br />
Keller, Thomas ......................................................... WeDT1.8 3387<br />
Kelly, Alonzo............................................................. MoAT6 O<br />
.................................................................................. MoBT6 O<br />
.................................................................................. WeAT5.4 2172<br />
.................................................................................. WeBPT10.13 *<br />
Kelly, Jonathan ......................................................... WeCT9 C<br />
Kendoul, Farid .......................................................... ThCT8.5 4953<br />
.................................................................................. ThDPT10.23 *<br />
Kermani, Mehrdad R. ............................................... WeBT3.6 2551<br />
.................................................................................. WeCPT10.9 *<br />
Kermorgant, Olivier................................................... MoBT9.6 768<br />
.................................................................................. TuAPT10.24 *<br />
.................................................................................. WeBT9.6 2849<br />
.................................................................................. WeCPT10.27 *<br />
Kersbergen, Bart ...................................................... WeBT7.3 2729<br />
Keshmiri, Mehdi........................................................ ThAT1.2 3752<br />
Keuning, Jasper David ............................................. MoBT1.1 421<br />
Khansari-Zadeh, Seyed Mohammad........................ MoBT8.4 710<br />
.................................................................................. TuAPT10.19 *<br />
Khatib, Oussama ...................................................... TuAT7.4 1036<br />
.................................................................................. TuBPT10.16 *<br />
.................................................................................. TuCT7.4 1830<br />
.................................................................................. WeAPT10.16 *<br />
.................................................................................. WeCT3.4 2992<br />
.................................................................................. WeCT3.5 2998<br />
.................................................................................. WeDPT10.7 *<br />
.................................................................................. WeDPT10.8 *<br />
Kheddar, Abderrahmane .......................................... ThAT3.6 3887<br />
.................................................................................. ThBT5.6 4414<br />
.................................................................................. ThBPT10.9 *<br />
.................................................................................. ThCPT10.15 *<br />
Khushaba, Rami ....................................................... ThBT9.7 4608<br />
Kielhöfer, Simon ....................................................... TuCT9.4 1933<br />
.................................................................................. WeAPT10.22 *<br />
Kiesler, Sara ............................................................. WeAT1.4 1986<br />
.................................................................................. WeBPT10.1 *
Kiguchi, Kazuo.......................................................... TuCT5.8 1755<br />
Kim, Ayoung ............................................................. TuCT2.5 1647<br />
.................................................................................. WeAPT10.5 *<br />
Kim, Chunwoo .......................................................... TuAT5.4 943<br />
.................................................................................. TuBPT10.10 *<br />
Kim, Chyon Hae ....................................................... ThAT1.8 3792<br />
Kim, Dongwook ........................................................ TuCT3.3 1674<br />
Kim, Eunyoung ......................................................... ThAT2.1 3800<br />
Kim, H. Jin ................................................................ WeBT8.5 2790<br />
.................................................................................. WeCPT10.23 *<br />
Kim, James Dokyoon................................................ WeCT9.2 3290<br />
Kim, Jung.................................................................. ThBT7 CC<br />
.................................................................................. ThBT7.7 4516<br />
Kim, Junsuk.............................................................. WeCT4.3 3039<br />
Kim, Keehoon........................................................... ThBT7.2 4483<br />
Kim, Min Jeong......................................................... TuBT7.7 1447<br />
Kim, Minjun............................................................... WeBT3.2 2524<br />
Kim, Sangbae........................................................... ThAT5.5 3957<br />
.................................................................................. ThBPT10.14 *<br />
Kim, Sungmin ........................................................... WeBT3.2 2524<br />
Kim, Ui-Hyun ............................................................ WeCT1.7 2910<br />
Kim, Woojin .............................................................. WeBT8.5 2790<br />
.................................................................................. WeCPT10.23 *<br />
Kim, Yeongjin ........................................................... ThBT7.7 4516<br />
Kim, Young Soo........................................................ WeBT3.2 2524<br />
Kim, Youngmoo........................................................ WeCT1.8 2916<br />
King, Raymond......................................................... ThCT8.8 4975<br />
Kinnaert, Michel........................................................ ThBT7.1 4477<br />
Kinsey, James .......................................................... MoAT6.8 261<br />
.................................................................................. ThCT3.2 4722<br />
Kirchner, Frank......................................................... ThAT9 CC<br />
.................................................................................. ThAT9.3 4134<br />
Kirchner, Nathan....................................................... WeCT1.6 2903<br />
.................................................................................. WeDPT10.3 *<br />
Kit, Dmitry................................................................. TuBT1.4 1194<br />
.................................................................................. TuCPT10.1 *<br />
Kitt, Bernd................................................................. MoAT6.3 227<br />
Klappstein, Jens ....................................................... ThBT9.4 4587<br />
.................................................................................. ThCPT10.25 *<br />
Kleiner, Alexander .................................................... WeCT8.8 3276<br />
Klimchik, Alexandr.................................................... ThAT7.2 4034<br />
Klodmann, Julian...................................................... WeDT9.3 3708<br />
Klotzbuecher, Markus............................................... ThCT2.3 4684<br />
Kneip, Laurent .......................................................... MoAT2.5 79<br />
.................................................................................. MoBPT10.5 *<br />
.................................................................................. WeAT6.7 2235<br />
.................................................................................. WeBT6.6 2694<br />
.................................................................................. WeCPT10.18 *<br />
Knepper, Ross A ...................................................... ThAT3.1 3856<br />
Knoll, Alois................................................................ TuAT7.8 1063<br />
.................................................................................. WeDT1.6 3375<br />
.................................................................................. ThAT9.5 4148<br />
.................................................................................. ThAPT10.3 *<br />
.................................................................................. ThBPT10.26 *<br />
Ko, Hanseok............................................................. MoAT3 O<br />
.................................................................................. MoBT3 O<br />
Kobayashi, Ryo ........................................................ TuCT8.3 1875<br />
Kobayashi, Yo .......................................................... TuAT5.6 955<br />
.................................................................................. TuBPT10.12 *<br />
Kober, Jens .............................................................. MoAT8.4 338<br />
.................................................................................. MoBPT10.22 *<br />
Kocamaz, Mehmet.................................................... ThAT8.3 4084<br />
Kodagoda, Sarath..................................................... ThBT9.7 4608<br />
Kodama, Atsushi ...................................................... MoAT7.1 268<br />
Koehler, Sarah.......................................................... WeCT5.7 3120<br />
Koepl, Devin ............................................................. ThAT1.3 3758<br />
Koh, Kyoung-Chul .................................................... TuAT5.8 967<br />
Kohda, Takehisa....................................................... WeAT7.5 2274<br />
.................................................................................. WeAT7.6 2280<br />
.................................................................................. WeBPT10.20 *<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–244–<br />
.................................................................................. WeBPT10.21 *<br />
Kohler, Norman ........................................................ ThCT8.3 4941<br />
Kojima, Masaru......................................................... MoAT1.1 1<br />
.................................................................................. MoBT1.3 433<br />
.................................................................................. MoBT1.7 457<br />
.................................................................................. TuCT3.6 1693<br />
.................................................................................. WeAPT10.9 *<br />
Kokuryu, Saori .......................................................... WeAT4.7 2151<br />
Komori, Kimihiro ....................................................... WeAT3.7 2115<br />
Kondak, Konstantin .................................................. WeDT3.5 3474<br />
.................................................................................. ThAPT10.8 *<br />
Kondo, Daisuke ........................................................ TuCT9.6 1946<br />
.................................................................................. WeAPT10.24 *<br />
Kondo, Hayato.......................................................... WeCT6 O<br />
.................................................................................. WeDT6 O<br />
Kondo, Hideki ........................................................... WeCT7.8 3221<br />
Kondo, Takao ........................................................... MoAT1.1 1<br />
Kong, Kyoungchul .................................................... WeBT2.3 2474<br />
Kong, Zhaodan ......................................................... WeCT5.3 3093<br />
Konietschke, Rainer ................................................. WeDT9.3 3708<br />
Königs, Achim........................................................... WeDT7.5 3614<br />
.................................................................................. ThAPT10.17 *<br />
Konolige, Kurt ........................................................... ThBT2.4 4249<br />
.................................................................................. ThCPT10.4 *<br />
Konyo, Masashi ........................................................ WeDT3 CC<br />
.................................................................................. WeDT3.7 3488<br />
.................................................................................. WeDT3.8 3494<br />
Koo, Ja Choon.......................................................... TuBT7.7 1447<br />
Kootstra, Gert ........................................................... TuAT6.5 987<br />
.................................................................................. TuBPT10.14 *<br />
Kormushev, Petar..................................................... MoAT8 CC<br />
.................................................................................. MoAT8.1 318<br />
Koropouli, Vasiliki ..................................................... MoAT8.5 344<br />
.................................................................................. MoBPT10.23 *<br />
Kosecka, Jana .......................................................... WeAT9 C<br />
.................................................................................. WeAT9.6 2383<br />
.................................................................................. WeBPT10.27 *<br />
Koseki, Yoshihiko ..................................................... WeBT4.3 2584<br />
Kosmatopoulos, Elias ............................................... TuCT2.7 1661<br />
Kossyk, Ingo ............................................................. WeDT3.5 3474<br />
.................................................................................. ThAPT10.8 *<br />
Kosuge, Kazuhiro ..................................................... MoAT7.8 311<br />
.................................................................................. WeAT1.7 2008<br />
.................................................................................. ThBT9.5 4593<br />
.................................................................................. ThCPT10.26 *<br />
Kotoku, Tetsuo ......................................................... TuAT7.5 1042<br />
.................................................................................. TuBPT10.17 *<br />
Kovecses, Jozsef...................................................... MoAT7.3 280<br />
.................................................................................. WeDT9 CC<br />
.................................................................................. WeDT9.8 3740<br />
Kraetzschmar, Gerhard ............................................ TuAT7.3 1030<br />
Kragic, Danica .......................................................... TuAT1.7 827<br />
.................................................................................. TuAT6.4 980<br />
.................................................................................. TuAT6.5 987<br />
.................................................................................. TuBT9 C<br />
.................................................................................. TuBT9.7 1554<br />
.................................................................................. TuBPT10.13 *<br />
.................................................................................. TuBPT10.14 *<br />
.................................................................................. WeAT2.2 2028<br />
.................................................................................. WeDT1.1 3342<br />
Kramer, Rebecca...................................................... MoAT9.8 414<br />
.................................................................................. TuCT9.2 1919<br />
Krauthausen, Peter................................................... ThCT5.4 4819<br />
.................................................................................. ThDPT10.13 *<br />
Kress-Gazit, Hadas .................................................. WeCT5 C<br />
.................................................................................. WeCT5 O<br />
.................................................................................. WeCT5.7 3120<br />
Kretzschmar, Henrik ................................................. MoAT2.6 86<br />
.................................................................................. MoBPT10.6 *<br />
.................................................................................. TuAT2.5 865
.................................................................................. TuBPT10.5 *<br />
Krid, Mohamed ......................................................... MoAT7.2 274<br />
Krieger, Kai............................................................... TuCT6.6 1789<br />
.................................................................................. WeAPT10.15 *<br />
Krishna, Madhava..................................................... ThBT3.5 4314<br />
.................................................................................. ThCPT10.8 *<br />
Kristan, Matej............................................................ WeDT1.8 3387<br />
Kroemer, Oliver ........................................................ TuBT9.6 1548<br />
.................................................................................. TuCPT10.24 *<br />
Kronander, Klas........................................................ MoBT8.4 710<br />
.................................................................................. TuAPT10.19 *<br />
Krontiris, Athanasios................................................. WeCT8.2 3235<br />
Kroschel, Kristian...................................................... TuBT1.1 1173<br />
Krug, Robert ............................................................. TuCT6.7 1797<br />
Krüger, Norbert......................................................... TuAT6.5 987<br />
.................................................................................. TuBPT10.14 *<br />
Kruijff, Geert-Jan ...................................................... WeDT1.8 3387<br />
Krupa, Alexandre...................................................... WeBT9.3 2831<br />
.................................................................................. WeBT9.4 2837<br />
.................................................................................. WeCPT10.25 *<br />
Krut, Sebastien......................................................... ThDT4.2 5067<br />
Kryczka, Przemyslaw ............................................... WeCT7.8 3221<br />
Kuan, Jiun-Yih .......................................................... MoAT9.1 372<br />
Kubus, Daniel ........................................................... WeBT2.4 2481<br />
.................................................................................. WeCPT10.4 *<br />
Kuchenbecker, Katherine J. ..................................... WeAT4 C<br />
.................................................................................. WeAT4 O<br />
.................................................................................. WeBT4 CC<br />
.................................................................................. WeBT4 O<br />
.................................................................................. WeCT4 O<br />
Kuehn, Benjamin ...................................................... TuBT1.1 1173<br />
Kuehne, Hildegard.................................................... ThCT5.4 4819<br />
.................................................................................. ThDPT10.13 *<br />
Kuemmerle, Rainer................................................... WeDT9.4 3716<br />
.................................................................................. ThAPT10.22 *<br />
Kuffner, James ......................................................... WeAT5.8 2199<br />
Kumar, Rajesh.......................................................... WeBT3.4 2539<br />
.................................................................................. WeCPT10.7 *<br />
Kumar, Vijay ............................................................. WeBT6.2 2668<br />
.................................................................................. WeCT5.8 3126<br />
.................................................................................. WeCT8.4 3248<br />
.................................................................................. WeDT8.5 3667<br />
.................................................................................. WeDPT10.22 *<br />
.................................................................................. ThAPT10.20 *<br />
Kumon, Makoto ........................................................ MoAT3.3 112<br />
Kuniyoshi, Yasuo...................................................... TuBT1.7 1214<br />
.................................................................................. TuBT8.7 1499<br />
Kunze, Lars .............................................................. WeCT7.1 3172<br />
Kunze, Mirko............................................................. TuCT6.6 1789<br />
.................................................................................. WeAPT10.15 *<br />
Kuperij, Nicole .......................................................... TuAT5.3 937<br />
Kurazume, Ryo......................................................... WeAT2.1 2020<br />
.................................................................................. ThBT1.5 4201<br />
.................................................................................. ThCPT10.2 *<br />
Kurdila, Andrew ........................................................ TuAT9.4 1140<br />
.................................................................................. TuBPT10.22 *<br />
Kurita, Yuichi ............................................................ WeAT4.3 2127<br />
Kurniawati, Hanna .................................................... WeDT6.3 3551<br />
Kushleyev, Aleksandr............................................... TuAT9.5 1146<br />
.................................................................................. TuBPT10.23 *<br />
Kuthirummal, Sujit .................................................... ThAT3.4 3874<br />
.................................................................................. ThBPT10.7 *<br />
Kuwahara, Hiroyuki .................................................. ThBT8.4 4550<br />
.................................................................................. ThCPT10.22 *<br />
Kuwata, Yoshiaki...................................................... ThCT3.3 4728<br />
Kwak, Kiho................................................................ WeCT9.1 3283<br />
Kweon, In So ............................................................ WeCT9.2 3290<br />
.................................................................................. ThBT6.3 4436<br />
Kwon, Dong-Soo ...................................................... WeBT7 C<br />
.................................................................................. WeBT7.8 2759<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–245–<br />
Kyrkjebø, Erik ........................................................... MoAT6.6 247<br />
.................................................................................. MoBPT10.18 *<br />
.................................................................................. ThCT2.2<br />
L<br />
4676<br />
Labbé, Mathieu......................................................... TuBT2.7 1271<br />
Lacerda, Bruno ......................................................... WeCT5.1 3081<br />
Ladjal, Hamid............................................................ TuBT3.8 1315<br />
Lakaemper, Rolf ....................................................... ThAT2.2 3808<br />
Lal, Sara ................................................................... ThBT9.7 4608<br />
Lalande, Viviane ....................................................... TuAT3.4 901<br />
.................................................................................. TuBT3.3 1285<br />
.................................................................................. TuBPT10.7 *<br />
Lallée, Stéphane....................................................... WeCT1.5 2895<br />
.................................................................................. WeDPT10.2 *<br />
Lam, Tin Lun............................................................. TuBT8.5 1487<br />
.................................................................................. TuCPT10.20 *<br />
Lambercy, Olivier...................................................... WeCT4.8 3074<br />
Lambert, Ghislain ..................................................... TuCT7.7 1849<br />
Lamiraux, Florent...................................................... ThBT5.5 4408<br />
.................................................................................. ThCPT10.14 *<br />
Lampert, Christoph H. .............................................. MoAT8.3 332<br />
Lamy-Perbal, Sylvie.................................................. WeBT2.7 2505<br />
Lan, Chao-Chieh ...................................................... TuCT9.8 1962<br />
Lane, Ian................................................................... MoAT3 CC<br />
.................................................................................. MoAT3 O<br />
.................................................................................. MoBT3 O<br />
.................................................................................. MoBT3.1 *<br />
Langner, Tim ............................................................ WeBT1.5 2430<br />
Lapeyre, Matthieu..................................................... TuBT8.2 1465<br />
Lapierre, Lionel......................................................... WeDT6.4 3558<br />
Laschi, Cecilia .......................................................... ThCT5.5 4826<br />
.................................................................................. ThDPT10.14 *<br />
Latscha, Stella .......................................................... MoAT9.7 408<br />
Lau, Haye ................................................................. WeDT8.8 3686<br />
Laugier, Christian ..................................................... WeAT1.8 2014<br />
Laurent, Guillaume J. ............................................... ThDT6.2 5121<br />
LaValle, Steven M .................................................... WeAT5.8 2199<br />
.................................................................................. WeBT2.8 2511<br />
.................................................................................. WeBT5 CC<br />
.................................................................................. WeBT5 O<br />
.................................................................................. WeCT5.4 3101<br />
.................................................................................. WeDPT10.13 *<br />
.................................................................................. ThAT3.2 3862<br />
Lavoie, Samuel......................................................... TuCT7.7 1849<br />
Lawitzky, Martin........................................................ WeBT1.3 2416<br />
Lazea Gheorghe, Gheorghe..................................... TuBT2.5 1256<br />
.................................................................................. TuCPT10.5 *<br />
Lazewatsky, Daniel................................................... ThAT8.8 4115<br />
Le, Minh-Quyen ........................................................ MoBT7.4 659<br />
.................................................................................. MoBT9.1 738<br />
.................................................................................. TuAPT10.16 *<br />
Le Fort-Piat, Nadine ................................................. ThDT6.2 5121<br />
Lea, Colin ................................................................. ThAT2.3 3816<br />
Lebastard, Vincent.................................................... TuCT8.7 1901<br />
Lee, C. S. George..................................................... WeAT8 C<br />
.................................................................................. WeAT8.1 2300<br />
Lee, Daniel D............................................................ WeAT5.6 2186<br />
.................................................................................. WeBPT10.15 *<br />
.................................................................................. ThAT5.6 3963<br />
.................................................................................. ThBPT10.15 *<br />
Lee, Deukhee ........................................................... WeBT9.3 2831<br />
Lee, Dongheui .......................................................... MoAT8.5 344<br />
.................................................................................. MoBPT10.23 *<br />
.................................................................................. TuAT6.7 1002<br />
.................................................................................. WeBT1.3 2416<br />
.................................................................................. WeDT7.7 3626<br />
Lee, Dongjun ............................................................ MoAT4.3 149<br />
.................................................................................. WeCT4.3 3039<br />
Lee, Gim Hee ........................................................... TuCT2.6 1655<br />
.................................................................................. WeAPT10.6 *
.................................................................................. ThAT6.7 4012<br />
Lee, Jae Hoon .......................................................... WeDT7.2 3595<br />
Lee, James............................................................... ThAT2.3 3816<br />
Lee, Ji Yeong Lee..................................................... ThDT6.3 5127<br />
Lee, Jongwon ........................................................... WeBT3.2 2524<br />
Lee, Min Kyung......................................................... WeAT1.4 1986<br />
.................................................................................. WeBPT10.1 *<br />
Lee, Seong-Whan..................................................... WeCT4.3 3039<br />
Lee, Seongsoo ......................................................... TuAT2.1 841<br />
Lee, Seung Hwan..................................................... TuAT5.8 967<br />
Lee, Tae-kyeong....................................................... TuAT2.1 841<br />
Lee, Yi-Chiao............................................................ TuCT9.8 1962<br />
Leeper, Adam Eric.................................................... WeBT4.1 2570<br />
Legrand, Bernard...................................................... MoAT1.8 45<br />
Lehman, Joel............................................................ WeBT8.7 2802<br />
Lehnert, Christopher................................................. MoBT8.1 692<br />
Leishman, Robert ..................................................... WeBT6.5 2688<br />
.................................................................................. WeCPT10.17 *<br />
.................................................................................. ThDT8.2 5173<br />
Lemaignan, Séverin.................................................. WeCT1.5 2895<br />
.................................................................................. WeDPT10.2 *<br />
Lemerle, Pierre......................................................... WeCT7.7 3213<br />
Lenain, Roland ......................................................... ThAT8.1 4072<br />
.................................................................................. ThBT9.1 4569<br />
Lenz, Alexander........................................................ WeCT1.5 2895<br />
.................................................................................. WeDPT10.2 *<br />
Lenz, Claus............................................................... WeDT1.6 3375<br />
.................................................................................. ThAPT10.3 *<br />
Leon, Lisandro.......................................................... TuBT5.2 1321<br />
Leu, Adrian ............................................................... TuAT1.1 786<br />
Leutenegger, Stefan................................................. WeAT6.5 2223<br />
.................................................................................. WeBPT10.17 *<br />
Leven, Severin.......................................................... ThCT9.6 5015<br />
.................................................................................. ThDPT10.27 *<br />
Lewinger, William ..................................................... MoAT5.7 215<br />
Lewis, Bennie ........................................................... WeBT4.4 2590<br />
.................................................................................. WeCPT10.10 *<br />
Li, Baopu .................................................................. WeAT3.3 2090<br />
Li, Bin........................................................................ TuCT8.2 1869<br />
Li, Howard ................................................................ TuAT2.3 853<br />
.................................................................................. TuAT2.7 880<br />
Li, Jinglin................................................................... ThBT1.6 4207<br />
.................................................................................. ThCPT10.3 *<br />
Li, Peng .................................................................... ThBT1.8 4222<br />
Li, Xiaodong.............................................................. TuCT3.8 1705<br />
Li, Xiaofei.................................................................. WeCT1.3 2879<br />
Li, Xin........................................................................ MoAT4.8 184<br />
Li, Y.F. ...................................................................... WeDT2.8 3440<br />
Li, Yuanhong ............................................................ ThBT9.2 4575<br />
Li, Zhanchu............................................................... TuBT8.3 1473<br />
Liang, Dong .............................................................. MoBT3.8 550<br />
Liang, Jianhong ........................................................ TuCT8.1 1863<br />
Libby, Tom................................................................ TuCT8.5 1887<br />
.................................................................................. WeAPT10.20 *<br />
Lidholm, Jörgen........................................................ TuCT2.2 1626<br />
Liemhetcharat, Somchaya........................................ WeDT8.1 3638<br />
Lien, Jyh-Ming .......................................................... TuCT1 CC<br />
.................................................................................. TuCT1.2 1573<br />
.................................................................................. WeBT5.4 2626<br />
.................................................................................. WeCPT10.13 *<br />
Likhachev, Maxim..................................................... TuAT9.5 1146<br />
.................................................................................. TuBPT10.23 *<br />
.................................................................................. WeAT5 CC<br />
.................................................................................. WeAT5 O<br />
.................................................................................. WeCT8.5 3254<br />
.................................................................................. WeDT5 C<br />
.................................................................................. WeDT5 O<br />
.................................................................................. WeDPT10.23 *<br />
Lim, Hun-ok .............................................................. WeCT7.8 3221<br />
Lim, Hup Boon.......................................................... ThCT7.6 4923<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–246–<br />
.................................................................................. ThDPT10.21 *<br />
Lim, Jongwoo ........................................................... TuCT2.1 1618<br />
.................................................................................. ThAT6.3 3982<br />
Lima, Pedro .............................................................. WeCT5.1 3081<br />
Lin, Pei-Chun............................................................ TuBT8.6 1493<br />
.................................................................................. TuCPT10.21 *<br />
.................................................................................. ThDT4 CC<br />
Lin, Yun .................................................................... WeDT1.2 3349<br />
Lind, Morten.............................................................. WeBT9.8 2861<br />
Lindsey, Quentin....................................................... WeBT6.2 2668<br />
Lippiello, Vincenzo.................................................... ThBT1.4 4194<br />
.................................................................................. ThCPT10.1 *<br />
Lipson, Hod .............................................................. ThBT4.5 4366<br />
.................................................................................. ThCPT10.11 *<br />
Little, James J........................................................... WeBT9.2 2825<br />
.................................................................................. ThCT6.8 4885<br />
Liu, Albert ................................................................. TuCT3.3 1674<br />
Liu, Chao .................................................................. WeDT6.4 3558<br />
.................................................................................. ThAPT10.13 *<br />
Liu, Dikai................................................................... WeDT8.8 3686<br />
Liu, Hong .................................................................. WeCT1.3 2879<br />
Liu, Jindong .............................................................. MoBT3.7 542<br />
Liu, Jingmeng ........................................................... MoBT9.2 744<br />
Liu, Ming ................................................................... ThAT2.4 3824<br />
.................................................................................. ThBPT10.4 *<br />
Liu, Wen ................................................................... TuBT5.6 1346<br />
.................................................................................. TuCPT10.12 *<br />
Liu, Yen-Chen........................................................... MoBT7.7 679<br />
Liu, Ziyuan ................................................................ WeDT7.7 3626<br />
Lofaro, Daniel ........................................................... WeCT1.8 2916<br />
Loiselle, Stéphane .................................................... MoBT3.3 518<br />
Long, Jonathan......................................................... TuAT1.2 793<br />
Lopes, Ana ............................................................... WeBT1.6 2438<br />
.................................................................................. WeCPT10.3 *<br />
Lopes, Gabriel .......................................................... WeBT7.3 2729<br />
Low, K. H. ................................................................. MoAT5 O<br />
.................................................................................. MoBT5 C<br />
.................................................................................. MoBT5 O<br />
.................................................................................. MoBT5.5 568<br />
.................................................................................. TuCT5.6 1743<br />
.................................................................................. WeAPT10.12 *<br />
.................................................................................. ThCT7.6 4923<br />
.................................................................................. ThDPT10.21 *<br />
Lozano-Perez, Tomas .............................................. WeAT5.1 *<br />
Lu, Wenyu ................................................................ ThBT3.6 4321<br />
.................................................................................. ThCPT10.9 *<br />
Lu, Yan ..................................................................... ThAT8.3 4084<br />
Lu, Yanyan ............................................................... WeBT5.4 2626<br />
.................................................................................. WeCPT10.13 *<br />
Lu, Yibiao.................................................................. WeDT5.2 3507<br />
Lu, Zhiguo................................................................. ThDT5.2 5094<br />
Luber, Matthias......................................................... ThAT2.7 3844<br />
Lucas, Blake ............................................................. TuBT5.6 1346<br />
.................................................................................. TuCPT10.12 *<br />
Luettel, Thorsten....................................................... ThBT8.6 4562<br />
.................................................................................. ThCPT10.24 *<br />
Lumia, Ron ............................................................... WeAT8 CC<br />
.................................................................................. WeAT8.6 2333<br />
Luna, Ryan ............................................................... WeCT8.7 3268<br />
Luo, Guoliang ........................................................... WeAT2.2 2028<br />
Luo, Ren ................................................................... WeCT2.8 2965<br />
Lupashin, Sergei....................................................... ThDT6.1 5113<br />
.................................................................................. ThDT8.3 5179<br />
Luu, Trieu Phat ......................................................... ThCT7.6 4923<br />
.................................................................................. ThDPT10.21 *<br />
Ly, Olivier.................................................................. TuBT8 CC<br />
.................................................................................. TuBT8.2<br />
M<br />
1465<br />
Ma, Shugen .............................................................. TuCT8.2 1869<br />
.................................................................................. ThDT3 CC
.................................................................................. ThDT3.1 5035<br />
Ma, Yan .................................................................... ThCT8.6 4961<br />
.................................................................................. ThDPT10.24 *<br />
MacAllister, Brian...................................................... TuAT9.5 1146<br />
.................................................................................. TuBPT10.23 *<br />
Macasieb, Juan Antonio Miguel Lim......................... ThCT4.7 4797<br />
Macdonald, John ...................................................... ThDT8.2 5173<br />
MacLachlan, Robert A.............................................. ThDT7.4 5160<br />
Macwan, Ashish ....................................................... ThBT8.3 4544<br />
Madhavan, Raj ......................................................... ThDT6 C<br />
Mae, Yasushi............................................................ MoAT1.5 25<br />
.................................................................................. MoBT1.2 427<br />
.................................................................................. MoBPT10.2 *<br />
.................................................................................. TuAT1.4 807<br />
.................................................................................. TuBPT10.1 *<br />
Maeda, Guilherme Jorge.......................................... MoBT8.6 725<br />
.................................................................................. TuAPT10.21 *<br />
Magnenat, Stéphane ................................................ ThAT2.4 3824<br />
.................................................................................. ThBPT10.4 *<br />
Magnusson, Martin................................................... ThAT8.2 4078<br />
Mahmudi, Mentar...................................................... WeBT5.8 2653<br />
Mahnič, Marko.......................................................... WeDT1.8 3387<br />
Mahon, Ian................................................................ ThBT6.6 4455<br />
.................................................................................. ThCPT10.18 *<br />
Mahoor, Mohammad ................................................ WeAT9 CC<br />
.................................................................................. WeAT9.5 2377<br />
.................................................................................. WeBPT10.26 *<br />
Mair, Elmar ............................................................... WeCT9.3 3297<br />
Mairiaux, Estelle ....................................................... MoAT1.8 45<br />
Majdik, Andras.......................................................... TuBT2.5 1256<br />
.................................................................................. TuCPT10.5 *<br />
Majidi, Carmel........................................................... TuCT9.2 1919<br />
Makikawa, Masaaki .................................................. TuCT9.6 1946<br />
.................................................................................. WeAPT10.24 *<br />
Malosio, Matteo ........................................................ WeCT9.7 3327<br />
Malvezzi, Monica...................................................... TuAT6.8 1008<br />
Malysz, Pawel........................................................... MoBT7.2 645<br />
Mansard, Nicolas...................................................... ThAT3.6 3887<br />
.................................................................................. ThAT9.2 4127<br />
.................................................................................. ThBPT10.9 *<br />
Mansour, Darine....................................................... WeCT7.7 3213<br />
Manz, Michael .......................................................... ThBT8.6 4562<br />
.................................................................................. ThCPT10.24 *<br />
Marble, James.......................................................... ThBT3.2 4292<br />
Marchal-Crespo, Laura............................................. WeCT4.7 3068<br />
Marchand, Eric ......................................................... MoBT6.6 619<br />
.................................................................................. TuAPT10.15 *<br />
.................................................................................. ThCT8.1 4929<br />
Marchese, Andrew.................................................... MoBT9.4 756<br />
.................................................................................. TuAPT10.22 *<br />
Marchetti, Luca......................................................... WeBT8.6 2796<br />
.................................................................................. WeCPT10.24 *<br />
Marino, Alessandro................................................... WeBT8.3 2778<br />
Mariottini, Gian Luca................................................. WeAT6.6 2229<br />
.................................................................................. WeAT9.4 2371<br />
.................................................................................. WeBPT10.18 *<br />
.................................................................................. WeBPT10.25 *<br />
.................................................................................. WeDT7 C<br />
.................................................................................. WeDT7.4 3608<br />
.................................................................................. ThAPT10.16 *<br />
Marks, Tim K. ........................................................... ThCT2.4 4690<br />
.................................................................................. ThDPT10.4 *<br />
Marques, Hugo......................................................... TuAT7.8 1063<br />
Marques, Lino........................................................... ThBT2.8 4277<br />
Martel, Sylvain.......................................................... TuAT3 C<br />
.................................................................................. TuAT3 O<br />
.................................................................................. TuAT3.4 901<br />
.................................................................................. TuBT3 CC<br />
.................................................................................. TuBT3 O<br />
.................................................................................. TuBT3.3 1285<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–247–<br />
.................................................................................. TuBT3.6 1304<br />
.................................................................................. TuBPT10.7 *<br />
.................................................................................. TuCT3 C<br />
.................................................................................. TuCT3 O<br />
.................................................................................. TuCPT10.9 *<br />
Martin, Christian ....................................................... WeBT1.5 2430<br />
Martinelli, Agostino ................................................... WeBT2 CC<br />
.................................................................................. WeBT2.1 2460<br />
Martinet, Philippe...................................................... WeBT9.7 2855<br />
.................................................................................. ThAT8.1 4072<br />
Martinoli, Alcherio ..................................................... WeBT8.1 2765<br />
.................................................................................. WeCT8.3 3241<br />
.................................................................................. WeCT9.5 3313<br />
.................................................................................. WeDPT10.26 *<br />
.................................................................................. ThBT4.1 4341<br />
Martinson, Eric.......................................................... MoAT3.5 125<br />
.................................................................................. MoBPT10.8 *<br />
Marton, Zoltan-Csaba............................................... ThBT2.6 4263<br />
.................................................................................. ThCPT10.6 *<br />
Maruyama, Hisataka................................................. MoAT1.3 13<br />
Mascaro, Stephen .................................................... MoAT9 CC<br />
.................................................................................. MoAT9.6 402<br />
.................................................................................. MoBT9.8 780<br />
.................................................................................. MoBPT10.27 *<br />
.................................................................................. WeBT4.5 2596<br />
.................................................................................. WeCPT10.11 *<br />
Masehian, Ellips ....................................................... ThBT3.3 4299<br />
Mason, Matthew T. ................................................... TuCT6.8 1804<br />
.................................................................................. ThAT3.1 3856<br />
Masone, Carlo .......................................................... WeAT6.4 2215<br />
.................................................................................. WeBPT10.16 *<br />
Mastrangeli, Massimo............................................... ThBT4.1 4341<br />
Mastrogiovanni, Fulvio.............................................. WeDT9.1 3694<br />
Masuda, Taisuke ...................................................... MoAT1.3 13<br />
Mataric, Maja ............................................................ WeBT1.1 2403<br />
Mathews, Nithin ........................................................ ThCT4.2 4762<br />
.................................................................................. ThCT9.8 5027<br />
Mathieu, Philippe ...................................................... WeDT5.4 3519<br />
.................................................................................. ThAPT10.10 *<br />
Mathisen, Geir .......................................................... WeBT9.8 2861<br />
Matos, Vítor .............................................................. WeAT7.7 2286<br />
Matsubara, Takamitsu .............................................. ThAT5.8 3975<br />
Matsuno, Fumitoshi .................................................. TuCT8 CC<br />
.................................................................................. TuCT8.8 1907<br />
Matsusaka, Yosuke .................................................. WeAT1.6 2000<br />
.................................................................................. WeBPT10.3 *<br />
Mattos, Leonardo...................................................... TuBT5.8 1359<br />
Maughan, Thom ....................................................... WeCT6.3 3132<br />
Mavroidis, Constantinos ........................................... ThCT7 CC<br />
.................................................................................. ThCT7.1 4893<br />
Maxwell, Bruce ......................................................... ThAT8.8 4115<br />
Maycock, Jonathan................................................... WeCT2.5 2947<br />
.................................................................................. WeDPT10.5 *<br />
Mazalaigue, Stéphane.............................................. WeBT3.3 2532<br />
McAllester, David...................................................... ThBT6.4 4442<br />
.................................................................................. ThCPT10.16 *<br />
McBride, James........................................................ ThBT4.7 4378<br />
McCann, Mike........................................................... WeCT6.3 3132<br />
McGinnity, Martin...................................................... WeDT3.1 3446<br />
.................................................................................. ThCT2.8 4716<br />
McLain, T.W. ............................................................ WeBT6.5 2688<br />
.................................................................................. WeCPT10.17 *<br />
.................................................................................. ThDT8.2 5173<br />
McLaren, David ........................................................ WeCT6.4 3140<br />
.................................................................................. WeDPT10.16 *<br />
McPherson, Timothy................................................. TuCT9.5 1940<br />
.................................................................................. WeAPT10.23 *<br />
Medina Ayala, Ana Ivonne........................................ WeCT5.5 3108<br />
.................................................................................. WeDPT10.14 *<br />
Medina Hernandez, Jose Ramon ............................. WeBT1.3 2416
Medioni, Gerard........................................................ ThAT2.1 3800<br />
Medrano-Cerda, Gustavo......................................... TuAT8.5 1093<br />
.................................................................................. TuBPT10.20 *<br />
Meek, Sanford .......................................................... TuCT9 CC<br />
.................................................................................. TuCT9.3 1927<br />
.................................................................................. WeDT7.8 3632<br />
.................................................................................. ThBT1.1 4174<br />
Meeussen, Wim........................................................ WeCT2 CC<br />
.................................................................................. WeCT2.4 2941<br />
.................................................................................. WeDPT10.4 *<br />
Mefoued, Saber........................................................ TuCT5.7 1749<br />
Meger, David Paul.................................................... ThCT6.8 4885<br />
Meghjani, Malika....................................................... ThDT3.3 5048<br />
Mehnert, Jens........................................................... ThBT9.4 4587<br />
.................................................................................. ThCPT10.25 *<br />
Mehrjerdi, Hasan ...................................................... WeAT8.7 2340<br />
Meier, Franziska....................................................... WeDT2.3 3407<br />
Meilland, Maxime...................................................... ThBT2.3 4242<br />
Meirion-Griffith, Gareth............................................. MoAT7.7 305<br />
Meißner, Pascal........................................................ ThCT1.3 4633<br />
Melchiorri, Claudio.................................................... TuCT6.4 1775<br />
.................................................................................. WeAPT10.13 *<br />
.................................................................................. ThAT3.8 3899<br />
Melendez-Calderon, Alejandro................................. WeBT4.2 2578<br />
Melhuish, Chris......................................................... WeCT1.5 2895<br />
.................................................................................. WeDPT10.2 *<br />
Mellinger, Daniel....................................................... WeBT6.2 2668<br />
Menegatti, Emanuele................................................ WeDT3.6 3480<br />
.................................................................................. ThAPT10.9 *<br />
Meng, Max Q.-H. ...................................................... WeAT3.3 2090<br />
Mermoud, Gregory ................................................... ThBT4.1 4341<br />
Merten, Matthias....................................................... WeBT1.5 2430<br />
Merz, Torsten ........................................................... ThCT8.5 4953<br />
.................................................................................. ThDPT10.23 *<br />
Messié, Monique ...................................................... WeCT6.3 3132<br />
Metta, Giorgio........................................................... TuBT9.3 1526<br />
.................................................................................. WeCT1.5 2895<br />
.................................................................................. WeDT9 C<br />
.................................................................................. WeDT9.1 3694<br />
.................................................................................. WeDPT10.2 *<br />
.................................................................................. ThAT9.6 4154<br />
.................................................................................. ThAT9.7 4161<br />
.................................................................................. ThBPT10.27 *<br />
Mettin, Uwe............................................................... ThDT5.2 5094<br />
Mettler, Bernie .......................................................... WeCT5.3 3093<br />
Metzger, Jean-Claude .............................................. WeCT4.8 3074<br />
Meyer-Delius, Daniel ................................................ WeCT8.8 3276<br />
Micaelli, Alain............................................................ WeCT7.7 3213<br />
Michael, Nathan........................................................ WeAT6 C<br />
.................................................................................. WeAT6 O<br />
.................................................................................. WeBT6 O<br />
.................................................................................. WeBT6.8 2708<br />
.................................................................................. WeDT8.5 3667<br />
.................................................................................. ThAPT10.20 *<br />
Michaud, Francois .................................................... TuBT2.7 1271<br />
Michel, Fabien .......................................................... ThDT3.2 5041<br />
Michelin, Micaël........................................................ WeBT9.7 2855<br />
Mier-Y-Teran-Romero, Luis...................................... ThAT4.3 3905<br />
Mignano, Anthony..................................................... MoBT5.7 580<br />
Milella, Annalisa........................................................ MoAT6.7 255<br />
Milighetti, Giulio ........................................................ WeCT7.4 3192<br />
.................................................................................. WeDPT10.19 *<br />
Miller, Stephen.......................................................... ThCT6.7 4877<br />
Milutinovic, Dejan ..................................................... ThAT4.5 3917<br />
.................................................................................. ThBT4 CC<br />
.................................................................................. ThBPT10.11 *<br />
Minamizawa, Kouta .................................................. MoAT4.4 157<br />
.................................................................................. MoBPT10.10 *<br />
Minato, Takashi ........................................................ WeDT3.6 3480<br />
.................................................................................. ThAPT10.9 *<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–248–<br />
Ming, Aiguo............................................................... TuCT9.7 1954<br />
.................................................................................. ThAT7.4 4048<br />
.................................................................................. ThCT3.4 4735<br />
.................................................................................. ThDPT10.7 *<br />
Minor, Mark............................................................... MoAT7.5 292<br />
.................................................................................. MoBPT10.20 *<br />
.................................................................................. ThCT8 CC<br />
.................................................................................. ThCT8.8 4975<br />
Miroir, Mathieu.......................................................... WeBT3.3 2532<br />
Misra, Sarthak .......................................................... MoBT1.1 421<br />
.................................................................................. TuAT5.3 937<br />
.................................................................................. WeAT3.1 2076<br />
.................................................................................. WeBT3 CC<br />
.................................................................................. WeBT3.7 2557<br />
Mita, Seiichi .............................................................. ThBT6.4 4442<br />
.................................................................................. ThCPT10.16 *<br />
Mitra, Urbashi ........................................................... WeDT6.5 3564<br />
.................................................................................. ThAPT10.14 *<br />
Mitsuhashi, Masaru .................................................. WeCT7.2 3179<br />
Miura, Hiroki ............................................................. MoBT3.4 524<br />
.................................................................................. TuAPT10.7 *<br />
Miura, Kanako .......................................................... ThBT5.3 4392<br />
.................................................................................. ThBT5.8 4428<br />
Miyamori, Go ............................................................ ThBT5.4 4400<br />
.................................................................................. ThCPT10.13 *<br />
Miyashita, Takahiro .................................................. WeDT7.1 3589<br />
Miyazaki, Fumio........................................................ ThBT7.4 4496<br />
.................................................................................. ThCPT10.19 *<br />
Mizumoto, Takeshi ................................................... MoAT3.4 118<br />
.................................................................................. MoBPT10.7 *<br />
.................................................................................. WeCT1.7 2910<br />
Mobarhani, Amir ....................................................... TuAT9.2 1128<br />
Mochiyama, Hiromi................................................... ThCT1.6 4652<br />
.................................................................................. ThDPT10.3 *<br />
Mohammadi, Mahmood............................................ TuBT3.6 1304<br />
.................................................................................. TuCPT10.9 *<br />
Mohammed, Samer .................................................. TuCT5.7 1749<br />
Mohan, Anush .......................................................... ThBT4.7 4378<br />
Mohta, Kartik ............................................................ WeBT6.8 2708<br />
Mohtat, Arash ........................................................... WeDT9.8 3740<br />
Molinari Tosatti, Lorenzo .......................................... WeCT9.7 3327<br />
Molotchnikoff, Stéphane ........................................... MoBT3.3 518<br />
Mombaur, Katja ........................................................ ThBT5.1 *<br />
Mondada, Francesco................................................ ThCT4.2 4762<br />
.................................................................................. ThCT9.1 4981<br />
Montano, Luis ........................................................... WeCT1.4 2887<br />
.................................................................................. WeDPT10.1 *<br />
Montés Sánchez, Nicolás ......................................... TuCT1.1 1567<br />
Montesano, Luis ....................................................... ThBT6.5 4448<br />
.................................................................................. ThCPT10.17 *<br />
Montiel, J.M.M .......................................................... TuBT2.8 1277<br />
Moon, AJung ............................................................ WeAT1.5 1994<br />
.................................................................................. WeBPT10.2 *<br />
Moon, Hyungpil......................................................... TuBT7.7 1447<br />
Moore, Joseph.......................................................... WeBT6.7 2700<br />
Moore, Richard James Donald ................................. ThCT8.2 4935<br />
Mora, Marta Covadonga........................................... TuCT1.1 1567<br />
Moral del Agua, Diego .............................................. ThBT7.8 4522<br />
Morbidi, Fabio........................................................... WeAT6.6 2229<br />
.................................................................................. WeBPT10.18 *<br />
.................................................................................. WeDT7.4 3608<br />
.................................................................................. ThAPT10.16 *<br />
Moreau, Richard ....................................................... MoBT7.4 659<br />
.................................................................................. TuAPT10.16 *<br />
Morel, Guillaume ...................................................... TuBT5.4 1333<br />
.................................................................................. TuBT5.5 1339<br />
.................................................................................. TuCPT10.10 *<br />
.................................................................................. TuCPT10.11 *<br />
Mori, Taketoshi ......................................................... WeDT2.5 3419<br />
.................................................................................. WeDT7.3 3601
.................................................................................. ThAPT10.5 *<br />
Morimoto, Jun........................................................... WeBT7.1 2715<br />
.................................................................................. WeCT7.3 3185<br />
.................................................................................. ThAT5.8 3975<br />
Morin, Pascal............................................................ WeCT9.8 3335<br />
Morioka, Hiroshi........................................................ ThAT6.5 3998<br />
.................................................................................. ThBPT10.17 *<br />
Morisawa, Mitsuharu ................................................ ThBT5.3 4392<br />
.................................................................................. ThBT5.4 4400<br />
.................................................................................. ThBT5.8 4428<br />
.................................................................................. ThCPT10.13 *<br />
Morozovsky, Nicholas............................................... WeBT7.5 2741<br />
.................................................................................. WeCPT10.20 *<br />
.................................................................................. ThAT8.5 4097<br />
.................................................................................. ThBPT10.23 *<br />
Morris, Aaron............................................................ ThAT2.3 3816<br />
Mörtl, Alexander ....................................................... WeBT1.3 2416<br />
Morton, Ryan............................................................ TuCT1.3 1579<br />
Mörwald, Thomas..................................................... TuAT1.5 813<br />
.................................................................................. TuBPT10.2 *<br />
Mösenlechner, Lorenz.............................................. ThAT9.4 4141<br />
.................................................................................. ThBPT10.25 *<br />
Motegi, Yuichi........................................................... ThCT9.7 5021<br />
Mouret, Jean-Baptiste .............................................. ThBT4.4 4360<br />
.................................................................................. ThCPT10.10 *<br />
Mourikis, Anastasios................................................. MoAT2.3 65<br />
Moutinho, Nuno ........................................................ ThCT5.5 4826<br />
.................................................................................. ThDPT10.14 *<br />
Movafaghpour, Mohamad Ali.................................... ThBT3.3 4299<br />
Mueller, Joerg........................................................... ThCT8.3 4941<br />
Mueller, Steffen ........................................................ WeBT1.5 2430<br />
Muelling, Katharina................................................... MoAT8.3 332<br />
Mukai, Toshiharu...................................................... WeBT1.7 2445<br />
Mukherjee, Ranjan ................................................... ThCT3.6 4749<br />
.................................................................................. ThDPT10.9 *<br />
Müller, Mark Wilfried................................................. ThDT6.1 5113<br />
Muñoz, Victor............................................................ WeAT3.8 2121<br />
Muñoz Hernandez, Laura Elena............................... WeBT6.4 2682<br />
.................................................................................. WeCPT10.16 *<br />
Muntwyler, Simon..................................................... TuAT3.7 919<br />
Murakami, Kazunori.................................................. ThBT9.5 4593<br />
.................................................................................. ThCPT10.26 *<br />
Murasaki, Kazuhiko .................................................. WeDT7.3 3601<br />
Murillo, Ana Cristina ................................................. WeAT9.6 2383<br />
.................................................................................. WeBPT10.27 *<br />
Murphy, Elizabeth..................................................... ThAT3.3 3868<br />
Murphy, Robin .......................................................... MoAT5.6 209<br />
Murray, Richard........................................................ WeAT2.6 2056<br />
.................................................................................. WeBPT10.6 *<br />
.................................................................................. WeCT3.8 3017<br />
Murrieta-Cid, Rafael ................................................. ThBT8.1 4528<br />
Muscio, Giuseppe..................................................... ThBT1.4 4194<br />
.................................................................................. ThCPT10.1<br />
N<br />
*<br />
Naba, Shu................................................................. TuCT7.6 1843<br />
.................................................................................. WeAPT10.18 *<br />
Nadeau, Caroline...................................................... WeBT9.4 2837<br />
.................................................................................. WeCPT10.25 *<br />
Nagai, Takayuki........................................................ TuBT9.2 1520<br />
.................................................................................. TuBT9.5 1540<br />
.................................................................................. TuCPT10.23 *<br />
Nagasaka, Tomomi .................................................. TuAT2.6 872<br />
.................................................................................. TuBPT10.6 *<br />
Nagatani, Keiji .......................................................... MoAT6.1 *<br />
.................................................................................. MoBT6.3 601<br />
Nagpal, Radhika....................................................... ThAT4.6 3923<br />
.................................................................................. ThBT7.3 4488<br />
.................................................................................. ThBPT10.12 *<br />
.................................................................................. ThCT9 CC<br />
.................................................................................. ThCT9.2 4989<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–249–<br />
Nakadai, Kazuhiro .................................................... MoAT3 O<br />
.................................................................................. MoAT3.2 106<br />
.................................................................................. MoAT3.6 131<br />
.................................................................................. MoAT3.8 143<br />
.................................................................................. MoBT3 C<br />
.................................................................................. MoBT3 O<br />
.................................................................................. MoBT3.4 524<br />
.................................................................................. MoBT3.5 530<br />
.................................................................................. MoBPT10.9 *<br />
.................................................................................. TuAPT10.7 *<br />
.................................................................................. TuAPT10.8 *<br />
Nakajima, Hirofumi ................................................... MoAT3.2 106<br />
.................................................................................. MoAT3.6 131<br />
.................................................................................. MoBT3.5 530<br />
.................................................................................. MoBPT10.9 *<br />
.................................................................................. TuAPT10.8 *<br />
Nakajima, Masahiro.................................................. MoAT1.1 1<br />
.................................................................................. MoBT1.3 433<br />
.................................................................................. MoBT1.7 457<br />
.................................................................................. TuCT3.6 1693<br />
.................................................................................. WeAPT10.9 *<br />
Nakamura, Keisuke .................................................. MoAT3.2 106<br />
.................................................................................. MoAT3.6 131<br />
.................................................................................. MoAT3.8 143<br />
.................................................................................. MoBT3.4 524<br />
.................................................................................. MoBPT10.9 *<br />
.................................................................................. TuAPT10.7 *<br />
Nakamura, Kenichi ................................................... ThBT9.5 4593<br />
.................................................................................. ThCPT10.26 *<br />
Nakamura, Tomoaki ................................................. TuBT9.2 1520<br />
.................................................................................. TuBT9.5 1540<br />
.................................................................................. TuCPT10.23 *<br />
Nakamura, Yoshihiko ............................................... MoAT7.1 268<br />
.................................................................................. WeDT2.2 3401<br />
.................................................................................. WeDT9.2 3701<br />
Nakamura, Yutaka.................................................... WeCT1.1 2867<br />
Nakanishi, Hiroki....................................................... MoBT6.7 625<br />
Nakanishi, Jun .......................................................... MoBT8 C<br />
.................................................................................. MoBT8.5 718<br />
.................................................................................. TuAPT10.20 *<br />
Nakano, Mikio........................................................... TuBT9.5 1540<br />
.................................................................................. TuCPT10.23 *<br />
Nakano, Tomoyasu .................................................. WeAT1.6 2000<br />
.................................................................................. WeBPT10.3 *<br />
Nakaoka, Shin'ichiro................................................. WeAT1.6 2000<br />
.................................................................................. WeBPT10.3 *<br />
.................................................................................. ThBT5.3 4392<br />
Nakashima, Hiromichi............................................... WeBT1.7 2445<br />
Nambi, Manikantan................................................... MoBT1.5 445<br />
.................................................................................. TuAPT10.2 *<br />
Naniwa, Keisuke....................................................... TuCT7.2 1817<br />
Narasimhan, Srinivasa ............................................. WeAT9.1 2352<br />
Nardi, Daniele........................................................... WeBT8.6 2796<br />
.................................................................................. WeCPT10.24 *<br />
Narioka, Kenichi ....................................................... ThCT5.7 4838<br />
Natale, Lorenzo ........................................................ TuBT9.3 1526<br />
.................................................................................. WeCT1.5 2895<br />
.................................................................................. WeDT9.1 3694<br />
.................................................................................. WeDPT10.2 *<br />
.................................................................................. ThAT9.6 4154<br />
.................................................................................. ThAT9.7 4161<br />
.................................................................................. ThBPT10.27 *<br />
Natarajan, Saravana K. ............................................ TuAT1.1 786<br />
Natraj, Ashutosh ....................................................... ThAT6.6 4006<br />
.................................................................................. ThBPT10.18 *<br />
Navarro-Alarcon, David ............................................ ThBT1.8 4222<br />
Nazari, Shaghayegh ................................................. TuAT9.2 1128<br />
Nebot, Eduardo ........................................................ ThBT9.6 4601<br />
.................................................................................. ThCPT10.27 *<br />
Negoro, Makoto ........................................................ MoBT1.4 439
.................................................................................. TuAPT10.1 *<br />
Neira, José ............................................................... ThAT6 C<br />
.................................................................................. ThAT6 O<br />
.................................................................................. ThBT6 C<br />
.................................................................................. ThBT6 O<br />
.................................................................................. ThCT6 CC<br />
Nejat, Goldie............................................................. ThBT8.3 4544<br />
Nelson, Bradley J. .................................................... TuAT3.7 919<br />
.................................................................................. TuBT3.5 1297<br />
.................................................................................. TuCPT10.8 *<br />
Nelson, Gabriel......................................................... MoBT5.1 *<br />
Nemec, Bojan ........................................................... ThCT5.6 4832<br />
.................................................................................. ThDPT10.15 *<br />
Nenchev, Dragomir................................................... WeCT7.2 3179<br />
Nerurkar, Esha ......................................................... MoBT2.6 502<br />
.................................................................................. TuAPT10.6 *<br />
Nettleton, Eric........................................................... ThBT2.2 4236<br />
Newman, Paul .......................................................... ThAT3.3 3868<br />
.................................................................................. ThAT6 O<br />
.................................................................................. ThBT6 O<br />
Nguyen, Binh............................................................ TuBT7.5 1433<br />
.................................................................................. TuCPT10.17 *<br />
Nguyen, Chanh-Nghiem........................................... MoBT1.2 427<br />
Nguyen, Kien Cuong ................................................ TuBT8.1 1459<br />
Nguyen, Yann........................................................... WeBT3.3 2532<br />
Nguyen-Tuong, Duy ................................................. MoBT8.2 698<br />
.................................................................................. MoBT8.3 704<br />
Nia Kosari, Sina........................................................ WeBT4.8 2614<br />
Nickl, Mathias ........................................................... TuAT7.7 1055<br />
Nieto, Juan ............................................................... MoAT2.7 92<br />
.................................................................................. ThBT9 CC<br />
.................................................................................. ThBT9.6 4601<br />
.................................................................................. ThCPT10.27 *<br />
Nieto-Granda, Carlos................................................ TuBT2.6 1264<br />
.................................................................................. TuCPT10.6 *<br />
Niiyama, Ryuma ....................................................... TuBT8.7 1499<br />
Nikolic, Janosch........................................................ WeBT6.6 2694<br />
.................................................................................. WeCPT10.18 *<br />
Nili Ahmadabadi, Majid............................................. WeBT7.2 2723<br />
Nill, Scott T. .............................................................. WeBT3.1 2517<br />
Nishii, Tatsuya.......................................................... WeCT7.2 3179<br />
Nishikawa, Satoshi ................................................... TuBT8.7 1499<br />
Noda, Tomoyuki ....................................................... ThAT5.8 3975<br />
Noda, Yoshitaka ....................................................... MoAT3.3 112<br />
Nogawa, Kousuke .................................................... TuCT3.6 1693<br />
.................................................................................. WeAPT10.9 *<br />
Noguchi, Hiroshi ....................................................... WeDT2.5 3419<br />
.................................................................................. ThAPT10.5 *<br />
Noh, Si Tae............................................................... WeDT7.2 3595<br />
Noonan, David.......................................................... TuAT5.5 949<br />
.................................................................................. TuBPT10.11 *<br />
Nori, Francesco ........................................................ WeDT9.1 3694<br />
.................................................................................. ThAT9.6 4154<br />
.................................................................................. ThAT9.7 4161<br />
.................................................................................. ThBT4.3 4354<br />
.................................................................................. ThBPT10.27 *<br />
Nothhelfer, Alexander............................................... TuAT7.7 1055<br />
Nunes, Urbano ......................................................... WeBT1 C<br />
.................................................................................. WeBT1.6 2438<br />
.................................................................................. WeCPT10.3 *<br />
Nuske, Stephen........................................................ MoAT6.3 227<br />
.................................................................................. WeAT9.1 2352<br />
.................................................................................. ThCT8.4 4947<br />
.................................................................................. ThDPT10.22<br />
O<br />
*<br />
O'Dowd, Paul Jason ................................................. ThCT9.3 4995<br />
O'Grady, Rehan........................................................ ThCT4.2 4762<br />
.................................................................................. ThCT9.8 5027<br />
O'Malley, Marcia....................................................... TuCT5.1 1711<br />
.................................................................................. WeAT4 CC<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–250–<br />
.................................................................................. WeAT4 O<br />
.................................................................................. WeBT4 O<br />
.................................................................................. WeCT4 C<br />
.................................................................................. WeCT4 O<br />
Obara, Takeshi ......................................................... MoAT1.2 7<br />
Odedra, Siddharth .................................................... MoAT7.4 286<br />
.................................................................................. MoBPT10.19 *<br />
Odhner, Lael............................................................. TuBT7.3 1420<br />
Ogasawara, Tsukasa................................................ WeAT4.3 2127<br />
Ogata, Tetsuya ......................................................... MoAT3.4 118<br />
.................................................................................. MoBPT10.7 *<br />
.................................................................................. WeCT1.7 2910<br />
Ogawa, Keita ............................................................ ThCT5.7 4838<br />
Oh, Paul Y. ............................................................... TuAT7.2 1022<br />
.................................................................................. WeCT1.8 2916<br />
Oh, Sang-Rok........................................................... ThBT7.2 4483<br />
Oh, Se Min................................................................ TuAT5.8 967<br />
Oh, Se-Young........................................................... TuAT2.1 841<br />
Ohara, Kenichi.......................................................... MoAT1.5 25<br />
.................................................................................. MoBT1.2 427<br />
.................................................................................. MoBPT10.2 *<br />
.................................................................................. TuAT1.4 807<br />
.................................................................................. TuBPT10.1 *<br />
Ohashi, Shigeo ......................................................... TuBT3.7 1309<br />
Oishi, Shuji ............................................................... WeAT2.1 2020<br />
Ok, Sungsuk ............................................................. MoAT7.1 268<br />
Okada, Masafumi ..................................................... MoBT9.5 762<br />
.................................................................................. TuAPT10.23 *<br />
.................................................................................. WeDT8.6 3673<br />
.................................................................................. ThAPT10.21 *<br />
.................................................................................. ThCT9.7 5021<br />
Okada, Shima........................................................... TuCT9.6 1946<br />
.................................................................................. WeAPT10.24 *<br />
Okamoto, Jun ........................................................... MoBT9.3 750<br />
Okamoto, Shogo....................................................... WeCT4.6 3060<br />
.................................................................................. WeDPT10.12 *<br />
Okamura, Allison M. ................................................. WeBT4.3 2584<br />
.................................................................................. ThDT7 CC<br />
Oki, Tomohisa .......................................................... MoBT6.7 625<br />
Okuno, Hiroshi G. ..................................................... MoAT3 O<br />
.................................................................................. MoAT3.1 *<br />
.................................................................................. MoAT3.4 118<br />
.................................................................................. MoBT3 CC<br />
.................................................................................. MoBT3 O<br />
.................................................................................. MoBPT10.7 *<br />
.................................................................................. WeCT1.7 2910<br />
Okuno, Keisuke ........................................................ WeDT1.7 3381<br />
Okutomi, Masatoshi.................................................. ThBT6.7 4463<br />
Oliveira, Miguel......................................................... WeAT7.7 2286<br />
Oliver, Gabriel A. ...................................................... WeDT6.7 3577<br />
Olmi, Roberto ........................................................... ThBT9.8 4615<br />
Olofsson, Bjorn ......................................................... ThCT2.6 4704<br />
.................................................................................. ThDPT10.6 *<br />
Olson, Edwin ............................................................ TuCT1.3 1579<br />
.................................................................................. ThAT3 C<br />
.................................................................................. ThAT3.5 3881<br />
.................................................................................. ThBT2.7 4271<br />
.................................................................................. ThBPT10.8 *<br />
Omer, Aiman ............................................................ WeCT7.8 3221<br />
Omura, Suguru ......................................................... MoAT9.5 395<br />
.................................................................................. MoBPT10.26 *<br />
Onal, Cagdas Denizel............................................... MoBT9 C<br />
.................................................................................. MoBT9.4 756<br />
.................................................................................. TuAPT10.22 *<br />
Ong, Lee-Ling Sharon .............................................. ThAT4.7 3931<br />
Oriolo, Giuseppe....................................................... MoBT2 C<br />
.................................................................................. MoBT2.1 469<br />
Osswald, Marc.......................................................... ThDT5.4 5107<br />
Osswald, Stefan ....................................................... ThCT5.8 4844<br />
Oster, Stéphane ....................................................... MoAT1.7 39
Osuka, Koichi ........................................................... TuCT7.2 1817<br />
Ota, Jun.................................................................... WeAT8.3 2314<br />
.................................................................................. ThCT2 C<br />
.................................................................................. ThCT2.5 4698<br />
.................................................................................. ThDPT10.5 *<br />
Otsuka, Takuma ....................................................... MoAT3.4 118<br />
.................................................................................. MoBPT10.7 *<br />
Ott, Christian............................................................. MoBT7.5 665<br />
.................................................................................. TuAPT10.17 *<br />
.................................................................................. ThBT5.7 4420<br />
Oudeyer, Pierre-Yves............................................... TuBT8.2 1465<br />
Overholt, Jim ............................................................ ThAT8.4 4091<br />
.................................................................................. ThBPT10.22 *<br />
Özkil, Ali Gürcan....................................................... TuAT2.2 847<br />
O'Reilly, Tom ............................................................ WeCT6.3<br />
P<br />
3132<br />
Paanajärvi, Janne Olavi............................................ MoAT2.2 59<br />
Padoy, Nicolas.......................................................... WeAT3.5 2102<br />
.................................................................................. WeBPT10.8 *<br />
Pagac, Daniel ........................................................... WeDT8.8 3686<br />
Pahlajani, Chetan ..................................................... ThAT4.4 3911<br />
.................................................................................. ThBPT10.10 *<br />
Paik, Jamie ............................................................... MoAT9.8 414<br />
Palli, Gianluca........................................................... TuCT6.4 1775<br />
.................................................................................. WeAPT10.13 *<br />
Pallottino, Lucia ........................................................ WeBT9.1 2817<br />
Palyart Lamarche, Jean Christophe ......................... ThAT5.4 3951<br />
.................................................................................. ThBPT10.13 *<br />
Pane, Salvador......................................................... TuBT3.5 1297<br />
.................................................................................. TuCPT10.8 *<br />
Pangercic, Dejan ...................................................... ThBT2.6 4263<br />
.................................................................................. ThCPT10.6 *<br />
Papadopoulos, Evangelos........................................ MoBT6.2 595<br />
.................................................................................. WeCT2 C<br />
.................................................................................. WeCT2.1 2922<br />
Papadopoulos, Georgios.......................................... WeDT6.3 3551<br />
Papanikolopoulos, Nikos .......................................... TuAT9.8 1167<br />
Park, Jaesik.............................................................. WeCT9.2 3290<br />
Park, Myoung Soo.................................................... ThBT7.2 4483<br />
Park, Yong-Lae......................................................... ThBT7.3 4488<br />
Parker, Chris............................................................. WeAT1.5 1994<br />
.................................................................................. WeBPT10.2 *<br />
.................................................................................. WeDT1.4 3361<br />
.................................................................................. ThAPT10.1 *<br />
Parker, Lynne ........................................................... WeAT2 C<br />
.................................................................................. WeAT2.4 2044<br />
.................................................................................. WeBPT10.4 *<br />
Parks, Perry.............................................................. MoBT5.6 574<br />
.................................................................................. TuAPT10.12 *<br />
Parnandi, Avinash .................................................... WeBT1.1 2403<br />
Parra Vega, Vicente ................................................. TuCT1.8 1612<br />
Parusel, Sven ........................................................... WeDT1.5 3367<br />
.................................................................................. ThAPT10.2 *<br />
Pascoal, Antonio....................................................... WeCT6.8 3166<br />
Pashkevich, Anatol................................................... ThAT7.2 4034<br />
Paskarbeit, Jan......................................................... ThAT1.6 3776<br />
.................................................................................. ThBPT10.3 *<br />
Passaglia, Andrea .................................................... ThAT1.5 3770<br />
.................................................................................. ThBPT10.2 *<br />
Passig, Georg........................................................... WeAT3.6 2108<br />
.................................................................................. WeBPT10.9 *<br />
Pastor, Peter............................................................. MoAT8.2 325<br />
.................................................................................. MoAT8.8 365<br />
.................................................................................. ThCT1.4 4639<br />
.................................................................................. ThDPT10.1 *<br />
Patel, Rajnikant V..................................................... MoBT7.3 653<br />
.................................................................................. WeBT3.6 2551<br />
.................................................................................. WeCPT10.9 *<br />
Patoglu, Volkan ........................................................ ThCT7.5 4917<br />
.................................................................................. ThDPT10.20 *<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–251–<br />
Patrikalakis, Nicholas ............................................... WeDT6.3 3551<br />
Pattacini, Ugo ........................................................... WeCT1.5 2895<br />
.................................................................................. WeDPT10.2 *<br />
.................................................................................. ThAT9.6 4154<br />
.................................................................................. ThBPT10.27 *<br />
Paull, Liam................................................................ TuAT2.3 853<br />
.................................................................................. TuAT2.7 880<br />
Paulus, Dietrich ........................................................ WeDT5.8 3545<br />
Payandeh, Shahram................................................. WeAT3.4 2096<br />
.................................................................................. WeBPT10.7 *<br />
Payne, Christopher................................................... TuAT5.5 949<br />
.................................................................................. TuBPT10.11 *<br />
Pchelkin, Stepan....................................................... ThDT5.2 5094<br />
Pedrocchi, Nicola...................................................... WeCT9.7 3327<br />
Pedrono, Brice.......................................................... WeBT4.2 2578<br />
Peer, Angelika .......................................................... MoAT4 C<br />
.................................................................................. MoAT4 O<br />
.................................................................................. TuAT6.7 1002<br />
Pehlivan, Utku .......................................................... TuCT5.1 1711<br />
Peng, Huei................................................................ WeCT3.6 3004<br />
.................................................................................. WeDPT10.9 *<br />
Peng, Peter............................................................... WeBT3.4 2539<br />
Penning, Ryan .......................................................... ThDT7.1 5139<br />
Peralta Cabezas, José Luis...................................... WeAT7.2 2255<br />
Perdereau, Véronique .............................................. TuBT8.1 1459<br />
Pereira, Flávio Garcia............................................... WeDT7.6 3620<br />
.................................................................................. ThAPT10.18 *<br />
Perez, Alejandro ....................................................... ThBT3.4 4307<br />
.................................................................................. ThCPT10.7 *<br />
Pérez Arias, Antonia................................................. WeCT4.5 3053<br />
.................................................................................. WeDPT10.11 *<br />
Perez-Arancibia, Nestor O........................................ TuAT8.7 1107<br />
Pericet-Camara, Ramon........................................... TuCT9.1 1913<br />
Perlmutter, Leah ....................................................... ThAT8.8 4115<br />
Perrin, Nicolas Yves ................................................. ThBT5.5 4408<br />
.................................................................................. ThCPT10.14 *<br />
Persson, Erik ............................................................ MoAT6.4 235<br />
.................................................................................. MoBPT10.16 *<br />
Peterlik, Igor ............................................................. WeBT4.7 2608<br />
Peters, Jan ............................................................... MoAT8 C<br />
.................................................................................. MoAT8.3 332<br />
.................................................................................. MoAT8.4 338<br />
.................................................................................. MoBT8.2 698<br />
.................................................................................. MoBT8.3 704<br />
.................................................................................. MoBPT10.22 *<br />
.................................................................................. TuBT9.6 1548<br />
.................................................................................. TuCPT10.24 *<br />
Peters, Steven .......................................................... MoAT7.6 298<br />
.................................................................................. MoBPT10.21 *<br />
Peterson, Gilbert....................................................... TuAT2.4 859<br />
.................................................................................. TuBPT10.4 *<br />
Peterson, Kevin ........................................................ ThDT4.4 5080<br />
Petit, Antoine ............................................................ MoBT6.6 619<br />
.................................................................................. TuAPT10.15 *<br />
Petit, Florian ............................................................. TuCT7.5 1836<br />
.................................................................................. WeAPT10.17 *<br />
.................................................................................. ThBT1.2 4180<br />
Pêtrès, Clément........................................................ WeDT6.6 3571<br />
.................................................................................. ThAPT10.15 *<br />
Petric, Tadej ............................................................. ThCT5.6 4832<br />
.................................................................................. ThDPT10.15 *<br />
Petrisor, Doru ........................................................... TuAT5.4 943<br />
.................................................................................. TuBPT10.10 *<br />
Petruska, Andrew ..................................................... WeDT7.8 3632<br />
Petsch, Susanne ...................................................... TuBT1.8 1221<br />
Pettersen, Kristin Y................................................... MoAT6.6 247<br />
.................................................................................. MoBPT10.18 *<br />
.................................................................................. ThCT2.2 4676<br />
Peynot, Thierry ......................................................... WeBT2 C<br />
.................................................................................. WeBT2.5 2489
.................................................................................. WeCPT10.5 *<br />
Pfeifer, Rolf............................................................... ThAT9.8 4168<br />
Pfeiffer, Kai............................................................... MoAT6.5 241<br />
.................................................................................. MoBPT10.17 *<br />
Pflimlin, Jean-Michel................................................. WeBT9.5 2843<br />
.................................................................................. WeCPT10.26 *<br />
Pham, Hang.............................................................. ThBT7.4 4496<br />
.................................................................................. ThCPT10.19 *<br />
Pham, Minh Tu ......................................................... MoBT7.4 659<br />
.................................................................................. MoBT9.1 738<br />
.................................................................................. TuAPT10.16 *<br />
Phan, Samson.......................................................... WeCT3.4 2992<br />
.................................................................................. WeCT3.5 2998<br />
.................................................................................. WeDPT10.7 *<br />
.................................................................................. WeDPT10.8 *<br />
Philippsen, Roland.................................................... TuAT7 CC<br />
.................................................................................. TuAT7.4 1036<br />
.................................................................................. TuBPT10.16 *<br />
Phung, Tri Cong ....................................................... TuBT7.7 1447<br />
Piat, Emmanuel ........................................................ MoAT1.7 39<br />
Pierce, Matthew........................................................ MoAT9.6 402<br />
.................................................................................. MoBPT10.27 *<br />
Pierri, Francesco ...................................................... ThBT1.4 4194<br />
.................................................................................. ThCPT10.1 *<br />
Pierrot, François ....................................................... ThDT4 C<br />
.................................................................................. ThDT4.2 5067<br />
Pietrusinski, Maciej................................................... ThCT7.1 4893<br />
Pinciroli, Carlo .......................................................... ThCT9.1 4981<br />
.................................................................................. ThCT9.8 5027<br />
Pineda, Elvine Philip................................................. ThAT8.4 4091<br />
.................................................................................. ThBPT10.22 *<br />
Pini, Giovanni ........................................................... ThCT9.8 5027<br />
Pires, Gabriel............................................................ WeBT1.6 2438<br />
.................................................................................. WeCPT10.3 *<br />
Pirker, Katrin............................................................. ThAT6.4 3990<br />
.................................................................................. ThBPT10.16 *<br />
Pistillo, Antonio......................................................... WeCT4.4 3047<br />
.................................................................................. WeDT2.4 3413<br />
.................................................................................. WeDPT10.10 *<br />
.................................................................................. ThAPT10.4 *<br />
Pitzer, Benjamin ....................................................... TuBT7.2 1413<br />
Pivtoraiko, Mihail ...................................................... WeAT5.4 2172<br />
.................................................................................. WeBPT10.13 *<br />
Pizarro, Oscar........................................................... TuBT2.3 1242<br />
.................................................................................. TuBT9.4 1533<br />
.................................................................................. TuCPT10.22 *<br />
.................................................................................. ThBT6.6 4455<br />
.................................................................................. ThCT3.2 4722<br />
.................................................................................. ThCPT10.18 *<br />
Plaku, Erion .............................................................. TuBT5.6 1346<br />
.................................................................................. TuCPT10.12 *<br />
.................................................................................. WeAT3 CC<br />
Plumet, Frederic ....................................................... WeDT6.6 3571<br />
.................................................................................. ThAPT10.15 *<br />
Poggendorf, Maik ..................................................... WeDT1.3 3355<br />
Poignet, Philippe....................................................... WeBT3.5 2545<br />
.................................................................................. WeCPT10.8 *<br />
Pollefeys, Marc......................................................... TuCT2.1 1618<br />
.................................................................................. TuCT2.6 1655<br />
.................................................................................. WeAPT10.6 *<br />
.................................................................................. ThAT6.7 4012<br />
Polushin, Ilia G. ........................................................ MoBT7.3 653<br />
Pomerleau, Francois ................................................ ThAT2.4 3824<br />
.................................................................................. ThBPT10.4 *<br />
Popa, Dan................................................................. TuCT3.7 1699<br />
Popovic, Mila ............................................................ TuAT6.5 987<br />
.................................................................................. TuBPT10.14 *<br />
Porez, Mathieu ......................................................... TuCT8.7 1901<br />
Porquis, Lope Ben.................................................... WeDT3.7 3488<br />
Porta, Josep M ......................................................... WeBT5.7 2646<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–252–<br />
Portello, Alban .......................................................... MoAT3.7 137<br />
Porter, Judson .......................................................... TuAT8.6 1099<br />
.................................................................................. TuBPT10.21 *<br />
Pounds, Paul ............................................................ WeBT6.1 2660<br />
Prankl, Johann.......................................................... TuAT1.5 813<br />
.................................................................................. TuBPT10.2 *<br />
Prattichizzo, Domenico............................................. TuAT6.8 1008<br />
.................................................................................. WeDT7 CC<br />
Prestes, Edson ......................................................... TuAT9.1 1122<br />
Preusche, Carsten.................................................... MoAT4.7 177<br />
.................................................................................. MoBT7.5 665<br />
.................................................................................. TuAPT10.17 *<br />
Prorok, Amanda........................................................ WeCT8.3 3241<br />
.................................................................................. WeCT9.5 3313<br />
.................................................................................. WeDPT10.26 *<br />
Protzel, Peter............................................................ TuBT2.2 1234<br />
Przybylski, Markus.................................................... TuCT6.2 1761<br />
.................................................................................. TuCT6.5 1781<br />
.................................................................................. WeAPT10.14 *<br />
Puerto, Gustavo Armando ........................................ WeAT9.4 2371<br />
.................................................................................. WeBPT10.25 *<br />
Puzik, Arnold ............................................................ ThCT2.6 4704<br />
.................................................................................. ThDPT10.6 *<br />
Py, Frederic .............................................................. WeCT6.3<br />
Q<br />
3132<br />
Qin, Lei ..................................................................... ThBT7.6 4508<br />
.................................................................................. ThCPT10.21 *<br />
Qu, Xingda................................................................ ThCT7.6 4923<br />
.................................................................................. ThDPT10.21 *<br />
Quebe, Stephen ....................................................... ThDT8.2 5173<br />
Quek, Zhan Fan........................................................ WeCT3.4 2992<br />
.................................................................................. WeCT3.5 2998<br />
.................................................................................. WeDPT10.7 *<br />
.................................................................................. WeDPT10.8 *<br />
Quinn, Roger, D........................................................ MoAT5.4 197<br />
.................................................................................. MoAT5.6 209<br />
.................................................................................. MoAT5.7 215<br />
.................................................................................. MoBPT10.13 *<br />
.................................................................................. MoBPT10.15<br />
R<br />
*<br />
Radig, Bernd............................................................. ThBT9 C<br />
.................................................................................. ThBT9.4 4587<br />
.................................................................................. ThCPT10.25 *<br />
Radkhah, Katayon .................................................... ThCT5.3 4811<br />
Radrich, Helmuth...................................................... WeDT1.6 3375<br />
.................................................................................. ThAPT10.3 *<br />
Ragan, Matthew ....................................................... WeCT6.5 3147<br />
.................................................................................. WeDPT10.17 *<br />
Rai, Vijeth ................................................................. ThCT4.5 4783<br />
.................................................................................. ThDPT10.11 *<br />
Raibert, Marc ............................................................ TuPL.1 *<br />
Rajan, Kanna............................................................ WeCT6.3 3132<br />
Ramamoorthy, Subramanian.................................... WeDT8.7 3679<br />
Ramos, Oscar........................................................... ThAT9.2 4127<br />
Randazzo, Marco ..................................................... ThAT9.7 4161<br />
Rander, Peter ........................................................... ThAT6.8 4020<br />
Ranganathan, Ananth............................................... TuCT2.1 1618<br />
.................................................................................. ThAT6.3 3982<br />
Rañó, Iñaki ............................................................... WeAT8.8 2346<br />
Raschendörfer, Lars ................................................. WeDT3.5 3474<br />
.................................................................................. ThAPT10.8 *<br />
Rasmussen, Christopher .......................................... ThAT8.3 4084<br />
Rasolzadeh, Babak .................................................. WeDT1.1 3342<br />
Ratnasingam, Sivalogeswaran ................................. WeDT3.1 3446<br />
Rauter, Georg........................................................... WeCT4.7 3068<br />
Rawlik, Konrad ......................................................... MoBT8.5 718<br />
.................................................................................. TuAPT10.20 *<br />
Ray, Christopher....................................................... ThAPT10.16 *<br />
Raz, Ariel .................................................................. WeDT5.7 3539<br />
Reckhaus, Michael ................................................... TuAT7.3 1030
Reddy, Prashant....................................................... WeAT8.5 2327<br />
.................................................................................. WeBPT10.23 *<br />
Reggente, Matteo..................................................... ThCT9.5 5007<br />
.................................................................................. ThDPT10.26 *<br />
Régnier, Stéphane.................................................... TuAT3.3 894<br />
.................................................................................. TuAT3.6 913<br />
.................................................................................. TuBPT10.9 *<br />
.................................................................................. TuCT3.5 1687<br />
.................................................................................. WeAPT10.8 *<br />
Rehder, Joern........................................................... MoAT6.3 227<br />
Reiher, Stephane...................................................... TuCT7.7 1849<br />
Reilink, Rob .............................................................. TuAT5.3 937<br />
.................................................................................. WeAT3.1 2076<br />
Reina, Giulio............................................................. MoAT6.7 255<br />
Reinecke, Jens......................................................... TuBT6.3 1366<br />
.................................................................................. ThBT1.7 4215<br />
Reiter, Austin............................................................ WeAT9.7 2390<br />
Rekleitis, Georgios ................................................... MoBT6.2 595<br />
Rekleitis, Ioannis ...................................................... ThDT3 C<br />
.................................................................................. ThDT3.3 5048<br />
.................................................................................. ThDT3.4 5054<br />
Remy, C. David ........................................................ MoAT5.3 190<br />
.................................................................................. MoBT5.4 562<br />
.................................................................................. TuAPT10.10 *<br />
Ren, Hongliang......................................................... WeAT3.2 2083<br />
Ren, Wei................................................................... ThCT1.5 4645<br />
.................................................................................. ThDPT10.2 *<br />
Ren, Xiaofeng........................................................... TuAT1.6 821<br />
.................................................................................. TuBPT10.3 *<br />
.................................................................................. ThCT6.3 4850<br />
Renaud, Pierre ......................................................... WeBT3.8 2564<br />
Renzaglia, Alessandro.............................................. TuCT2.7 1661<br />
.................................................................................. WeBT2.1 2460<br />
Resende, Cassius .................................................... ThAT1.1 3746<br />
Rétornaz, Philippe .................................................... ThCT4.2 4762<br />
Revzen, Shai ............................................................ ThCT4.7 4797<br />
Riazuelo, Luis........................................................... TuBT2.8 1277<br />
Richa, Rogerio.......................................................... WeCT2.6 2953<br />
.................................................................................. WeDPT10.6 *<br />
Richardson, Andrew ................................................. ThAT3.5 3881<br />
.................................................................................. ThBPT10.8 *<br />
Richier, Mathieu........................................................ ThBT9.1 4569<br />
Richtsfeld, Andreas .................................................. TuBT1.5 1201<br />
.................................................................................. TuCPT10.2 *<br />
Rickert, Markus......................................................... TuAT7.8 1063<br />
Riener, Robert .......................................................... WeCT4.7 3068<br />
Righetti, Ludovic....................................................... MoAT8.2 325<br />
.................................................................................. MoAT8.8 365<br />
.................................................................................. ThCT1 CC<br />
.................................................................................. ThCT1.4 4639<br />
.................................................................................. ThDPT10.1 *<br />
Rimon, Elon.............................................................. TuCT6 CC<br />
Rios-Martinez, Jorge ................................................ WeAT1.8 2014<br />
Risi, Sebastian.......................................................... WeBT8.7 2802<br />
Ristic-Durrant, Danijela............................................. TuAT1.1 786<br />
Ritter, Helge Joachim ............................................... TuBT7.4 1427<br />
.................................................................................. TuCPT10.16 *<br />
.................................................................................. WeCT2.5 2947<br />
.................................................................................. WeDPT10.5 *<br />
Rituerto, Jorge.......................................................... WeAT9.6 2383<br />
.................................................................................. WeBPT10.27 *<br />
Ritz, Robin................................................................ ThDT8.3 5179<br />
Ritzmann, Roy Earl................................................... MoAT5.7 215<br />
Rives, Patrick............................................................ WeCT9.6 3320<br />
.................................................................................. WeDPT10.27 *<br />
.................................................................................. ThBT2.3 4242<br />
Riviere, Cameron...................................................... ThBT7.8 4522<br />
.................................................................................. ThDT7.4 5160<br />
Roa, Maximo A......................................................... TuCT6.3 1768<br />
.................................................................................. ThBT5.7 4420<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–253–<br />
Robert, Thomas........................................................ ThAT5.7 3969<br />
Robertsson, Anders.................................................. ThCT2.6 4704<br />
.................................................................................. ThDPT10.6 *<br />
Robuffo Giordano, Paolo .......................................... MoAT4.5 163<br />
.................................................................................. MoBPT10.11 *<br />
.................................................................................. WeAT6.4 2215<br />
.................................................................................. WeBPT10.16 *<br />
.................................................................................. WeCT4.3 3039<br />
Rocco, Paolo ............................................................ WeCT3.1 2971<br />
Rodemann, Tobias ................................................... MoAT3.2 106<br />
.................................................................................. MoAT3.6 131<br />
.................................................................................. MoBPT10.9 *<br />
.................................................................................. TuBT1.3 1187<br />
Röder, Thorsten........................................................ WeDT1.6 3375<br />
.................................................................................. ThAPT10.3 *<br />
Rodriguez, Alberto.................................................... TuCT6.8 1804<br />
Roesthuis, Roy ......................................................... WeBT3.7 2557<br />
Rogers III, John G. ................................................... TuBT2.6 1264<br />
.................................................................................. TuCPT10.6 *<br />
Rognini, Giulio .......................................................... ThCT1.8 4664<br />
Rogoff, Joshua ......................................................... ThCT4.6 4790<br />
.................................................................................. ThDPT10.12 *<br />
Rollinson, David........................................................ MoAT5.8 221<br />
.................................................................................. TuAT8.2 1075<br />
Romero-Ramirez, Miguel-Angel ............................... WeDT6.6 3571<br />
.................................................................................. ThAPT10.15 *<br />
Rosa, Benoît............................................................. TuBT5.5 1339<br />
.................................................................................. TuCPT10.11 *<br />
Rotea, Mario ............................................................. TuBT1.2 1180<br />
Roth, Bernard ........................................................... TuPL C<br />
Rotinat, Christine ...................................................... MoAT1.8 45<br />
Rouat, Jean .............................................................. MoBT3.3 518<br />
Roumeliotis, Stergios................................................ MoAT2.3 65<br />
.................................................................................. MoAT2.8 98<br />
.................................................................................. MoBT2.6 502<br />
.................................................................................. TuAPT10.6 *<br />
Rucker, Caleb........................................................... WeBT3.1 2517<br />
.................................................................................. ThAT1.4 3764<br />
.................................................................................. ThBPT10.1 *<br />
Ruehr, Thomas......................................................... ThBT2.6 4263<br />
.................................................................................. ThCPT10.6 *<br />
Ruepp, Oliver............................................................ WeCT9.3 3297<br />
Ruggiero, Fabio ........................................................ ThBT1.4 4194<br />
.................................................................................. ThCPT10.1 *<br />
Ruhnke, Michael....................................................... TuBT2.4 1249<br />
.................................................................................. TuCPT10.4 *<br />
Ruini, Fabio .............................................................. ThCT9.6 5015<br />
.................................................................................. ThDPT10.27 *<br />
Ruppel, Alexander .................................................... WeAT9.3 2365<br />
Rus, Daniela ............................................................. MoBT9.4 756<br />
.................................................................................. TuAPT10.22 *<br />
.................................................................................. WeCT5.2 3087<br />
.................................................................................. WeDT8.2 3645<br />
.................................................................................. ThCT4.8 4803<br />
.................................................................................. ThDT8.4 5187<br />
Russell, Paul T. ........................................................ WeBT3.1 2517<br />
Russell, Shawn......................................................... ThAT5.7 3969<br />
Rusu, Radu Bogdan ................................................. ThBT2.4 4249<br />
.................................................................................. ThCPT10.4 *<br />
Rüther, Matthias ....................................................... ThAT6.4 3990<br />
.................................................................................. ThBPT10.16 *<br />
Rutter, Brandon ........................................................ MoAT5.7 215<br />
Rybok, Lukas............................................................ ThCT5.4 4819<br />
.................................................................................. ThDPT10.13 *<br />
Rydén, Fredrik .......................................................... WeBT4.8 2614<br />
Ryden, Thomas ........................................................ MoAT4.1 *<br />
Rye, David ................................................................ MoBT8.6 725<br />
.................................................................................. TuAPT10.21 *<br />
Ryu, Jee-Hwan ......................................................... MoAT4.7 177<br />
Ryu, Seok Chang ..................................................... WeBT3.8 2564
S<br />
Saab, Layale............................................................. ThAT9.2 4127<br />
Saad, Maarouf.......................................................... WeAT8.7 2340<br />
Saarinen, Jari Pekka ................................................ MoAT2.2 59<br />
Sabattini, Lorenzo..................................................... WeAT8.4 2321<br />
.................................................................................. WeBPT10.22 *<br />
Sadeghian, Hamid.................................................... ThAT1.2 3752<br />
Saeedi Gharahbolagh, Sajad ................................... TuAT2.3 853<br />
.................................................................................. TuAT2.7 880<br />
Saenko, Kate............................................................ TuAT1.2 793<br />
Saffiotti, Alessandro.................................................. ThCT9.5 5007<br />
.................................................................................. ThDPT10.26 *<br />
Sahai, Ranjana......................................................... TuCT9.2 1919<br />
Saida, Masao............................................................ MoAT7.8 311<br />
Saito, Takefumi......................................................... ThCT7.2 4899<br />
Sakagami, Norimitsu ................................................ ThCT3.7 4756<br />
Sakamoto, Kiyoshi.................................................... WeAT2.3 2036<br />
Sakurai, Tatsuma ..................................................... WeDT3.8 3494<br />
Salaris, Paolo ........................................................... WeBT9.1 2817<br />
.................................................................................. ThAT1.5 3770<br />
.................................................................................. ThBPT10.2 *<br />
Salisbury, Kenneth ................................................... WeBT4.1 2570<br />
Samarasekera, Supun.............................................. ThAT3.4 3874<br />
.................................................................................. ThBPT10.7 *<br />
Sammut, Claude....................................................... MoBT8.7 732<br />
Sanahuja, Guillaume ................................................ WeBT6.4 2682<br />
.................................................................................. WeCPT10.16 *<br />
Sanchez, Fernando .................................................. TuBT8.8 1507<br />
Sanchez Fibla, Marti................................................. TuAT8.8 1115<br />
Sanchez Plazas, Oscar ............................................ WeCT5.4 3101<br />
.................................................................................. WeDPT10.13 *<br />
Sandini, Giulio .......................................................... ThAT9.7 4161<br />
Sandini, Giulio .......................................................... ThBT4.3 4354<br />
Sanemasa, Toru....................................................... TuCT5.2 1717<br />
Sangkyu, Yi .............................................................. ThAT6.5 3998<br />
.................................................................................. ThBPT10.17 *<br />
Sani, Hamidreza....................................................... TuCT9.3 1927<br />
Sankai, Yoshiyuki ..................................................... TuCT5.5 1737<br />
.................................................................................. WeAPT10.11 *<br />
.................................................................................. ThCT7.2 4899<br />
Sano, Kumiko ........................................................... WeDT3.3 3460<br />
Santos, Cristina ........................................................ WeAT7.7 2286<br />
Santos, Milton César Paes....................................... WeDT7.6 3620<br />
.................................................................................. ThAPT10.18 *<br />
Santos Sanchez, Omar Jacobo................................ WeBT6.4 2682<br />
.................................................................................. WeCPT10.16 *<br />
Santos-Victor, José .................................................. ThCT5.5 4826<br />
.................................................................................. ThDPT10.14 *<br />
Sarcinelli-Filho, Mario ............................................... ThAT1.1 3746<br />
Sardellitti, Irene......................................................... MoAT9.2 378<br />
.................................................................................. WeCT3 CC<br />
.................................................................................. ThAT7 C<br />
.................................................................................. ThAT7.1 4026<br />
Sarholz, Frederik ...................................................... ThBT9.4 4587<br />
.................................................................................. ThCPT10.25 *<br />
Saripalli, Srikanth...................................................... WeAT6 O<br />
.................................................................................. WeBT6 C<br />
.................................................................................. WeBT6 O<br />
Sarria, Javier Francisco............................................ TuBT8.8 1507<br />
Saruwatari, Hiroshi ................................................... MoBT3.2 *<br />
Sasaki, Hironobu ...................................................... ThCT2.1 4670<br />
Sato, Fuyuki.............................................................. WeCT7.2 3179<br />
Sato, Katsunari......................................................... MoAT4 O<br />
.................................................................................. MoAT4.4 157<br />
.................................................................................. MoBPT10.10 *<br />
Sato, Takahide ......................................................... TuCT8.3 1875<br />
.................................................................................. TuCT8.4 1881<br />
.................................................................................. WeAPT10.19 *<br />
Sato, Tomomasa ...................................................... WeDT2.5 3419<br />
.................................................................................. WeDT7.3 3601<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–254–<br />
.................................................................................. ThAPT10.5 *<br />
Scaccia, Milena ........................................................ ThDT3.4 5054<br />
Scandaroli, Glauco Garcia........................................ WeCT9.8 3335<br />
Scaramuzza, Davide ................................................ TuCT2 CC<br />
.................................................................................. TuCT2.7 1661<br />
.................................................................................. ThBT6.8 4469<br />
Schaal, Stefan .......................................................... MoAT8.2 325<br />
.................................................................................. MoAT8.8 365<br />
.................................................................................. WeDT2.3 3407<br />
.................................................................................. ThCT1.4 4639<br />
.................................................................................. ThDPT10.1 *<br />
Schäfer, Felix............................................................ TuAT5.4 943<br />
.................................................................................. TuBPT10.10 *<br />
Schairer, Timo .......................................................... ThBT2.1 4229<br />
Schauerte, Boris ....................................................... TuBT1.1 1173<br />
Scherer, Sebastian ................................................... MoAT6.3 227<br />
.................................................................................. WeAT6.3 2207<br />
Scheuer, Alexis......................................................... ThAT8.6 4103<br />
.................................................................................. ThBPT10.24 *<br />
Schiele, Andre .......................................................... WeAT4.8 2158<br />
Schilling, Andreas..................................................... ThBT2.1 4229<br />
Schindler, Thorsten .................................................. TuBT7.5 1433<br />
.................................................................................. TuCPT10.17 *<br />
Schlaefer, Steven ..................................................... MoAT9.7 408<br />
Schlegel, Christian.................................................... WeAT2 CC<br />
.................................................................................. WeAT2.7 2064<br />
Schmidt-Rohr, Sven R.............................................. ThCT1.3 4633<br />
Schmidt-Wetekam, Christopher................................ WeBT7.5 2741<br />
.................................................................................. WeCPT10.20 *<br />
.................................................................................. ThAT8.5 4097<br />
.................................................................................. ThBPT10.23 *<br />
Schmidts, Alexander Markus.................................... TuAT6.7 1002<br />
Schmit, Nicolas......................................................... MoBT9.5 762<br />
.................................................................................. TuAPT10.23 *<br />
Schneider, Axel ........................................................ ThAT1.6 3776<br />
.................................................................................. ThBPT10.3 *<br />
Schneider, Jeff.......................................................... MoAT8.7 357<br />
.................................................................................. TuAT8.1 1069<br />
Schneider, Robert..................................................... TuBT5.3 1327<br />
Schneider, Ulrich ...................................................... ThCT2.6 4704<br />
.................................................................................. ThDPT10.6 *<br />
Schoelkopf, Bernhard ............................................... MoAT8.3 332<br />
.................................................................................. MoBT8.2 698<br />
Schoen, Timothy Ryan ............................................. ThCT4.8 4803<br />
Schrimpf, Johannes.................................................. WeBT9.8 2861<br />
Schroeter, Christof.................................................... WeBT1.5 2430<br />
Schultz, Tanja........................................................... ThCT5.4 4819<br />
.................................................................................. ThDPT10.13 *<br />
Schultz, Ulrik Pagh ................................................... WeDT8.4 3659<br />
.................................................................................. ThAPT10.19 *<br />
Schulz, Dirk .............................................................. WeDT7.5 3614<br />
.................................................................................. ThAPT10.17 *<br />
Schwager, Mac......................................................... ThDT8 CC<br />
.................................................................................. ThDT8.4 5187<br />
Schwartz, Ira............................................................. ThAT4.1 *<br />
.................................................................................. ThAT4.3 3905<br />
Schwartz, Matthijs P ................................................. TuAT5.3 937<br />
Schwartz, Maxim ...................................................... TuAT9.6 1154<br />
.................................................................................. TuBPT10.24 *<br />
Scioni, Enea ............................................................. WeCT4.2 3031<br />
Secchi, Cristian......................................................... MoAT4 O<br />
.................................................................................. MoAT4.5 163<br />
.................................................................................. MoBPT10.11 *<br />
.................................................................................. WeAT8.4 2321<br />
.................................................................................. WeBPT10.22 *<br />
.................................................................................. ThBT9.8 4615<br />
Sedef, Mert ............................................................... WeBT3.4 2539<br />
.................................................................................. WeCPT10.7 *<br />
Seegmiller, Neal Andrew .......................................... MoBT6.4 607<br />
.................................................................................. MoBT6.5 613
.................................................................................. TuAPT10.13 *<br />
.................................................................................. TuAPT10.14 *<br />
Seipel, Justin ............................................................ MoAT5 C<br />
.................................................................................. MoAT5 O<br />
.................................................................................. MoAT5.5 203<br />
.................................................................................. MoBT5 O<br />
.................................................................................. MoBPT10.14 *<br />
Seki, Hiroya .............................................................. WeAT8.3 2314<br />
Sekiyama, Kosuke.................................................... ThCT2.1 4670<br />
Senda, Kei................................................................ WeAT7.5 2274<br />
.................................................................................. WeAT7.6 2280<br />
.................................................................................. WeBPT10.20 *<br />
.................................................................................. WeBPT10.21 *<br />
Sentis, Luis............................................................... TuAT7.4 1036<br />
.................................................................................. TuBPT10.16 *<br />
.................................................................................. WeAT7 C<br />
.................................................................................. WeAT7.4 2267<br />
.................................................................................. WeBPT10.19 *<br />
Sepp, Wolfgang........................................................ WeDT7.7 3626<br />
Seps, Monika............................................................ WeDT3.2 3454<br />
Seyfarth, Andre......................................................... TuCT7.1 1811<br />
Shah, Preyas............................................................ WeCT3.4 2992<br />
.................................................................................. WeDPT10.7 *<br />
Shah, Shridhar.......................................................... ThAT4.4 3911<br />
.................................................................................. ThBPT10.10 *<br />
Shahbazi, Hossein.................................................... TuBT2.1 1228<br />
Shakhimardanov, Azamat ........................................ TuAT7.3 1030<br />
Shamma, Jeff ........................................................... ThCT4.3 4770<br />
Shammas, Elie ......................................................... ThBT3 CC<br />
.................................................................................. ThBT3.7 4329<br />
Shams, Sarmad........................................................ ThDT6.3 5127<br />
Shang, Jianzhong..................................................... TuAT5.5 949<br />
.................................................................................. TuBPT10.11 *<br />
Shao, Xiaowei........................................................... WeAT2.3 2036<br />
Shaw, Kendrick......................................................... MoAT5.4 197<br />
.................................................................................. MoBPT10.13 *<br />
Sheh, Raymond Ka-Man .......................................... MoBT8.7 732<br />
Shell, Dylan .............................................................. ThCT9 C<br />
.................................................................................. ThCT9.4 5001<br />
.................................................................................. ThDPT10.25 *<br />
Shen, Jinglin............................................................. TuBT1.2 1180<br />
Shen, Lincheng......................................................... MoBT5.5 568<br />
.................................................................................. TuAPT10.11 *<br />
Shen, Yantao............................................................ WeAT4.4 2133<br />
.................................................................................. WeBPT10.10 *<br />
.................................................................................. ThBT9.2 4575<br />
Shende, Apoorva...................................................... WeCT8.1 3227<br />
Sheng, Weihua......................................................... WeCT1 C<br />
.................................................................................. WeCT1.2 2873<br />
.................................................................................. WeDT2.1 3395<br />
Sherbert, Robert....................................................... TuAT7.2 1022<br />
Sherikov, Aleksander................................................ WeAT7.8 2292<br />
Shi, Chaoyang.......................................................... WeAT3.7 2115<br />
Shi, Yun.................................................................... WeAT2.3 2036<br />
Shibasaki, Ryosuke.................................................. WeAT2.3 2036<br />
Shibata, Mizuho........................................................ ThCT3.7 4756<br />
Shida, Kazuya .......................................................... TuBT8.7 1499<br />
Shih, Albert J. ........................................................... TuAT5.7 961<br />
Shiller, Zvi................................................................. WeDT5.7 3539<br />
Shimojo, Makoto....................................................... TuCT9.7 1954<br />
.................................................................................. ThAT7.4 4048<br />
.................................................................................. ThCT3.4 4735<br />
.................................................................................. ThDPT10.7 *<br />
Shimosaka, Masamichi............................................. WeDT2.5 3419<br />
.................................................................................. WeDT7.3 3601<br />
.................................................................................. ThAPT10.5 *<br />
Shin, Dongik ............................................................. ThDT6.3 5127<br />
Shin, Dongjun........................................................... TuCT7.4 1830<br />
.................................................................................. WeAPT10.16 *<br />
.................................................................................. WeCT3.4 2992<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–255–<br />
.................................................................................. WeCT3.5 2998<br />
.................................................................................. WeDPT10.7 *<br />
.................................................................................. WeDPT10.8 *<br />
Shin, Kyoosik............................................................ ThDT6.3 5127<br />
Shin, Seung Hoon .................................................... TuBT7.7 1447<br />
Shintake, Jun............................................................ ThCT3.4 4735<br />
.................................................................................. ThDPT10.7 *<br />
Shiriaev, Anton ......................................................... ThDT5.2 5094<br />
Shkolnik, Alexander.................................................. ThBT3.4 4307<br />
.................................................................................. ThCPT10.7 *<br />
Shkurti, Florian ......................................................... ThDT3.3 5048<br />
.................................................................................. ThDT3.4 5054<br />
Shomin, Michael ....................................................... WeBT6.2 2668<br />
Shou, Kaiyu .............................................................. TuBT3.5 1297<br />
.................................................................................. TuCPT10.8 *<br />
Shyr, Alex ................................................................. TuAT1.2 793<br />
Siciliano, Bruno......................................................... TuCT6.4 1775<br />
.................................................................................. WeAPT10.13 *<br />
.................................................................................. ThAT1.2 3752<br />
Siegwart, Roland ...................................................... MoAT2.5 79<br />
.................................................................................. MoAT5.3 190<br />
.................................................................................. MoBT5.4 562<br />
.................................................................................. MoBPT10.5 *<br />
.................................................................................. TuAPT10.10 *<br />
.................................................................................. TuCT2.7 1661<br />
.................................................................................. WeAT6.5 2223<br />
.................................................................................. WeAT6.7 2235<br />
.................................................................................. WeAT6.8 2242<br />
.................................................................................. WeBT6.6 2694<br />
.................................................................................. WeBPT10.17 *<br />
.................................................................................. WeCPT10.18 *<br />
.................................................................................. ThAT2.4 3824<br />
.................................................................................. ThBPT10.4 *<br />
Sigrist, Roland .......................................................... WeCT4.7 3068<br />
Silveira, Geraldo ....................................................... WeCT9.8 3335<br />
Silver, David ............................................................. MoBT2.7 510<br />
Simaan, Nabil ........................................................... WeAT9.7 2390<br />
Simonin, Olivier ........................................................ ThAT8.6 4103<br />
.................................................................................. ThBPT10.24 *<br />
Simpson, Jason ........................................................ ThCT8.8 4975<br />
Singh, Sanjiv............................................................. MoAT6.3 227<br />
.................................................................................. WeAT6.3 2207<br />
.................................................................................. WeAT9.1 2352<br />
Singh, Surya ............................................................. MoBT8.6 725<br />
.................................................................................. TuAPT10.21 *<br />
Singleton, John......................................................... TuCT3.5 1687<br />
.................................................................................. WeAPT10.8 *<br />
Sinnet, Ryan W......................................................... TuCT5.3 1723<br />
Sirouspour, Shahin ................................................... MoBT7.2 645<br />
Sisbot, Emrah Akin ................................................... WeCT1.5 2895<br />
.................................................................................. WeDPT10.2 *<br />
Sitti, Metin................................................................. MoBT5.3 556<br />
.................................................................................. TuBT3.4 1291<br />
.................................................................................. TuCT3.1 *<br />
.................................................................................. TuCT3.3 1674<br />
.................................................................................. TuCT3.5 1687<br />
.................................................................................. TuCPT10.7 *<br />
.................................................................................. WeAPT10.8 *<br />
.................................................................................. ThAT4.8 3937<br />
Sivalingam, Ravishankar .......................................... TuAT9.8 1167<br />
Sjöö, Kristoffer .......................................................... TuBT9.1 1513<br />
Skachek, Sergey ...................................................... WeCT1.5 2895<br />
.................................................................................. WeDPT10.2 *<br />
Skantze, Gabriel ....................................................... WeDT1.1 3342<br />
Skocaj, Danijel.......................................................... WeDT1.8 3387<br />
Skourup, Charlotte.................................................... MoBPT10.16 *<br />
Smart, William .......................................................... ThAT8 CC<br />
.................................................................................. ThAT8.8 4115<br />
Smeraldi, Fabrizio..................................................... TuBT9.3 1526<br />
Smith, Ryan .............................................................. WeCT6 C
.................................................................................. WeCT6 O<br />
.................................................................................. WeDT6 O<br />
Smith, Stephen L...................................................... WeCT5.2 3087<br />
.................................................................................. WeDT8 CC<br />
.................................................................................. WeDT8.2 3645<br />
Smith, William........................................................... WeCT3.6 3004<br />
.................................................................................. WeDPT10.9 *<br />
Smits, Ruben............................................................ WeCT4.2 3031<br />
.................................................................................. ThCT2.3 4684<br />
Soccol, Dean ............................................................ ThCT8.2 4935<br />
Solanes, Ernesto ...................................................... ThBT3.8 4335<br />
Soltero, Daniel E....................................................... WeDT8.2 3645<br />
Son, Hyoung Il.......................................................... WeCT4.3 3039<br />
Sone, Satoshi ........................................................... TuCT9.7 1954<br />
Song, Dan................................................................. TuAT6.4 980<br />
.................................................................................. TuBPT10.13 *<br />
Song, Yixu ................................................................ WeAT9.2 2359<br />
Sornmo, Olof ............................................................ ThCT2.6 4704<br />
.................................................................................. ThDPT10.6 *<br />
Sotzek, Alice............................................................. WeDT1.6 3375<br />
.................................................................................. ThAPT10.3 *<br />
Soueres, Philippe ..................................................... ThAT9.2 4127<br />
Soulignac, Michaël ................................................... WeDT5.4 3519<br />
.................................................................................. ThAPT10.10 *<br />
Sousa, João.............................................................. WeCT6.6 3154<br />
.................................................................................. WeDPT10.18 *<br />
Sousa Junior, Samuel Felix de................................. WeAT1.2 1974<br />
Spalanzani, Anne ..................................................... WeAT1.8 2014<br />
Spampinato, Giacomo.............................................. TuCT2.2 1626<br />
Spenko, Matthew...................................................... MoAT7 CC<br />
.................................................................................. MoAT7.7 305<br />
Spinello, Luciano ...................................................... ThAT2.6 3838<br />
.................................................................................. ThAT2.7 3844<br />
.................................................................................. ThBPT10.6 *<br />
Srinivasa, Siddhartha ............................................... TuCT6.8 1804<br />
.................................................................................. WeAT1.4 1986<br />
.................................................................................. WeBPT10.1 *<br />
Srinivasan, Mandyam............................................... WePL.1 *<br />
.................................................................................. ThCT8.2 4935<br />
Stachniss, Cyrill........................................................ MoAT2.6 86<br />
.................................................................................. MoBPT10.6 *<br />
.................................................................................. TuAT2.5 865<br />
.................................................................................. TuBPT10.5 *<br />
.................................................................................. WeAT5.5 2180<br />
.................................................................................. WeBPT10.14 *<br />
.................................................................................. ThBT2 C<br />
.................................................................................. ThBT2.4 4249<br />
.................................................................................. ThCPT10.4 *<br />
Stanley, Kenneth ...................................................... WeBT8.7 2802<br />
Stasse, Olivier .......................................................... ThBT5.5 4408<br />
.................................................................................. ThCPT10.14 *<br />
Steck, Andreas ......................................................... WeAT2.7 2064<br />
Steder, Bastian......................................................... TuBT2.4 1249<br />
.................................................................................. TuCPT10.4 *<br />
Steffen, Jan .............................................................. WeCT2.5 2947<br />
.................................................................................. WeDPT10.5 *<br />
Stegagno, Paolo....................................................... MoBT2.1 469<br />
Stein, David .............................................................. ThCT4.8 4803<br />
Steinberg, Daniel...................................................... TuBT9.4 1533<br />
.................................................................................. TuCPT10.22 *<br />
.................................................................................. ThCT3.2 4722<br />
Steinwender, Jasmin ................................................ WeCT1.5 2895<br />
.................................................................................. WeDPT10.2 *<br />
Stelzer, Annett.......................................................... WeCPT10.6 *<br />
Stentz, Anthony ........................................................ MoBT2.7 510<br />
.................................................................................. WeBT8.4 2784<br />
.................................................................................. WeCPT10.22 *<br />
Sterkers, Olivier........................................................ WeBT3.3 2532<br />
Stiefelhagen, Rainer................................................. TuBT1.1 1173<br />
.................................................................................. ThCT5.4 4819<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–256–<br />
.................................................................................. ThDPT10.13 *<br />
Stilman, Mike ............................................................ WeBT5.6 2640<br />
.................................................................................. WeCPT10.15 *<br />
.................................................................................. ThCT1 C<br />
.................................................................................. ThCT1.2 4627<br />
Stilwell, Daniel .......................................................... TuAT9.4 1140<br />
.................................................................................. TuBPT10.22 *<br />
.................................................................................. WeCT8.1 3227<br />
Stirling, Leia.............................................................. ThBT7.3 4488<br />
Stirling, Timothy........................................................ ThCT9.8 5027<br />
Stoianovici, Dan........................................................ TuAT5.4 943<br />
.................................................................................. TuBPT10.10 *<br />
Stolle, Christian ........................................................ TuAT3.3 894<br />
Stone, Peter.............................................................. ThBT9.3 4581<br />
Stoy, Kasper ............................................................. WeDT8 C<br />
.................................................................................. WeDT8.4 3659<br />
.................................................................................. ThAPT10.19 *<br />
Stramigioli, Stefano .................................................. TuAT5.3 937<br />
.................................................................................. WeAT3 C<br />
.................................................................................. WeAT3.1 2076<br />
Strasser, Wolfgang ................................................... ThBT2.1 4229<br />
Strausser, Katherine................................................. ThCT7.4 4911<br />
.................................................................................. ThDPT10.19 *<br />
Strefling, Paul ........................................................... ThCT3.6 4749<br />
.................................................................................. ThDPT10.9 *<br />
Strom, Johannes H................................................... ThBT2.7 4271<br />
Studley, Matthew ...................................................... ThCT9.3 4995<br />
Stulp, Freek .............................................................. MoAT8.2 325<br />
.................................................................................. WeDT2.3 3407<br />
Stump, Ethan............................................................ MoBT2 CC<br />
.................................................................................. WeBT6.8 2708<br />
Stumpf, Jan Carsten................................................. ThDT8.1 5166<br />
Sturm, Peter ............................................................. ThAT6.6 4006<br />
.................................................................................. ThBPT10.18 *<br />
Su, Hu....................................................................... WeDT2.7 3434<br />
Sucan, Ioan Alexandru ............................................. ThCT1.1 4621<br />
Suetani, Hiromichi .................................................... WeBT7.1 2715<br />
Sugahara, Yusuke .................................................... ThBT9.5 4593<br />
.................................................................................. ThCPT10.26 *<br />
Sugano, Shigeki ....................................................... WeDT9.7 3734<br />
.................................................................................. ThAT1.8 3792<br />
.................................................................................. ThBT5 O<br />
.................................................................................. ThCT5 CC<br />
.................................................................................. ThCT5 O<br />
Sugi, Masao.............................................................. ThCT2.5 4698<br />
Sugimoto, Norikazu .................................................. WeCT7.3 3185<br />
Sugimoto, Shigeki..................................................... ThBT6.7 4463<br />
Sugimoto, Yasuhiro .................................................. TuCT7.2 1817<br />
Sugiura, Komei ......................................................... MoAT8.6 350<br />
.................................................................................. MoBPT10.24 *<br />
Suh, Il Hong.............................................................. WeDT7.2 3595<br />
Sujit, P.B................................................................... WeCT6.6 3154<br />
.................................................................................. WeDPT10.18 *<br />
Sukhatme, Gaurav.................................................... WeCT6.3 3132<br />
.................................................................................. WeCT6.5 3147<br />
.................................................................................. WeCT6.7 3160<br />
.................................................................................. WeDT6.5 3564<br />
.................................................................................. WeDPT10.17 *<br />
.................................................................................. ThAPT10.14 *<br />
.................................................................................. ThBT4.2 4348<br />
Sukkarieh, Salah ...................................................... ThBT2.5 4256<br />
.................................................................................. ThCPT10.5 *<br />
Sukthankar, Gita....................................................... WeBT4.4 2590<br />
.................................................................................. WeCPT10.10 *<br />
Sullivan, Brian........................................................... TuBT1.4 1194<br />
.................................................................................. TuCPT10.1 *<br />
Sullivan, Jenny ......................................................... TuCT5.1 1711<br />
Sumioka, Hidenobu .................................................. ThAT9.8 4168<br />
Sun, Baiqing ............................................................. ThCT2.1 4670<br />
Sun, Dali ................................................................... WeCT8.8 3276
Sun, Dong................................................................. MoBT1.6 451<br />
.................................................................................. TuAPT10.3 *<br />
Sun, Xiaochuan ........................................................ WeAT3.4 2096<br />
.................................................................................. WeBPT10.7 *<br />
Sun, Yi...................................................................... ThDT3.1 5035<br />
Sun, Yu..................................................................... ThAT6.1 *<br />
Sünderhauf, Niko...................................................... TuBT2.2 1234<br />
Suppa, Michael......................................................... TuCT9.4 1933<br />
.................................................................................. WeAPT10.22 *<br />
.................................................................................. WeCT9.3 3297<br />
Suzuki, Kouki............................................................ ThBT9.5 4593<br />
.................................................................................. ThCPT10.26 *<br />
Suzuki, Shota ........................................................... TuCT8.6 1895<br />
.................................................................................. WeAPT10.21 *<br />
Suzuki, Takahiro....................................................... TuBT1.7 1214<br />
Suzuki, Yosuke......................................................... TuCT9.7 1954<br />
Svec, Petr................................................................. TuAT9.6 1154<br />
.................................................................................. TuBPT10.24 *<br />
Svinin, Mikhail........................................................... ThBT4.8 4386<br />
Swaney, Philip.......................................................... WeBT3.1 2517<br />
Szczuka, Roman ...................................................... WeDT8.3 3653<br />
Szewczyk, Jérôme.................................................... TuBT5.4 1333<br />
.................................................................................. TuBT5.5 1339<br />
.................................................................................. TuCPT10.10 *<br />
.................................................................................. TuCPT10.11 *<br />
Sznitman, Raphael ................................................... WeCT2.6 2953<br />
.................................................................................. WeDPT10.6 *<br />
Szwaykowska, Klementyna...................................... WeDT6.8<br />
T<br />
3583<br />
Tabak, Ahmet Fatih .................................................. MoBT1.8 463<br />
Tachi, Susumu.......................................................... MoAT4.4 157<br />
.................................................................................. MoBPT10.10 *<br />
Tadakuma, Kenjiro ................................................... TuBT6.6 1386<br />
.................................................................................. TuBT6.7 1392<br />
.................................................................................. TuCPT10.15 *<br />
.................................................................................. ThAT7.4 4048<br />
.................................................................................. ThBPT10.19 *<br />
Tadakuma, Riichiro................................................... ThAT7.4 4048<br />
.................................................................................. ThBPT10.19 *<br />
Tadano, Kotaro......................................................... TuAT5.2 931<br />
Tadokoro, Satoshi .................................................... MoAT6 C<br />
.................................................................................. MoAT6 O<br />
.................................................................................. MoBT6 CC<br />
.................................................................................. MoBT6 O<br />
.................................................................................. WeDT3.7 3488<br />
.................................................................................. WeDT3.8 3494<br />
Tae, Kyung ............................................................... TuAT5.8 967<br />
Tagawa, Yasutaka.................................................... ThCT2.7 4710<br />
Taghirad, Hamid....................................................... TuAT9.2 1128<br />
Taguchi, Yuichi......................................................... ThCT2.4 4690<br />
.................................................................................. ThDPT10.4 *<br />
Tahara, Kenji ............................................................ TuCT7.6 1843<br />
.................................................................................. WeAPT10.18 *<br />
.................................................................................. ThBT1.5 4201<br />
.................................................................................. ThCPT10.2 *<br />
Taji, Kouichi.............................................................. WeBT7.4 2735<br />
.................................................................................. WeCPT10.19 *<br />
Takahashi, Jun ......................................................... WeCT7.2 3179<br />
Takahashi, Koji......................................................... ThBT6.4 4442<br />
.................................................................................. ThCPT10.16 *<br />
Takaki, Takeshi ........................................................ TuBT1.6 1208<br />
.................................................................................. TuCPT10.3 *<br />
Takanishi, Atsuo....................................................... WeCT7.8 3221<br />
Takano, Wataru........................................................ WeDT2.2 3401<br />
Takaoka, Shunichi.................................................... TuAT8.3 1081<br />
Takayama, Leila ....................................................... WeBT1.8 2452<br />
Takemura, Noriko..................................................... WeCT1.1 2867<br />
Takeshita, Keisuke ................................................... MoAT4.4 157<br />
.................................................................................. MoBPT10.10 *<br />
Takeuchi, Masaru..................................................... MoBT1.7 457<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–257–<br />
Takhmar, Amir .......................................................... MoBT7.3 653<br />
Takiguchi, Kingo ....................................................... MoAT1.1 1<br />
Takubo, Tomohito..................................................... MoAT1.5 25<br />
.................................................................................. MoBT1.2 427<br />
.................................................................................. MoBPT10.2 *<br />
.................................................................................. TuAT1.4 807<br />
.................................................................................. TuBPT10.1 *<br />
Takubo, Toshio......................................................... WeBT1.4 2423<br />
.................................................................................. WeCPT10.1 *<br />
Takumi, Yoshida....................................................... MoAT4.4 157<br />
.................................................................................. MoBPT10.10 *<br />
Tamjidi, Amir Hossein............................................... TuAT9.2 1128<br />
Tamura, Yusuke ....................................................... WeCT7.6 3207<br />
.................................................................................. WeDPT10.21 *<br />
Tan, Jindong............................................................. TuCT2.8 1668<br />
Tan, Min.................................................................... WeDT2.7 3434<br />
Tan, Sia Nguan Eugene ........................................... ThCT7.3 4905<br />
Tan, Xiaobo .............................................................. MoBT5.8 588<br />
Tanaka, Daiki............................................................ WeBT7.6 2747<br />
.................................................................................. WeCPT10.21 *<br />
Tanaka, Fumihide..................................................... WeBT1.2 2409<br />
Tanaka, Kanji............................................................ TuAT2.6 872<br />
.................................................................................. TuBPT10.6 *<br />
Tanaka, Yoshiyuki .................................................... TuCT5.2 1717<br />
Tang, Chaoquan....................................................... TuCT8.2 1869<br />
Tangorra, James ...................................................... MoBT5.7 580<br />
Tani, Atsushi............................................................. WeBT1.4 2423<br />
.................................................................................. WeCPT10.1 *<br />
Tanikawa, Tamio ...................................................... MoAT1.5 25<br />
.................................................................................. MoBPT10.2 *<br />
Tanner, Herbert G. ................................................... ThAT4.4 3911<br />
.................................................................................. ThBPT10.10 *<br />
Tao, Xinyong ............................................................ TuCT3.8 1705<br />
Tardos, Juan D. ........................................................ MoAT2 C<br />
.................................................................................. MoAT2.1 51<br />
.................................................................................. TuBT2.8 1277<br />
Täubig, Holger .......................................................... TuCT1.4 1585<br />
.................................................................................. WeAPT10.1 *<br />
Tavakoli, Mahdi ........................................................ MoBT7.4 659<br />
.................................................................................. MoBT9.1 738<br />
.................................................................................. TuAPT10.16 *<br />
Tavakoli, Mahmoud .................................................. ThBT2.8 4277<br />
Tayama, Munenori.................................................... ThCT7.2 4899<br />
Taylor, Brian ............................................................. MoAT5.7 215<br />
Taylor, Camillo Jose ................................................. TuAT7.6 1048<br />
.................................................................................. TuBPT10.18 *<br />
.................................................................................. ThCT4.4 4776<br />
.................................................................................. ThDPT10.10 *<br />
Taylor, Russell H. ..................................................... MoBT7.1 639<br />
.................................................................................. WeCT2.6 2953<br />
.................................................................................. WeDPT10.6 *<br />
Tedrake, Russ .......................................................... WeBT6.7 2700<br />
Tegopoulou, Anastasia............................................. WeCT2.1 2922<br />
Tehrani Nik Nejad, Hossein...................................... ThBT6.4 4442<br />
.................................................................................. ThCPT10.16 *<br />
Teller, Seth ............................................................... ThBT3.4 4307<br />
.................................................................................. ThCPT10.7 *<br />
Temel, Fatma Zeynep .............................................. MoBT1.8 463<br />
Tenenholtz, Neil........................................................ TuBT5.3 1327<br />
Tenorth, Moritz ......................................................... ThBT2.6 4263<br />
.................................................................................. ThCPT10.6 *<br />
ter Mors, Adriaan W.................................................. WeAT5.3 2166<br />
Terada, Kazuki ......................................................... TuCT9.7 1954<br />
.................................................................................. ThAT7.4 4048<br />
.................................................................................. ThBPT10.19 *<br />
Tercero Villagran, Carlos Rafael .............................. MoBT1.4 439<br />
.................................................................................. TuAPT10.1 *<br />
.................................................................................. WeAT3.7 2115<br />
Terekhov, Alexander V. ............................................ ThBT4.4 4360<br />
.................................................................................. ThCPT10.10 *
Tesch, Matthew ........................................................ MoAT8.7 357<br />
.................................................................................. TuAT8.1 1069<br />
Teuliere, Celine ........................................................ ThCT8.1 4929<br />
Tews, Ashley Desmond............................................ TuAT1 CC<br />
.................................................................................. TuAT1.8 834<br />
Thakur, Atul .............................................................. TuAT9.6 1154<br />
.................................................................................. TuBPT10.24 *<br />
Theodorou, Evangelos ............................................. MoAT8.2 325<br />
.................................................................................. WeDT2.3 3407<br />
Thielmann, Sophie Charlotte Franziska ................... WeCT4.1 3023<br />
Thobbi, Anand .......................................................... WeCT1.2 2873<br />
Thompson, David ..................................................... WeCT6.4 3140<br />
.................................................................................. WeDPT10.16 *<br />
Thompson, Paul ....................................................... ThBT2.2 4236<br />
Thorson, Ivar ............................................................ MoAT9.4 390<br />
.................................................................................. MoBPT10.25 *<br />
Thrun, Sebastian ...................................................... ThPT11.1 *<br />
Thuilot, Benoit........................................................... ThAT8.1 4072<br />
.................................................................................. ThBT9.1 4569<br />
Thurrowgood, Saul ................................................... ThCT8.2 4935<br />
Tian, Weicheng......................................................... TuCT8.1 1863<br />
Tipaldi, Gian Diego................................................... WeAT1.1 1968<br />
Tobergte, Andreas.................................................... WeCT4.1 3023<br />
Tokekar, Pratap........................................................ MoBT2.4 488<br />
.................................................................................. TuAPT10.4 *<br />
Tokuda, Isao............................................................. WeBT7.6 2747<br />
.................................................................................. WeCPT10.21 *<br />
Tolley, Michael Thomas............................................ ThBT4.5 4366<br />
.................................................................................. ThCPT10.11 *<br />
Tombari, Federico .................................................... ThCT6.4 4857<br />
.................................................................................. ThDPT10.16 *<br />
Tominaga, Shoji........................................................ WeDT2.5 3419<br />
.................................................................................. ThAPT10.5 *<br />
Tomita, Kyohei.......................................................... MoAT1.3 13<br />
Tomiyama, Ken ........................................................ WeBT2.2 2466<br />
Tomizuka, Masayoshi............................................... TuCT8.5 1887<br />
.................................................................................. WeAPT10.20 *<br />
.................................................................................. WeBT2.3 2474<br />
Tomlin, Claire ........................................................... WeCT3.2 2979<br />
Tong, Chi Hay........................................................... MoBT6.8 631<br />
Torfs, Serge.............................................................. ThBT7.1 4477<br />
Tornero, Josep ......................................................... ThBT3.8 4335<br />
Torres, Luis G........................................................... ThDT7.3 5153<br />
Tow, Adela................................................................ ThCT7.6 4923<br />
.................................................................................. ThDPT10.21 *<br />
Toyama, Shigeki....................................................... MoBT9.3 750<br />
Transeth, Aksel Andreas .......................................... MoAT6.6 247<br />
.................................................................................. MoBPT10.18 *<br />
Tremblay, Charles .................................................... TuBT3.3 1285<br />
Trentini, Michael ....................................................... TuAT2.3 853<br />
.................................................................................. TuAT2.7 880<br />
Trevor, Alexander J B............................................... TuBT2.6 1264<br />
.................................................................................. TuCPT10.6 *<br />
Trianni, Vito .............................................................. ThCT9.8 5027<br />
Trinkle, Jeff............................................................... TuBT7 CC<br />
.................................................................................. TuBT7.5 1433<br />
.................................................................................. TuCT6 C<br />
.................................................................................. TuCPT10.17 *<br />
Troiani, Chiara.......................................................... WeBT2.1 2460<br />
Troni, Giancarlo ........................................................ WeDT9.5 3722<br />
.................................................................................. ThAPT10.23 *<br />
Tsagarakis, Nikolaos ................................................ MoAT8.1 318<br />
.................................................................................. MoAT9.2 378<br />
.................................................................................. ThAT7.1 4026<br />
Tsakiris, Dimitris ....................................................... ThAT7.5 4054<br />
.................................................................................. ThBPT10.20 *<br />
Tsiotras, Panagiotis.................................................. WeDT5.1 3501<br />
.................................................................................. WeDT5.2 3507<br />
.................................................................................. WeDT5.6 3533<br />
.................................................................................. ThAPT10.12 *<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–258–<br />
Tsuchiya, Kazuo ....................................................... WeAT7.5 2274<br />
.................................................................................. WeAT7.6 2280<br />
.................................................................................. WeBPT10.20 *<br />
.................................................................................. WeBPT10.21 *<br />
Tsugawa, Sadayuki .................................................. ThAT8.7 4109<br />
Tsuji, Tokuo .............................................................. ThBT5.3 4392<br />
Tsuji, Toshio ............................................................. TuCT5.2 1717<br />
Tsujino, Hiroshi......................................................... ThAT1.8 3792<br />
Tsukahara, Atsushi................................................... TuCT5.5 1737<br />
.................................................................................. WeAPT10.11 *<br />
Tully, Stephen........................................................... MoBT2.3 482<br />
.................................................................................. TuBT5.7 1353<br />
.................................................................................. ThDT7.2 5147<br />
Tunnell, Robert ......................................................... TuAT3.3 894<br />
Turkseven, Melih ...................................................... WeAT4.5 2139<br />
.................................................................................. WeBPT10.11<br />
U<br />
*<br />
Ueda, Jun ................................................................. TuCT9.5 1940<br />
.................................................................................. WeAT4.5 2139<br />
.................................................................................. WeAPT10.23 *<br />
.................................................................................. WeBPT10.11 *<br />
Ueda, Tomohiro........................................................ ThCT3.7 4756<br />
Ueyama, Tsuyoshi.................................................... ThCT2.5 4698<br />
.................................................................................. ThDPT10.5 *<br />
Ugurlu, Barkan.......................................................... MoAT8.1 318<br />
Ulbrich, Stefan .......................................................... TuCT6.2 1761<br />
Ulmen, John ............................................................. ThDT5.3 5100<br />
Ulusoy, Alphan ......................................................... WeCT5.2 3087<br />
Underwood, James Patrick....................................... MoAT6.7 255<br />
Unluhisarcikli, Ozer................................................... ThCT7.1 4893<br />
Uno, Yoji ................................................................... WeBT7.4 2735<br />
.................................................................................. WeCPT10.19 *<br />
Urcola, Pablo ............................................................ WeCT1.4 2887<br />
.................................................................................. WeDPT10.1<br />
V<br />
*<br />
Vahrenkamp, Nikolaus ............................................. TuCT6.2 1761<br />
Vaidyanathan, Ravi .................................................. MoAT5 O<br />
.................................................................................. MoBT5 CC<br />
.................................................................................. MoBT5 O<br />
.................................................................................. ThAT7.3 4042<br />
Vallery, Heike ........................................................... WeCT4.7 3068<br />
Vallone, Luca............................................................ WeCT7.4 3192<br />
.................................................................................. WeDPT10.19 *<br />
Valls Miro, Jaime ...................................................... TuAT2 C<br />
.................................................................................. TuCT2.4 1640<br />
.................................................................................. WeAPT10.4 *<br />
Valtazanos, Aris........................................................ WeDT8.7 3679<br />
van den Berg, Jur ..................................................... WeBT5.7 2646<br />
van den Boom, Ton .................................................. WeBT7.3 2729<br />
Van der Loos, H.F. Machiel ...................................... WeAT1.5 1994<br />
.................................................................................. WeBPT10.2 *<br />
van der Smagt, Patrick ............................................. MoBT7 CC<br />
.................................................................................. MoBT7.6 672<br />
.................................................................................. TuAPT10.18 *<br />
van Rossum, Anne ................................................... ThCT4.5 4783<br />
.................................................................................. ThDPT10.11 *<br />
van Toll, Wouter ....................................................... WeDT5.5 3526<br />
.................................................................................. ThAPT10.11 *<br />
van Veen, Youri ........................................................ WeBT3.7 2557<br />
Vander Hook, Joshua ............................................... MoBT2.4 488<br />
.................................................................................. TuAPT10.4 *<br />
Varga, Maja .............................................................. ThCT9.6 5015<br />
.................................................................................. ThDPT10.27 *<br />
Vartholomeos, Panagiotis......................................... ThBT7.6 4508<br />
.................................................................................. ThCPT10.21 *<br />
Vasilyev, Nikolay ...................................................... TuBT5.3 1327<br />
.................................................................................. WeAT3.2 2083<br />
Vassallo, Raquel Frizera .......................................... WeDT7.6 3620<br />
.................................................................................. ThAPT10.18 *<br />
Vasseur, Pascal........................................................ ThAT6.6 4006
.................................................................................. ThBPT10.18 *<br />
Vaughan, Richard..................................................... WeBT8 C<br />
Vaz, Luís................................................................... WeBT1.6 2438<br />
.................................................................................. WeCPT10.3 *<br />
Veloso, Manuela....................................................... MoAT2.4 73<br />
.................................................................................. MoBPT10.4 *<br />
.................................................................................. WeAT8.5 2327<br />
.................................................................................. WeBPT10.23 *<br />
.................................................................................. WeDT8.1 3638<br />
Venture, Gentiane .................................................... WeDT9.2 3701<br />
.................................................................................. ThCT2.7 4710<br />
Veon, Kevin .............................................................. WeAT9.5 2377<br />
.................................................................................. WeBPT10.26 *<br />
Vercher, Jean-Louis ................................................. TuBT5.4 1333<br />
.................................................................................. TuCPT10.10 *<br />
Verl, Alexander......................................................... WeAT9.3 2365<br />
Vernaza, Paul........................................................... WeAT5.6 2186<br />
.................................................................................. WeBPT10.15 *<br />
Verschure, Paul........................................................ TuAT8.8 1115<br />
Verspecht, Jonathan................................................. ThBT7.1 4477<br />
Vial, John.................................................................. TuAT2.8 886<br />
Vicentini, Federico.................................................... WeCT9.7 3327<br />
Vidal-Calleja, Teresa A............................................. MoAT2.7 92<br />
.................................................................................. WeBT2.5 2489<br />
.................................................................................. WeCPT10.5 *<br />
Vijayakumar, Sethu .................................................. MoBT8.5 718<br />
.................................................................................. TuAPT10.20 *<br />
Villani, Luigi .............................................................. ThAT1.2 3752<br />
.................................................................................. ThBT1 CC<br />
.................................................................................. ThBT1.4 4194<br />
.................................................................................. ThCPT10.1 *<br />
Vincze, Markus......................................................... TuAT1.5 813<br />
.................................................................................. TuBT1.5 1201<br />
.................................................................................. TuBPT10.2 *<br />
.................................................................................. TuCPT10.2 *<br />
.................................................................................. ThCT6.5 4865<br />
.................................................................................. ThDPT10.17 *<br />
Vitiello, Valentina...................................................... TuAT5.5 949<br />
.................................................................................. TuBPT10.11 *<br />
Vogel, Christian ........................................................ WeDT1.3 3355<br />
Vogel, Joern ............................................................. MoBT7.6 672<br />
.................................................................................. TuAPT10.18 *<br />
Voigt, Rainer............................................................. WeBT6.6 2694<br />
.................................................................................. WeCPT10.18 *<br />
Volkhardt, Michael.................................................... WeBT1.5 2430<br />
Volpe, Richard.......................................................... MoBT6.1 *<br />
von Stryk, Oskar....................................................... ThCT5.3 4811<br />
Vona, Marsette ......................................................... TuBT7 C<br />
.................................................................................. TuBT7.6 1439<br />
.................................................................................. TuCPT10.18 *<br />
Vonthron, Manuel ..................................................... TuAT3.4 901<br />
.................................................................................. TuBT3.3 1285<br />
.................................................................................. TuBPT10.7 *<br />
Vorst, Philipp ............................................................ ThBT2.1 4229<br />
Voyles, Richard ........................................................ WeAT9.5 2377<br />
.................................................................................. WeBPT10.26 *<br />
.................................................................................. WeCT3 C<br />
Vrecko, Alen ............................................................. WeDT1.8<br />
W<br />
3387<br />
Wade, Eric................................................................ WeBT1 CC<br />
.................................................................................. WeBT1.1 2403<br />
Wagner, Glenn ......................................................... WeCT8.6 3260<br />
.................................................................................. WeDPT10.24 *<br />
Wagner, René .......................................................... WeCT9.4 3305<br />
.................................................................................. WeDPT10.25 *<br />
Wahl, Friedrich M. .................................................... WeBT2.4 2481<br />
.................................................................................. WeCPT10.4 *<br />
Walker, Ian ............................................................... TuAT8.4 1087<br />
.................................................................................. TuBPT10.19 *<br />
.................................................................................. ThAT7.6 4060<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–259–<br />
.................................................................................. ThBPT10.21 *<br />
.................................................................................. ThCT6.6 4871<br />
.................................................................................. ThDPT10.18 *<br />
Walter, Christoph...................................................... WeDT1.3 3355<br />
Walter, Matthew........................................................ ThBT3.4 4307<br />
.................................................................................. ThCPT10.7 *<br />
Wang, Dangxiao ....................................................... WeBT4.6 2602<br />
.................................................................................. WeCPT10.12 *<br />
Wang, Fei ................................................................. MoAT4.8 184<br />
Wang, Hesheng........................................................ WeCT2.7 2959<br />
Wang, Hongbo.......................................................... WeAT1.7 2008<br />
Wang, Jianxun.......................................................... MoBT5.8 588<br />
Wang, Keyi ............................................................... WeDT2.8 3440<br />
Wang, Long .............................................................. TuBT6.5 1380<br />
.................................................................................. TuCPT10.14 *<br />
Wang, Ping ............................................................... TuCT5.6 1743<br />
.................................................................................. WeAPT10.12 *<br />
Wang, Ping Chuan ................................................... ThCT6.7 4877<br />
Wang, Shuhui ........................................................... ThDT4.1 5061<br />
Wang, Tianmiao ....................................................... TuCT8.1 1863<br />
Wang, Weifu ............................................................. ThBT3.6 4321<br />
.................................................................................. ThCPT10.9 *<br />
Wang, Yao-Dong ...................................................... TuBT1.6 1208<br />
.................................................................................. TuCPT10.3 *<br />
Wang, Yuechao ........................................................ TuCT8.2 1869<br />
Wang, Yunxia ........................................................... MoAT4.8 184<br />
Wang, Zhikun ........................................................... MoAT8.3 332<br />
Wang, Zhongli .......................................................... WeCT2.7 2959<br />
Wang, Zhuowei......................................................... WeDT8.3 3653<br />
Warneken, Felix........................................................ WeCT1.5 2895<br />
.................................................................................. WeDPT10.2 *<br />
Warnier, Matthieu ..................................................... WeCT1.5 2895<br />
.................................................................................. WeDPT10.2 *<br />
Warren, Frank........................................................... TuBT5.2 1321<br />
Waslander, Steven Lake .......................................... ThCT8.6 4961<br />
.................................................................................. ThDPT10.24 *<br />
Watanabe, Hiroki ...................................................... TuAT5.6 955<br />
.................................................................................. TuBPT10.12 *<br />
Watanabe, Kouichi ................................................... MoAT4.4 157<br />
.................................................................................. MoBPT10.10 *<br />
Watanabe, Tetsuyou ................................................ TuBT6.8 1398<br />
Watanabe, Wataru.................................................... TuCT8.6 1895<br />
.................................................................................. WeAPT10.21 *<br />
Weaver, Kyle D......................................................... WeBT3.1 2517<br />
Webster III, Robert James........................................ WeBT3.1 2517<br />
.................................................................................. ThAT1 C<br />
.................................................................................. ThAT1.4 3764<br />
.................................................................................. ThBPT10.1 *<br />
Wedge, Nathan......................................................... WeBT5.3 2620<br />
Wei, Gu-Yeon ........................................................... TuAT8.6 1099<br />
.................................................................................. TuBPT10.21 *<br />
Wei, Hung-Yuan ....................................................... TuCT9.8 1962<br />
Weinberg, Brian........................................................ ThCT7.1 4893<br />
Weiss, Stephan ........................................................ TuCT2.7 1661<br />
.................................................................................. WeAT6.7 2235<br />
.................................................................................. WeAT6.8 2242<br />
.................................................................................. WeBT6.6 2694<br />
.................................................................................. WeCPT10.18 *<br />
Weisshardt, Florian................................................... WeAT9.3 2365<br />
Weisz, Jonathan ....................................................... TuBT6.5 1380<br />
.................................................................................. TuCPT10.14 *<br />
Welihena Gamage, Kumudu Chalaka ThCT7.3 4905<br />
Gamage....................................................................<br />
Wenger, Philippe ...................................................... TuBT7.8 1453<br />
Wettergreen, David................................................... MoBT6.4 607<br />
.................................................................................. MoBT6.5 613<br />
.................................................................................. TuAPT10.13 *<br />
.................................................................................. TuAPT10.14 *<br />
Weyers, Christopher................................................. TuAT2.4 859<br />
.................................................................................. TuBPT10.4 *
Whitcomb, Louis....................................................... WeDT9.5 3722<br />
.................................................................................. ThAPT10.23 *<br />
White, Paul ............................................................... MoAT9.7 408<br />
Whittaker, Chuck ...................................................... ThAT2.3 3816<br />
Whittaker, William..................................................... ThAT2.3 3816<br />
Wieber, Pierre-Brice ................................................. WeAT7.8 2292<br />
.................................................................................. ThAT3.6 3887<br />
.................................................................................. ThBPT10.9 *<br />
Williams, David......................................................... ThCT3.5 4741<br />
.................................................................................. ThDPT10.8 *<br />
Williams, Ryan.......................................................... ThBT4.2 4348<br />
Williams, Stefan Bernard.......................................... TuBT2.3 1242<br />
.................................................................................. TuBT9.4 1533<br />
.................................................................................. TuCPT10.22 *<br />
.................................................................................. ThBT6.6 4455<br />
.................................................................................. ThCT3.2 4722<br />
.................................................................................. ThCPT10.18 *<br />
Willimon, Bryan......................................................... ThCT6.6 4871<br />
.................................................................................. ThDPT10.18 *<br />
Wimboeck, Thomas.................................................. TuAT6.3 973<br />
.................................................................................. WeCT7.5 3199<br />
.................................................................................. WeDPT10.20 *<br />
.................................................................................. ThBT1.7 4215<br />
Windau, Jens............................................................ WeAT9.8 2397<br />
Winfield, Alan............................................................ ThCT9.3 4995<br />
Winkens, Christian.................................................... WeDT5.8 3545<br />
Wisse, Martijn........................................................... TuAT6.6 995<br />
.................................................................................. TuBPT10.15 *<br />
.................................................................................. ThAT5.5 3957<br />
.................................................................................. ThBPT10.14 *<br />
Witick, Martha........................................................... ThAT8.8 4115<br />
Wittmeier, Steffen..................................................... TuAT7.8 1063<br />
.................................................................................. ThAT9.5 4148<br />
.................................................................................. ThBPT10.26 *<br />
Wohlkinger, Walter ................................................... ThCT6.5 4865<br />
.................................................................................. ThDPT10.17 *<br />
Wolf, Denis Fernando............................................... TuAT2 CC<br />
Wolf, Michael............................................................ ThCT3.3 4728<br />
Wolf, Peter................................................................ WeCT4.7 3068<br />
Wollherr, Dirk............................................................ WeCT5.6 3114<br />
.................................................................................. WeDPT10.15 *<br />
Wong, Liang Jie........................................................ WeDT6.3 3551<br />
Wong, Uland............................................................. ThAT2.3 3816<br />
Wood, John .............................................................. WeAT8.6 2333<br />
Wood, Levi................................................................ ThAT4.7 3931<br />
Wood, Nathan........................................................... ThBT7.8 4522<br />
Wood, Robert ........................................................... MoAT1.6 31<br />
.................................................................................. MoAT9.3 384<br />
.................................................................................. MoAT9.8 414<br />
.................................................................................. MoBT5.2 *<br />
.................................................................................. MoBPT10.3 *<br />
.................................................................................. TuAT8 C<br />
.................................................................................. TuAT8.6 1099<br />
.................................................................................. TuAT8.7 1107<br />
.................................................................................. TuBT8.4 1479<br />
.................................................................................. TuBPT10.21 *<br />
.................................................................................. TuCT9.2 1919<br />
.................................................................................. TuCPT10.19 *<br />
.................................................................................. ThBT7.3 4488<br />
.................................................................................. ThCT9.2 4989<br />
.................................................................................. ThDT4.3 5073<br />
Woodward, Matthew................................................. MoBT5.3 556<br />
Worcester, James..................................................... ThCT4.6 4790<br />
.................................................................................. ThDPT10.12 *<br />
Wortmann, Tim......................................................... TuBT3.5 1297<br />
.................................................................................. TuCPT10.8 *<br />
Wrede, Britta............................................................. TuBT1.3 1187<br />
Wrede, Sebastian..................................................... WeCT3.7 3011<br />
Wu, Licheng.............................................................. ThDT4.1 5061<br />
Wu, Wenqiang.......................................................... TuBT8.3 1473<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–260–<br />
Wu, Yen-Chang ........................................................ WeCT2.8 2965<br />
Wuensche, Hans J ................................................... ThBT8.6 4562<br />
.................................................................................. ThCPT10.24 *<br />
Wurm, Kai M............................................................. ThBT2.4 4249<br />
.................................................................................. ThCPT10.4 *<br />
Wüsthoff, Tilo............................................................ TuCT9.4 1933<br />
.................................................................................. WeAPT10.22 *<br />
.................................................................................. WeDT1.5 3367<br />
.................................................................................. ThAPT10.2 *<br />
Wyeth, Gordon ......................................................... MoBT8.1<br />
X<br />
692<br />
Xi, Ning ..................................................................... MoAT4.6 171<br />
.................................................................................. MoAT4.8 184<br />
.................................................................................. MoBPT10.12 *<br />
.................................................................................. WeAT4.4 2133<br />
.................................................................................. WeBPT10.10 *<br />
Xia, Tian ................................................................... MoBT7.1 639<br />
Xiang, Xianbo ........................................................... WeDT6.4 3558<br />
.................................................................................. ThAPT10.13 *<br />
Xiao, Jing.................................................................. WeBT4.6 2602<br />
.................................................................................. WeCPT10.12 *<br />
.................................................................................. ThBT1 C<br />
.................................................................................. ThBT1.6 4207<br />
.................................................................................. ThCPT10.3 *<br />
Xiao, Junhao............................................................. TuAT1.3 801<br />
Xie, Dan.................................................................... WeDT1.2 3349<br />
Xin, Ming................................................................... MoAT7.5 292<br />
.................................................................................. MoBPT10.20 *<br />
Xu, Anqi .................................................................... ThDT3.3 5048<br />
Xu, De....................................................................... WeDT2.7 3434<br />
Xu, Kai ...................................................................... TuAT5.7 961<br />
Xu, Ling .................................................................... WeBT8.4 2784<br />
.................................................................................. WeCPT10.22 *<br />
Xu, Yangsheng ......................................................... TuBT8.5 1487<br />
.................................................................................. TuCPT10.20<br />
Y<br />
*<br />
Yamada, Hiroya........................................................ TuAT8.3 1081<br />
Yamada, Yasunori .................................................... TuBT8.7 1499<br />
Yamada, Yoji ............................................................ WeCT4.6 3060<br />
.................................................................................. WeDPT10.12 *<br />
Yamamoto, Akio ....................................................... ThCT1.8 4664<br />
Yamamoto, Kiyohito ................................................. WeAT3.7 2115<br />
Yamamoto, Ko.......................................................... ThCT9.7 5021<br />
Yamamoto, Motoji..................................................... TuCT7.6 1843<br />
.................................................................................. WeAPT10.18 *<br />
.................................................................................. ThBT4.8 4386<br />
.................................................................................. ThDT5 CC<br />
.................................................................................. ThDT6.4 5133<br />
Yamanishi, Yoko....................................................... TuBT3.7 1309<br />
Yamashita, Tsuyoshi ................................................ WeAT7.5 2274<br />
.................................................................................. WeAT7.6 2280<br />
.................................................................................. WeBPT10.20 *<br />
.................................................................................. WeBPT10.21 *<br />
Yamato, Hideaki ....................................................... WeBT2.2 2466<br />
Yan, Liang ................................................................ MoBT9.2 744<br />
Yang, Chenguang..................................................... ThAT9.1 4121<br />
Yang, Guang-Zhong ................................................. MoBT3.7 542<br />
.................................................................................. TuAT5.5 949<br />
.................................................................................. TuBPT10.11 *<br />
Yang, Guosheng....................................................... ThDT4.1 5061<br />
Yang, Hai.................................................................. ThDT4.2 5067<br />
Yang, Jianyu............................................................. WeDT2.8 3440<br />
Yang, Xuesong ......................................................... WeCT1.3 2879<br />
Yatsurugi, Manabu ................................................... MoAT9.5 395<br />
.................................................................................. MoBPT10.26 *<br />
Yazbeck, Jano .......................................................... ThAT8.6 4103<br />
.................................................................................. ThBPT10.24 *<br />
Yazdanpanah, M. J................................................... WeBT7.2 2723<br />
Ye, Zhou ................................................................... TuBT3.4 1291<br />
.................................................................................. TuCPT10.7 *
Yeh, Xiyang .............................................................. TuCT7.4 1830<br />
.................................................................................. WeAPT10.16 *<br />
Yershov, Dmitry........................................................ ThAT3.2 3862<br />
Yesilyurt, Serhat ....................................................... MoBT1.8 463<br />
Yi, Byung-Ju ............................................................. TuAT5.8 967<br />
.................................................................................. WeDT7.2 3595<br />
Yi, Seung Joon ......................................................... ThAT5.6 3963<br />
.................................................................................. ThBPT10.15 *<br />
Yim, Mark ................................................................. MoAT9.7 408<br />
.................................................................................. ThCT4 CC<br />
.................................................................................. ThCT4 O<br />
.................................................................................. ThCT4.1 *<br />
.................................................................................. ThCT4.7 4797<br />
Yip, Hiu Man............................................................. ThBT1.8 4222<br />
Yoerger, Dana .......................................................... MoAT6.8 261<br />
.................................................................................. ThCT3.2 4722<br />
Yokoi, Kazuhito......................................................... WeAT1.6 2000<br />
.................................................................................. WeBPT10.3 *<br />
.................................................................................. ThBT5 CC<br />
.................................................................................. ThBT5.3 4392<br />
.................................................................................. ThBT5.8 4428<br />
Yokokohji, Yasuyoshi ............................................... WeAT4 O<br />
.................................................................................. WeBT4 C<br />
.................................................................................. WeBT4 O<br />
.................................................................................. WeCT4 CC<br />
.................................................................................. WeCT4 O<br />
Yonezawa, Naoaki.................................................... ThBT9.5 4593<br />
.................................................................................. ThCPT10.26 *<br />
Yonezawa, Satoshi................................................... WeAT4.3 2127<br />
Yoo, Jae Hyun.......................................................... WeBT8.5 2790<br />
.................................................................................. WeCPT10.23 *<br />
Yoon, Han................................................................. WeAT2.8 2070<br />
Yoon, Hyun-Soo ....................................................... TuAT5.8 967<br />
Yoshida, Eiichi.......................................................... WeCT7 C<br />
.................................................................................. ThBT5.5 4408<br />
.................................................................................. ThCPT10.14 *<br />
Yoshida, Kazuya....................................................... MoBT6.3 601<br />
.................................................................................. MoBT6.7 625<br />
Yoshida, Morio.......................................................... WeBT1.7 2445<br />
Yoshida, Takami....................................................... MoBT3.4 524<br />
.................................................................................. TuAPT10.7 *<br />
Yoshida, Tomoaki..................................................... MoAT6.1 *<br />
Yoshida, Yuki............................................................ WeCT7.2 3179<br />
Yoshimoto, Kayo ...................................................... TuBT6.6 1386<br />
.................................................................................. TuCPT10.15 *<br />
Young, Diana............................................................ ThBT7.3 4488<br />
Yozbatiran, Nuray..................................................... TuCT5.1 1711<br />
Yu, Shengwei ........................................................... WeAT8.1 2300<br />
Yu, Wei-Shun ........................................................... TuBT8.6 1493<br />
.................................................................................. TuCPT10.21 *<br />
Yuan, Haiwen ........................................................... ThDT4.1 5061<br />
Yuan, Qilong............................................................. WeCT2.3 2935<br />
Yuan, Shuai.............................................................. WeDT8.8 3686<br />
Yucel, Zeynep........................................................... WeDT7.1 3589<br />
Yue, Tao................................................................... MoBT1.3 433<br />
Yuh, Junku................................................................ WeDT6.1 *<br />
Yuma, Matsumura .................................................... MoAT7.1 268<br />
Yun, Seung-kook...................................................... ThAT5 CC<br />
.................................................................................. ThAT5 O<br />
.................................................................................. ThAT5.3 3943<br />
Yuta, Shinichi............................................................ WeDT7.2<br />
Z<br />
3595<br />
Zach, Christopher..................................................... TuCT2.1 1618<br />
Zacharias, Franziska ................................................ TuCT6.3 1768<br />
Zani, Paolo ............................................................... TuCT1 C<br />
.................................................................................. TuCT1.6 1599<br />
.................................................................................. WeAPT10.3 *<br />
Zarzhitsky, Dimitri..................................................... ThCT3.3 4728<br />
Zeeshan, Arif Muhammad ........................................ TuBT3.5 1297<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–261–<br />
.................................................................................. TuCPT10.8 *<br />
Zemiti, Nabil.............................................................. WeBT3.5 2545<br />
.................................................................................. WeCPT10.8 *<br />
Zenati, Marco............................................................ TuBT5.7 1353<br />
.................................................................................. ThBT7.8 4522<br />
Zeng, Shuqing .......................................................... ThBT9.2 4575<br />
Zhang, Byoung-Tak .................................................. ThAT5.6 3963<br />
.................................................................................. ThBPT10.15 *<br />
Zhang, Fumin ........................................................... WeCT6 O<br />
.................................................................................. WeDT6 C<br />
.................................................................................. WeDT6 O<br />
.................................................................................. WeDT6.8 3583<br />
Zhang, Hao............................................................... WeAT2.4 2044<br />
.................................................................................. WeBPT10.4 *<br />
Zhang, Hong............................................................. TuBT2.1 1228<br />
.................................................................................. TuBT8.3 1473<br />
Zhang, Houxiang ...................................................... TuAT1.3 801<br />
Zhang, Jianhua......................................................... TuAT1.3 801<br />
Zhang, Jianwei ......................................................... TuAT1.3 801<br />
Zhang, Liang............................................................. MoBT9.2 744<br />
Zhang, Qin................................................................ TuCT5.4 1731<br />
.................................................................................. WeAPT10.10 *<br />
.................................................................................. ThAPT10.13 *<br />
Zhang, Xianmin ........................................................ TuBT8.3 1473<br />
Zhang, Xin ................................................................ WeBT4.6 2602<br />
.................................................................................. WeCPT10.12 *<br />
Zhang, Yinghua ........................................................ TuBT1.2 1180<br />
Zhang, Yuru.............................................................. WeBT4.6 2602<br />
.................................................................................. WeCPT10.12 *<br />
Zhao, Huijing ............................................................ WeAT2.3 2036<br />
Zhao, Jiangran.......................................................... TuAT5.7 961<br />
Zheng, Minhua.......................................................... TuAT5.7 961<br />
Zhou, David .............................................................. TuAT3.4 901<br />
.................................................................................. TuBPT10.7 *<br />
Zhou, Kai .................................................................. TuBT1.5 1201<br />
.................................................................................. TuCPT10.2 *<br />
.................................................................................. WeDT1.8 3387<br />
Zhou, Ke ................................................................... MoAT2.8 98<br />
.................................................................................. MoBT2.6 502<br />
.................................................................................. TuAPT10.6 *<br />
Zhou, Quan............................................................... TuAT3.5 907<br />
.................................................................................. TuBPT10.8 *<br />
Zhou, Weizhen ......................................................... TuCT2.4 1640<br />
.................................................................................. WeAPT10.4 *<br />
Zhou, Xuefeng .......................................................... TuBT8.3 1473<br />
Zhou, Xun ................................................................. MoAT2.8 98<br />
Zhu, Chun................................................................. WeDT2.1 3395<br />
Zhu, Haifei ................................................................ TuBT8.3 1473<br />
Zhu, Huayong ........................................................... MoBT5.5 568<br />
.................................................................................. TuAPT10.11 *<br />
Ziegler, Jakob ........................................................... MoAT2.6 86<br />
.................................................................................. MoBPT10.6 *<br />
Zillich, Michael .......................................................... TuAT1.5 813<br />
.................................................................................. TuBT1 CC<br />
.................................................................................. TuBT1.5 1201<br />
.................................................................................. TuBPT10.2 *<br />
.................................................................................. TuCPT10.2 *<br />
.................................................................................. WeDT1.8 3387<br />
Zinn, Michael ............................................................ ThDT7 C<br />
.................................................................................. ThDT7.1 5139<br />
Zirbel, Alex................................................................ TuCT6.8 1804<br />
Zlot, Robert............................................................... ThAT2 C<br />
.................................................................................. ThAT2.5 3830<br />
.................................................................................. ThBPT10.5 *<br />
Zufferey, Jean-Christophe ........................................ ThCT9.6 5015<br />
.................................................................................. ThDPT10.27<br />
Ž<br />
*<br />
Žlajpah, Leon............................................................ ThCT5.6 4832<br />
.................................................................................. ThDPT10.15 *
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–262–
Keyword Index<br />
A<br />
Adaptive Control MoAT1.2, MoAT2.8, MoAT5.4,<br />
MoAT8.7, MoBPT10.13, MoBT7.7,<br />
ThAT9.1, ThBT1.1, ThCT2.7, ThCT4.2,<br />
TuAT9.3, TuBT1.2, TuCT2.8, TuCT8.2,<br />
TuCT8.4, WeAPT10.19, WeBT2.2,<br />
WeBT7.1, WeCT7.4, WeDPT10.19<br />
Aerial Robotics MoAT4.5, MoBPT10.11, MoBT5.6,<br />
ThAT4.5, ThAT6.6, ThAT6.7,<br />
ThBPT10.11, ThBPT10.18, ThBT2.5,<br />
ThCPT10.5, ThCT1.5, ThCT8.1,<br />
ThCT8.2, ThCT8.3, ThCT8.4, ThCT8.5,<br />
ThCT8.6, ThCT8.7, ThCT9.6,<br />
ThDPT10.2, ThDPT10.22,<br />
ThDPT10.23, ThDPT10.24,<br />
ThDPT10.27, ThDT4.4, ThDT6.1,<br />
ThDT8.1, ThDT8.2, ThDT8.3,<br />
TuAPT10.12, TuAT9.5, TuBPT10.23,<br />
TuCT2.7, WeAT6.1, WeAT6.3,<br />
WeAT6.4, WeAT6.5, WeAT6.6,<br />
WeAT6.7, WeAT6.8, WeAT9.8,<br />
WeBPT10.16, WeBPT10.17,<br />
WeBPT10.18, WeBT6.1, WeBT6.2,<br />
WeBT6.3, WeBT6.4, WeBT6.5,<br />
WeBT6.6, WeBT6.7, WeBT6.8,<br />
WeBT9.5, WeCPT10.16, WeCPT10.17,<br />
WeCPT10.18, WeCPT10.26, WeCT3.2,<br />
WeCT5.3<br />
Agent-Based Systems ThAPT10.21, ThAT4.3, ThBT9.3,<br />
ThCT9.2, ThCT9.7, ThDT3.2,<br />
WeBT4.4, WeCPT10.10, WeDT8.6<br />
AI Reasoning Methods ThAT9.4, ThBPT10.25, ThBT3.3,<br />
ThBT7.2, TuCT1.7, WeCT7.1,<br />
WeDT2.2, WeDT2.3<br />
Animation and Simulation ThBT5.6, ThCPT10.15, ThCT9.8,<br />
TuBT3.8, TuBT7.5, TuCPT10.17,<br />
WeBT4.7, WeBT5.8<br />
Automation in Life MoAT1.1, MoAT1.2, MoAT1.3,<br />
Sciences: Biotechnology, MoBT1.6, TuAPT10.3, TuAT3.7,<br />
Pharmaceutical and Health TuAT5.7, TuBT3.7, TuCT3.6,<br />
Care<br />
WeAPT10.9, WeAT3.3, WeAT4.4,<br />
WeBPT10.10, WeDT2.1<br />
Autonomous Agents ThAPT10.16, ThAPT10.21, ThAT4.3,<br />
ThAT4.5, ThAT4.6, ThAT8.6, ThAT9.4,<br />
ThBPT10.11, ThBPT10.12,<br />
ThBPT10.24, ThBPT10.25, ThBT2.7,<br />
ThBT8.2, ThBT9.3, ThCT9.7, TuAT7.1,<br />
TuBT8.7, TuCT2.8, TuCT3.6,<br />
WeAPT10.9, WeAT5.3, WeAT5.4,<br />
WeAT8.6, WeBPT10.13, WeBPT10.24,<br />
WeBT5.8, WeBT6.3, WeCT5.3,<br />
WeDT7.4, WeDT8.1, WeDT8.6,<br />
WeDT8.7<br />
B<br />
Behaviour-Based Systems ThAPT10.2, ThCT9.4, ThDPT10.25,<br />
TuAT8.8, TuBT8.2, WeAT2.7,<br />
WeDT1.5<br />
Biologically-Inspired MoAT1.6, MoAT3.1, MoAT3.3,<br />
Robots<br />
MoAT5.1, MoAT5.2, MoAT5.4,<br />
MoAT5.5, MoAT5.6, MoAT5.7,<br />
MoAT5.8, MoAT8.7, MoAT9.6,<br />
MoBPT10.3, MoBPT10.13,<br />
MoBPT10.14, MoBPT10.15,<br />
MoBPT10.27, MoBT1.8, MoBT3.3,<br />
MoBT5.1, MoBT5.2, MoBT5.3,<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–263–<br />
MoBT5.4, MoBT5.5, MoBT5.6,<br />
MoBT5.7, MoBT5.8, MoBT9.7,<br />
ThAT1.3, ThAT1.4, ThAT1.6, ThAT4.6,<br />
ThAT4.7, ThAT4.8, ThAT5.5, ThAT7.3,<br />
ThAT7.6, ThAT9.1, ThAT9.3, ThAT9.5,<br />
ThAT9.8, ThBPT10.1, ThBPT10.3,<br />
ThBPT10.12, ThBPT10.14,<br />
ThBPT10.21, ThBPT10.26, ThBT4.3,<br />
ThBT4.5, ThBT7.3, ThCPT10.11,<br />
ThCT1.6, ThCT3.6, ThCT5.3, ThCT5.7,<br />
ThCT8.8, ThCT9.2, ThDPT10.3,<br />
ThDPT10.9, ThDT4.1, ThDT4.3,<br />
ThDT4.4, TuAPT10.10, TuAPT10.11,<br />
TuAPT10.12, TuAT3.4, TuAT7.8,<br />
TuAT8.1, TuAT8.2, TuAT8.3, TuAT8.4,<br />
TuAT8.6, TuAT8.7, TuBPT10.7,<br />
TuBPT10.19, TuBPT10.21, TuBT1.3,<br />
TuBT8.2, TuBT8.3, TuBT8.4, TuBT8.6,<br />
TuBT8.7, TuBT8.8, TuCPT10.19,<br />
TuCPT10.21, TuCT3.3, TuCT3.4,<br />
TuCT3.5, TuCT5.4, TuCT7.1, TuCT8.1,<br />
TuCT8.2, TuCT8.3, TuCT8.4, TuCT8.5,<br />
TuCT8.6, TuCT9.8, WeAPT10.7,<br />
WeAPT10.8, WeAPT10.10,<br />
WeAPT10.19, WeAPT10.20,<br />
WeAPT10.21, WeAT3.6, WeAT7.5,<br />
WeAT7.6, WeAT8.8, WeBPT10.9,<br />
WeBPT10.20, WeBPT10.21, WeCT7.6,<br />
WeDPT10.21, WePL.1<br />
Biomimetics MoAT1.1, MoAT3.1, MoAT9.6,<br />
MoBPT10.27, MoBT5.3, MoBT5.5,<br />
MoBT5.7, MoBT9.7, ThAT1.6,<br />
ThAT4.1, ThAT5.4, ThAT7.5,<br />
ThBPT10.3, ThBPT10.13, ThBPT10.20,<br />
ThBT4.8, ThCT3.4, ThDPT10.7,<br />
ThDT5.3, TuAPT10.11, TuAT8.5,<br />
TuAT8.7, TuAT8.8, TuBPT10.20,<br />
TuBT8.1, TuCT7.2, TuCT8.3, TuCT8.4,<br />
TuCT8.6, TuCT8.7, WeAPT10.19,<br />
WeAPT10.21, WeAT3.7, WePL.1<br />
Brain Machine Interface MoBT7.8, ThBT9.7, WeAT3.6,<br />
WeBPT10.9, WeBT1.6, WeCPT10.3,<br />
WeDT3.2<br />
C<br />
Calibration and<br />
Identification<br />
Cellular and Modular<br />
Robots<br />
MoAT1.7, MoBT1.5, MoBT3.4,<br />
ThAPT10.22, ThAPT10.23,<br />
ThAPT10.24, ThAT2.3, ThAT2.4,<br />
ThBPT10.4, ThBT4.1, ThCT2.7,<br />
TuAPT10.2, TuAPT10.7, TuBT3.8,<br />
WeAT4.4, WeBPT10.10, WeCT9.1,<br />
WeCT9.2, WeCT9.3, WeCT9.4,<br />
WeCT9.5, WeCT9.6, WeCT9.7,<br />
WeDPT10.25, WeDPT10.26,<br />
WeDPT10.27, WeDT9.1, WeDT9.2,<br />
WeDT9.3, WeDT9.4, WeDT9.5,<br />
WeDT9.6, WeDT9.8<br />
MoAT9.7, ThAPT10.19, ThBT4.5,<br />
ThCPT10.11, ThCT4.5, ThCT4.7,<br />
ThDPT10.11, WeCT5.7, WeDT8.3,<br />
WeDT8.4<br />
Climbing robots ThAT8.5, ThBPT10.23, ThBT2.8,<br />
ThDT5.1, ThDT5.3, ThDT5.4, ThDT6.4,<br />
TuBT8.3, TuBT8.5, TuCPT10.20,<br />
Cognitive Human-Robot<br />
Interaction<br />
WeBT7.7<br />
MoAT8.6, MoBPT10.24, MoBT3.3,<br />
ThAPT10.3, ThBT9.7, TuAT1.7,
TuAT5.3, TuBT1.5, TuBT1.8, TuBT9.1,<br />
TuCPT10.2, TuCT5.8, WeAT1.3,<br />
WeAT1.5, WeAT2.8, WeAT3.8,<br />
WeBPT10.2, WeBT1.1, WeBT1.2,<br />
WeBT1.3, WeBT1.8, WeBT4.4,<br />
WeCPT10.10, WeCT1.2, WeCT1.5,<br />
WeDPT10.2, WeDT1.1, WeDT1.6,<br />
WeDT1.7, WeDT1.8, WeDT2.1,<br />
WeDT3.3<br />
Collision Avoidance MoAT6.6, MoBPT10.18, ThAT3.1,<br />
ThAT3.2, ThAT3.5, ThAT4.4,<br />
ThBPT10.8, ThBPT10.10, ThBT3.1,<br />
ThBT4.6, ThBT9.4, ThCPT10.12,<br />
ThCPT10.25, ThCT3.3, ThCT8.5,<br />
ThDPT10.23, TuCT1.1, TuCT1.2,<br />
TuCT1.3, TuCT1.4, TuCT1.5, TuCT1.6,<br />
TuCT1.7, TuCT2.1, TuCT9.7,<br />
WeAPT10.1, WeAPT10.2, WeAPT10.3,<br />
WeAT8.7, WeBT4.5, WeBT5.4,<br />
WeBT9.2, WeCPT10.11, WeCPT10.13,<br />
WeDT1.3, WeDT5.7, WeDT5.8,<br />
Compliance and<br />
Impedance Control<br />
WeDT8.2<br />
MoAT9.4, MoBPT10.25, MoBT8.5,<br />
ThAT1.5, ThAT1.6, ThAT7.1, ThAT9.3,<br />
ThBPT10.2, ThBPT10.3, ThBT1.7,<br />
ThBT1.8, ThBT4.3, ThCT1.4,<br />
ThDPT10.1, TuAPT10.20, TuAT6.3,<br />
TuAT6.8, TuAT7.4, TuBPT10.16,<br />
TuCT6.6, WeAPT10.15, WeBT4.2,<br />
WeCT3.5, WeCT7.5, WeDPT10.8,<br />
WeDPT10.20<br />
Compliant Assembly MoBT9.4, TuAPT10.22<br />
Computer Vision MoAT1.2, MoAT1.4, MoBPT10.1,<br />
MoBT3.7, ThAPT10.6, ThAPT10.18,<br />
ThAT2.2, ThAT2.8, ThAT6.3, ThAT6.4,<br />
ThAT6.5, ThAT6.6, ThAT6.7, ThAT6.8,<br />
ThAT9.6, ThBPT10.16, ThBPT10.17,<br />
ThBPT10.18, ThBPT10.27, ThBT4.7,<br />
ThBT6.3, ThBT6.4, ThBT6.5, ThBT6.6,<br />
ThBT6.7, ThBT6.8, ThCPT10.16,<br />
ThCPT10.17, ThCPT10.18, ThCT5.4,<br />
ThCT6.1, ThCT6.3, ThCT6.4, ThCT6.5,<br />
ThCT6.6, ThCT6.7, ThCT6.8, ThCT8.2,<br />
ThDPT10.13, ThDPT10.16,<br />
ThDPT10.17, ThDPT10.18, TuAT1.1,<br />
TuAT1.2, TuAT1.3, TuAT1.6, TuAT1.7,<br />
TuAT6.5, TuAT8.6, TuBPT10.3,<br />
TuBPT10.14, TuBPT10.21, TuBT1.1,<br />
TuBT1.2, TuBT1.3, TuBT1.4, TuBT1.5,<br />
TuBT1.6, TuBT1.7, TuBT2.2, TuBT2.7,<br />
TuBT2.8, TuBT7.6, TuBT9.3,<br />
TuCPT10.1, TuCPT10.2, TuCPT10.3,<br />
TuCPT10.18, TuCT1.6, TuCT2.3,<br />
TuCT2.6, WeAPT10.3, WeAPT10.6,<br />
WeAT2.1, WeAT2.4, WeAT3.1,<br />
WeAT3.3, WeAT3.4, WeAT3.5,<br />
WeAT9.2, WeAT9.3, WeAT9.4,<br />
WeAT9.5, WeAT9.6, WeAT9.7,<br />
WeBPT10.4, WeBPT10.7, WeBPT10.8,<br />
WeBPT10.25, WeBPT10.26,<br />
WeBPT10.27, WeBT9.7, WeCT2.2,<br />
WeCT2.4, WeCT2.6, WeCT2.8,<br />
WeCT9.1, WeCT9.6, WeDPT10.4,<br />
WeDPT10.6, WeDPT10.27, WeDT1.1,<br />
WeDT2.6, WeDT2.8, WeDT7.3,<br />
WeDT7.6<br />
Contact Modelling MoAT7.3, ThAT9.2, ThBT1.8, ThBT7.1,<br />
TuBT6.6, TuBT7.5, TuBT7.6,<br />
TuCPT10.15, TuCPT10.17,<br />
TuCPT10.18, WeBT4.7<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–264–<br />
Control Architectures and<br />
Programming<br />
MoBT9.1, ThAPT10.18, ThAT3.6,<br />
ThBPT10.9, ThCT2.3, TuAT7.1,<br />
TuAT7.2, TuAT7.3, TuAT7.5,<br />
TuBPT10.17, WeAT9.8, WeCT5.7,<br />
WeDT7.6<br />
Cooperating Robots MoAT2.8, MoBT2.1, ThAPT10.16,<br />
ThAPT10.20, ThBT2.8, ThBT4.2,<br />
ThBT8.3, ThBT9.5, ThCPT10.26,<br />
ThCT2.8, ThCT9.3, ThCT9.5, ThCT9.6,<br />
ThDPT10.26, ThDPT10.27, ThDT3.2,<br />
TuCT2.7, WeAT6.4, WeAT6.6,<br />
WeAT8.4, WeAT8.5, WeAT8.7,<br />
WeBPT10.16, WeBPT10.18,<br />
WeBPT10.22, WeBPT10.23, WeBT1.8,<br />
WeBT8.6, WeBT8.7, WeBT8.8,<br />
WeCPT10.24, WeCT1.4, WeCT5.1,<br />
WeCT8.2, WeCT8.4, WeDPT10.1,<br />
WeDPT10.22, WeDT1.2, WeDT7.4,<br />
WeDT8.1, WeDT8.5<br />
Cooperative Manipulators MoAT6.6, MoBPT10.18, ThBT1.3,<br />
TuBT7.1<br />
D<br />
Demining Systems TuAT9.7<br />
Dexterous Manipulation ThAT1.2, ThAT7.5, ThAT7.7,<br />
ThBPT10.20, ThBT1.4, ThCPT10.1,<br />
ThDT6.1, TuBT6.7, TuBT7.4, TuBT7.7,<br />
TuBT8.1, TuCPT10.16, TuCT6.2,<br />
TuCT6.7, TuCT6.8, WeBT3.1,<br />
WeCT2.5, WeDPT10.5<br />
Distributed Robot Systems MoAT3.5, MoAT4.5, MoBPT10.8,<br />
MoBPT10.11, MoBT2.1, ThAPT10.19,<br />
ThAT4.6, ThBPT10.12, ThBT1.1,<br />
ThBT4.1, ThBT4.2, ThBT4.6, ThBT9.5,<br />
ThCPT10.12, ThCPT10.26, ThCT2.8,<br />
ThCT4.2, ThCT4.3, ThCT4.5, ThCT4.6,<br />
ThCT4.8, ThCT9.1, ThCT9.2, ThCT9.3,<br />
ThCT9.4, ThCT9.5, ThCT9.6, ThCT9.8,<br />
ThDPT10.11, ThDPT10.12,<br />
ThDPT10.25, ThDPT10.26,<br />
ThDPT10.27, ThDT8.4, TuAT7.3,<br />
TuCT8.3, TuCT8.6, WeAPT10.21,<br />
WeAT8.1, WeAT8.3, WeAT8.4,<br />
WeAT8.5, WeBPT10.22, WeBPT10.23,<br />
WeBT8.1, WeBT8.3, WeBT8.6,<br />
WeBT8.8, WeCPT10.24, WeCT4.2,<br />
WeCT5.1, WeCT8.1, WeCT8.2,<br />
WeCT8.3, WeCT9.5, WeDPT10.26,<br />
Domestic Robots and<br />
Home Automation<br />
WeDT8.3, WeDT8.4<br />
ThCT6.6, ThCT9.5, ThDPT10.18,<br />
ThDPT10.26, TuAT1.2, TuAT1.4,<br />
TuAT2.1, TuBPT10.1, TuBT1.5,<br />
TuBT1.8, TuCPT10.2, WeAT1.1,<br />
WeAT9.3, WeBT1.5, WeCPT10.2,<br />
WeCT1.6, WeCT2.4, WeCT3.4,<br />
WeCT3.7, WeDPT10.3, WeDPT10.4,<br />
WeDPT10.7<br />
Dynamics MoAT7.3, MoAT7.5, MoAT7.6,<br />
MoAT7.7, MoBPT10.20, MoBPT10.21,<br />
MoBT5.6, MoBT5.8, MoBT6.7,<br />
ThAPT10.24, ThAT1.8, ThAT4.3,<br />
ThAT7.5, ThAT7.6, ThAT8.1, ThAT9.2,<br />
ThAT9.5, ThBPT10.20, ThBPT10.21,<br />
ThBPT10.26, ThCT4.3, ThDT8.2,<br />
TuAPT10.12, TuAT8.7, TuBT7.5,<br />
TuCPT10.17, TuCT7.2, WeAT7.1,<br />
WeAT7.4, WeBPT10.19, WeBT6.5,<br />
WeBT7.1, WeBT7.5, WeBT7.6,<br />
WeCPT10.17, WeCPT10.20,<br />
WeCPT10.21, WeDT9.2, WeDT9.6<br />
E
Education Robotics WeBT1.2<br />
Entertainment Robotics MoAT3.4, MoBPT10.7, ThDT6.1,<br />
WeAT1.2, WeAT1.6, WeAT1.7,<br />
WeBPT10.3, WeCT1.8<br />
Evolutionary Robotics ThBT4.4, ThCPT10.10, ThCT9.3,<br />
WeAT7.7, WeBT8.7<br />
F<br />
Failure Detection and WeAT8.3, WeCT3.7<br />
Recovery<br />
Field Robots MoAT6.1, MoAT6.3, MoAT6.4,<br />
MoAT6.7, MoAT6.8, MoAT7.7,<br />
MoBPT10.16, MoBT2.2, MoBT2.4,<br />
MoBT2.7, MoBT6.3, MoBT6.5,<br />
MoBT6.8, MoBT8.6, ThAT2.5,<br />
ThAT3.4, ThAT6.8, ThAT8.2, ThAT8.3,<br />
ThAT8.4, ThAT8.5, ThBPT10.5,<br />
ThBPT10.7, ThBPT10.22, ThBPT10.23,<br />
ThBT2.7, ThBT6.1, ThBT8.4, ThBT8.6,<br />
ThBT9.1, ThBT9.6, ThCPT10.22,<br />
ThCPT10.24, ThCPT10.27, ThCT3.2,<br />
ThCT3.7, ThCT8.6, ThDPT10.24,<br />
ThDT5.4, TuAPT10.4, TuAPT10.14,<br />
TuAPT10.21, TuCT1.3, TuCT7.7,<br />
WeAT6.3, WeAT9.1, WeBT2.5,<br />
WeBT7.5, WeCPT10.5, WeCPT10.20,<br />
WeCT3.6, WeCT6.1, WeCT6.3,<br />
WeDPT10.9, WeDT6.3, WeDT8.8,<br />
WeDT9.7<br />
Flexible Arms MoAT9.2, ThAT1.4, ThAT3.8, ThAT7.2,<br />
ThBPT10.1, ThBT1.2, ThBT1.6,<br />
ThBT1.7, ThCPT10.3, ThCT1.6,<br />
ThDPT10.3, ThDT7.1, TuAT8.5,<br />
TuBPT10.20, TuBT6.3, TuCT6.6,<br />
TuCT7.5, TuCT7.6, TuCT9.1,<br />
WeAPT10.15, WeAPT10.17,<br />
WeAPT10.18, WeBT3.6, WeCPT10.9,<br />
WeDT9.3<br />
Force and Tactile Sensing MoAT1.6, MoAT1.7, MoAT7.4,<br />
MoBPT10.3, MoBPT10.19, ThAPT10.7,<br />
ThAPT10.9, ThAT1.4, ThAT9.7,<br />
ThBPT10.1, ThBT7.7, ThBT8.5,<br />
ThCPT10.23, ThCT2.4, ThDPT10.4,<br />
TuAT5.2, TuBT9.7, TuCT9.2, TuCT9.3,<br />
TuCT9.6, WeAPT10.24, WeAT4.5,<br />
WeAT4.6, WeBPT10.11, WeBPT10.12,<br />
WeBT4.5, WeCPT10.11, WeDT3.1,<br />
WeDT3.2, WeDT3.4, WeDT3.6,<br />
WeDT3.7, WeDT9.1, WeDT9.7<br />
Force Control MoAT7.6, MoAT9.4, MoBPT10.21,<br />
MoBPT10.25, MoBT5.4, ThAT1.3,<br />
ThBT1.2, ThBT1.8, ThCT7.1,<br />
TuAPT10.10, TuAT3.7, TuBT7.7,<br />
TuCT9.3, TuCT9.6, WeAPT10.24,<br />
WeBT3.6, WeCPT10.9, WeCT4.1,<br />
WeCT7.2<br />
G<br />
Gesture, Posture, Social<br />
Spaces and Facial<br />
Expressions<br />
MoAT2.6, MoBPT10.6, ThAPT10.2,<br />
WeAT1.5, WeAT1.6, WeBPT10.2,<br />
WeBPT10.3, WeDT1.5<br />
Grasping MoAT8.8, ThAPT10.7, ThBT1.6,<br />
ThCPT10.3, ThCT8.8, TuAT6.1,<br />
TuAT6.3, TuAT6.4, TuAT6.5, TuAT6.6,<br />
TuAT6.7, TuAT6.8, TuBPT10.13,<br />
TuBPT10.14, TuBPT10.15, TuBT1.8,<br />
TuBT6.1, TuBT6.4, TuBT6.6, TuBT6.8,<br />
TuBT7.2, TuBT8.1, TuBT9.6, TuBT9.7,<br />
TuCPT10.13, TuCPT10.15,<br />
TuCPT10.24, TuCT6.1, TuCT6.2,<br />
TuCT6.3, TuCT6.4, TuCT6.5, TuCT6.7,<br />
TuCT6.8, TuCT9.3, WeAPT10.13,<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–265–<br />
Haptics and Haptic<br />
Interfaces<br />
Human detection &<br />
tracking<br />
Human Performance<br />
Augmentation<br />
Humanoid and Bipedal<br />
Locomotion<br />
WeAPT10.14, WeBT6.2, WeCT2.5,<br />
WeDPT10.5, WeDT3.4<br />
H<br />
MoAT4.3, MoAT4.7, MoBT7.2,<br />
MoBT7.3, MoBT7.4, MoBT7.5,<br />
ThAPT10.8, ThCT1.8, TuAPT10.16,<br />
TuAPT10.17, TuAT6.7, TuBT5.3,<br />
TuCT9.6, WeAPT10.24, WeAT4.1,<br />
WeAT4.3, WeAT4.4, WeAT4.5,<br />
WeAT4.6, WeAT4.7, WeAT4.8,<br />
WeBPT10.10, WeBPT10.11,<br />
WeBPT10.12, WeBT4.1, WeBT4.3,<br />
WeBT4.5, WeBT4.6, WeBT4.7,<br />
WeBT4.8, WeCPT10.11, WeCPT10.12,<br />
WeCT4.1, WeCT4.2, WeCT4.3,<br />
WeCT4.5, WeCT4.6, WeCT4.7,<br />
WeCT4.8, WeDPT10.11, WeDPT10.12,<br />
WeDT3.1, WeDT3.2, WeDT3.3,<br />
WeDT3.5, WeDT3.7, WeDT3.8,<br />
WeDT9.8<br />
MoAT3.8, MoBT3.6, ThAPT10.5,<br />
ThAPT10.16, ThAPT10.17, ThAT2.6,<br />
ThAT2.7, ThBPT10.6, TuAPT10.9,<br />
WeAT2.3, WeBT1.4, WeCPT10.1,<br />
WeCT1.4, WeCT1.6, WeCT2.3,<br />
WeCT3.1, WeCT5.4, WeDPT10.1,<br />
WeDPT10.3, WeDPT10.13, WeDT2.5,<br />
WeDT7.1, WeDT7.2, WeDT7.3,<br />
WeDT7.4, WeDT7.5, WeDT7.7,<br />
WeDT7.8<br />
MoAT9.1, ThAPT10.1, ThBT8.5,<br />
ThCPT10.23, TuBT5.4, TuCPT10.10,<br />
TuCT5.5, WeAPT10.11, WeAT2.8,<br />
WeCT1.8, WeDT1.4, WeDT3.7<br />
MoAT8.1, ThAT5.1, ThAT5.3, ThAT5.4,<br />
ThAT5.5, ThAT5.6, ThAT5.7, ThAT7.3,<br />
ThBPT10.13, ThBPT10.14,<br />
ThBPT10.15, ThBT5.1, ThBT5.6,<br />
ThBT5.7, ThBT5.8, ThCPT10.15,<br />
ThCT5.1, ThCT5.3, ThDT5.2, TuBT8.2,<br />
TuCT5.3, TuCT7.1, WeAT7.1,<br />
WeAT7.3, WeAT7.4, WeAT7.5,<br />
WeAT7.8, WeBPT10.19, WeBPT10.20,<br />
WeBT7.1, WeBT7.2, WeBT7.4,<br />
WeCPT10.19, WeCT2.3, WeCT7.6,<br />
WeCT7.8, WeDPT10.21<br />
Humanoid Robots MoBT7.8, ThAPT10.9, ThAT3.6,<br />
ThAT5.1, ThAT5.3, ThAT5.4, ThAT5.6,<br />
ThAT5.7, ThAT5.8, ThAT9.2, ThAT9.6,<br />
ThAT9.7, ThBPT10.9, ThBPT10.13,<br />
ThBPT10.15, ThBPT10.27, ThBT4.3,<br />
ThBT5.1, ThBT5.3, ThBT5.4, ThBT5.5,<br />
ThBT5.6, ThBT5.8, ThBT6.7,<br />
ThCPT10.13, ThCPT10.14,<br />
ThCPT10.15, ThCT5.1, ThCT5.4,<br />
ThCT5.5, ThCT5.6, ThCT5.7, ThCT5.8,<br />
ThDPT10.13, ThDPT10.14,<br />
ThDPT10.15, TuAT7.1, TuAT7.2,<br />
TuAT7.7, TuBT1.1, TuBT9.3, TuCT1.4,<br />
TuCT5.3, TuCT6.5, WeAPT10.1,<br />
WeAPT10.14, WeAT1.6, WeAT7.3,<br />
WeBPT10.3, WeCT1.2, WeCT1.7,<br />
WeCT1.8, WeCT7.2, WeCT7.3,<br />
WeCT7.5, WeCT7.6, WeCT7.7,<br />
WeCT9.4, WeDPT10.20, WeDPT10.21,<br />
WeDPT10.25, WeDT1.7, WeDT2.7,<br />
Hydraulic/Pneumatic<br />
Actuators<br />
WeDT3.6<br />
MoBT7.4, MoBT9.1, MoBT9.4,<br />
ThBT7.3, ThCT5.7, TuAPT10.16,<br />
TuAPT10.22, TuBT5.5, TuCPT10.11,
TuCT7.2, TuCT7.4, WeAPT10.16,<br />
WeCT3.5, WeDPT10.8<br />
I<br />
Industrial Robots MoBT9.1, ThAT1.7, ThAT7.2, ThBT9.8,<br />
ThCT2.1, ThCT2.2, ThCT2.4, ThCT2.5,<br />
ThCT2.6, ThCT2.7, ThDPT10.4,<br />
ThDPT10.5, ThDPT10.6, ThDT4.2,<br />
WeAT8.3, WeBT2.4, WeBT9.8,<br />
Intelligent Transportation<br />
Systems<br />
WeCPT10.4, WeCT2.2, WeCT8.8<br />
ThAT3.4, ThBPT10.7, ThBT6.4,<br />
ThBT9.2, ThBT9.3, ThBT9.4, ThBT9.5,<br />
ThBT9.6, ThBT9.7, ThBT9.8,<br />
ThCPT10.16, ThCPT10.25,<br />
ThCPT10.26, ThCPT10.27, TuCT1.6,<br />
TuCT2.2, WeAPT10.3, WeAT2.3,<br />
WeCT8.8, WeDT8.8<br />
Intrusion Detection, MoBT3.6, TuAPT10.9<br />
Identification and Security<br />
J<br />
Joint/Mechanism MoBT9.2, MoBT9.6, ThAPT10.24,<br />
ThAT1.7, ThAT7.3, ThCT2.6,<br />
ThDPT10.6, ThDT3.1, ThDT5.3,<br />
TuAPT10.24, TuAT5.5, TuAT8.3,<br />
TuBPT10.11, TuBT6.3, TuBT7.3,<br />
TuCT7.4, TuCT7.5, TuCT7.8, TuCT9.8,<br />
WeAPT10.16, WeAPT10.17, WeBT7.5,<br />
WeCPT10.20, WeDT9.6<br />
K<br />
Kinematics MoAT5.8, MoAT7.5, MoAT9.5,<br />
MoBPT10.20, MoBPT10.26, MoBT1.8,<br />
MoBT8.2, ThAPT10.10, ThAT3.7,<br />
ThAT7.7, ThAT8.4, ThBPT10.22,<br />
ThBT3.6, ThBT3.7, ThBT3.8, ThBT9.1,<br />
ThCPT10.9, ThCT2.2, ThCT7.3,<br />
ThDT3.1, TuAT8.5, TuBPT10.20,<br />
TuBT7.3, TuCT1.8, TuCT7.7, TuCT8.2,<br />
TuCT9.1, WeAT3.1, WeCT2.1,<br />
WeCT7.8, WeDT5.4<br />
L<br />
Learning and Adaptive MoAT8.1, MoAT8.2, MoAT8.3,<br />
Systems<br />
MoAT8.4, MoAT8.5, MoAT8.6,<br />
MoAT8.7, MoBPT10.22, MoBPT10.23,<br />
MoBPT10.24, MoBT2.7, MoBT5.5,<br />
MoBT7.6, MoBT8.1, MoBT8.2,<br />
MoBT8.3, MoBT8.4, MoBT8.7,<br />
ThAPT10.3, ThAPT10.4, ThAT5.6,<br />
ThAT9.1, ThAT9.8, ThBPT10.15,<br />
ThCT1.3, ThCT1.4, ThDPT10.1,<br />
TuAPT10.11, TuAPT10.18,<br />
TuAPT10.19, TuAT1.5, TuAT1.8,<br />
TuAT6.4, TuAT6.7, TuAT8.1, TuAT9.8,<br />
TuBPT10.2, TuBPT10.13, TuBT1.3,<br />
TuBT1.4, TuBT7.1, TuBT9.1, TuBT9.3,<br />
TuBT9.6, TuBT9.7, TuBT9.8,<br />
TuCPT10.1, TuCPT10.24, TuCT8.1,<br />
WeAT2.2, WeAT2.6, WeAT2.8,<br />
WeAT3.6, WeAT5.6, WeAT8.5,<br />
WeAT9.6, WeBPT10.6, WeBPT10.9,<br />
WeBPT10.15, WeBPT10.23,<br />
WeBPT10.27, WeCT1.2, WeCT3.2,<br />
WeCT4.4, WeCT6.7, WeDPT10.10,<br />
WeDT1.6, WeDT1.8, WeDT2.2,<br />
WeDT2.3, WeDT2.4, WeDT8.7<br />
Legged Robots MoAT5.3, MoAT5.7, MoBT5.4,<br />
ThAT1.3, ThAT5.5, ThAT5.8,<br />
ThBPT10.14, ThBT5.5, ThBT5.7,<br />
ThCPT10.14, ThCT5.6, ThDPT10.15,<br />
ThDT3.1, ThDT4.2, ThDT4.3, ThDT4.4,<br />
ThDT5.1, ThDT6.3, TuAPT10.10,<br />
TuBT8.4, TuBT8.6, TuBT8.8,<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–266–<br />
TuCPT10.19, TuCPT10.21, TuCT5.3,<br />
WeAT7.1, WeAT7.2, WeAT7.3,<br />
WeAT7.5, WeAT7.6, WeAT7.7,<br />
WeBPT10.20, WeBPT10.21, WeBT2.6,<br />
WeBT7.3, WeBT7.4, WeBT7.6,<br />
WeBT7.7, WeBT7.8, WeCPT10.6,<br />
WeCPT10.19, WeCPT10.21, WeCT7.7<br />
Localization MoAT2.1, MoAT2.2, MoAT2.3,<br />
MoAT2.4, MoAT2.5, MoAT2.6,<br />
MoAT2.7, MoAT2.8, MoAT3.5,<br />
MoAT3.7, MoAT3.8, MoBPT10.4,<br />
MoBPT10.5, MoBPT10.6, MoBPT10.8,<br />
MoBT2.1, MoBT2.2, MoBT2.3,<br />
MoBT2.4, MoBT2.5, MoBT2.6,<br />
MoBT2.7, MoBT6.5, ThAT2.4,<br />
ThBPT10.4, ThBT2.1, ThBT2.3,<br />
ThBT3.1, ThBT7.7, ThCT2.4, ThCT2.5,<br />
ThDPT10.4, ThDPT10.5, ThDT3.4,<br />
ThDT7.2, TuAPT10.4, TuAPT10.5,<br />
TuAPT10.6, TuAPT10.14, TuBT2.2,<br />
TuBT5.7, TuCT2.3, TuCT2.6,<br />
WeAPT10.6, WeAT2.5, WeBPT10.5,<br />
WeBT2.5, WeBT2.7, WeBT8.5,<br />
WeCPT10.5, WeCPT10.23, WeCT1.3,<br />
WeCT1.7, WeCT2.3, WeCT8.3,<br />
WeCT8.4, WeDPT10.22, WeDT6.8<br />
M<br />
Manipulation and<br />
Compliant Assembly<br />
ThAPT10.2, ThBT1.6, ThCPT10.3,<br />
ThDT6.2, TuCT3.7, TuCT6.6,<br />
WeAPT10.15, WeDT1.5<br />
Manipulation Planning MoBT9.8, ThBT1.5, ThBT3.4,<br />
ThCPT10.2, ThCPT10.7, ThCT1.2,<br />
ThCT1.3, TuAT6.1, TuAT6.6, TuAT6.8,<br />
TuAT8.8, TuBPT10.15, TuBT6.1,<br />
TuBT6.6, TuBT6.7, TuBT7.2,<br />
TuCPT10.15, TuCT6.3, TuCT6.4,<br />
WeAPT10.13, WeAT1.3, WeCT7.1<br />
Mapping MoAT2.2, MoAT6.3, MoBT3.4,<br />
ThAPT10.22, ThAT2.2, ThAT2.3,<br />
ThAT2.5, ThAT6.3, ThAT6.7,<br />
ThBPT10.5, ThBT2.1, ThBT2.2,<br />
ThBT2.3, ThBT2.4, ThBT2.5, ThBT2.6,<br />
ThBT2.7, ThBT2.8, ThBT4.2, ThBT6.3,<br />
ThBT6.5, ThCPT10.4, ThCPT10.5,<br />
ThCPT10.6, ThCPT10.17, ThCT6.3,<br />
ThPT11.1, TuAPT10.7, TuAT2.2,<br />
TuAT2.5, TuAT2.6, TuAT9.1,<br />
TuBPT10.5, TuBPT10.6, TuBT2.3,<br />
TuBT2.6, TuBT2.8, TuCPT10.6,<br />
WeBT2.8, WeCT2.5, WeCT8.5,<br />
WeDPT10.5, WeDPT10.23, WeDT9.4<br />
Marine Robotics MoAT6.8, MoBT5.8, ThAPT10.13,<br />
ThAPT10.14, ThAPT10.15,<br />
ThAPT10.23, ThCT3.2, ThCT3.3,<br />
ThCT3.4, ThCT3.5, ThCT3.6, ThCT3.7,<br />
ThDPT10.7, ThDPT10.8, ThDPT10.9,<br />
ThDT3.2, ThDT3.3, ThDT3.4, TuAT9.4,<br />
TuAT9.6, TuBPT10.22, TuBPT10.24,<br />
TuBT2.3, TuBT9.4, TuCPT10.22,<br />
WeCT6.1, WeCT6.3, WeCT6.5,<br />
WeCT6.6, WeCT6.7, WeCT6.8,<br />
WeDPT10.17, WeDPT10.18, WeDT6.1,<br />
WeDT6.3, WeDT6.4, WeDT6.5,<br />
WeDT6.6, WeDT6.7, WeDT6.8,<br />
WeDT9.5<br />
Mechanism Design MoAT1.8, MoAT3.3, MoAT5.5,<br />
MoAT9.1, MoAT9.2, MoAT9.4,<br />
MoAT9.5, MoBPT10.14, MoBPT10.25,<br />
MoBPT10.26, MoBT5.3, MoBT9.5,<br />
ThAT7.4, ThBPT10.19, ThBT7.5,
Medical Robots and<br />
Systems<br />
ThBT8.4, ThCPT10.20, ThCPT10.22,<br />
ThCT3.4, ThCT7.6, ThDPT10.7,<br />
ThDPT10.21, ThDT5.1, ThDT5.4,<br />
TuAPT10.23, TuAT5.3, TuAT5.5,<br />
TuAT5.7, TuAT5.8, TuBPT10.11,<br />
TuBT6.1, TuCT7.3, TuCT7.4, TuCT7.5,<br />
TuCT7.6, TuCT7.8, TuCT9.5, TuCT9.8,<br />
TuPL.1, WeAPT10.16, WeAPT10.17,<br />
WeAPT10.18, WeAPT10.23, WeAT4.7,<br />
WeBT4.1, WeBT4.2, WeBT7.6,<br />
WeCPT10.21<br />
MoBT1.1, MoBT1.4, MoBT7.1,<br />
MoBT9.3, ThBT7.5, ThBT7.6, ThBT7.7,<br />
ThBT7.8, ThCPT10.20, ThCPT10.21,<br />
ThCT7.2, ThCT7.4, ThDPT10.19,<br />
ThDT7.1, ThDT7.2, ThDT7.3, ThDT7.4,<br />
TuAPT10.1, TuAT3.4, TuAT5.1,<br />
TuAT5.2, TuAT5.3, TuAT5.4, TuAT5.5,<br />
TuAT5.6, TuAT5.7, TuAT5.8,<br />
TuBPT10.7, TuBPT10.10, TuBPT10.11,<br />
TuBPT10.12, TuBT3.3, TuBT3.6,<br />
TuBT5.1, TuBT5.2, TuBT5.3, TuBT5.4,<br />
TuBT5.5, TuBT5.6, TuBT5.7, TuBT5.8,<br />
TuCPT10.9, TuCPT10.10,<br />
TuCPT10.11, TuCPT10.12, TuCT3.5,<br />
TuCT5.7, WeAPT10.8, WeAT3.1,<br />
WeAT3.2, WeAT3.4, WeAT3.5,<br />
WeAT3.7, WeAT3.8, WeAT4.5,<br />
WeAT9.4, WeAT9.7, WeBPT10.7,<br />
WeBPT10.8, WeBPT10.11,<br />
WeBPT10.25, WeBT3.1, WeBT3.2,<br />
WeBT3.3, WeBT3.4, WeBT3.5,<br />
WeBT3.6, WeBT3.7, WeBT3.8,<br />
WeBT4.3, WeBT9.3, WeCPT10.7,<br />
WeCPT10.8, WeCPT10.9, WeCT4.1,<br />
WeDT7.8, WeDT9.3<br />
Micro-manipulation MoAT1.3, MoAT1.5, MoBPT10.2,<br />
MoBT1.2, MoBT1.3, MoBT1.4,<br />
MoBT1.5, MoBT1.6, MoBT1.7,<br />
ThAT7.7, ThDT7.4, TuAPT10.1,<br />
TuAPT10.2, TuAPT10.3, TuAT3.3,<br />
TuAT3.5, TuAT3.6, TuAT3.7, TuAT3.8,<br />
TuBPT10.8, TuBPT10.9, TuBT3.4,<br />
TuBT3.8, TuBT5.5, TuCPT10.7,<br />
TuCPT10.11, TuCT3.3, TuCT3.7,<br />
TuCT3.8<br />
Micro/Nano Robots MoAT1.1, MoAT1.3, MoAT1.4,<br />
MoAT1.8, MoAT9.8, MoBPT10.1,<br />
MoBT1.1, MoBT1.3, MoBT1.7,<br />
MoBT1.8, ThAT3.7, ThAT4.8, ThAT6.1,<br />
ThBT4.5, ThCPT10.11, ThCT4.1,<br />
ThDT4.3, TuAT3.1, TuAT3.4, TuAT3.5,<br />
TuAT3.6, TuAT3.8, TuAT8.6,<br />
TuBPT10.7, TuBPT10.8, TuBPT10.9,<br />
TuBPT10.21, TuBT3.1, TuBT3.3,<br />
TuBT3.4, TuBT3.5, TuBT3.6, TuBT3.7,<br />
TuBT7.3, TuCPT10.7, TuCPT10.8,<br />
TuCPT10.9, TuCT3.1, TuCT3.3,<br />
TuCT3.4, TuCT3.5, TuCT3.6, TuCT3.7,<br />
TuCT3.8, WeAPT10.7, WeAPT10.8,<br />
WeAPT10.9<br />
Mining Robotics MoBT8.6, ThBT2.2, ThBT9.6,<br />
ThCPT10.27, TuAPT10.21<br />
Mobile Manipulation MoAT8.8, ThAPT10.7, ThBT2.4,<br />
ThCPT10.4, ThCT1.1, ThCT1.4,<br />
ThCT1.5, ThCT4.4, ThDPT10.1,<br />
ThDPT10.2, ThDPT10.10, TuAT7.4,<br />
TuBPT10.16, TuBT3.4, TuCPT10.7,<br />
WeBT4.4, WeBT6.1, WeCPT10.10,<br />
WeCT4.5, WeCT7.5, WeDPT10.11,<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–267–<br />
WeDPT10.20, WeDT3.4<br />
Motion and Path Planning MoBT6.3, MoBT9.8, ThAPT10.10,<br />
ThAPT10.11, ThAPT10.12,<br />
ThAPT10.15, ThAT1.5, ThAT1.8,<br />
ThAT3.1, ThAT3.2, ThAT3.3, ThAT3.5,<br />
ThAT4.4, ThBPT10.2, ThBPT10.8,<br />
ThBPT10.10, ThBT3.1, ThBT3.2,<br />
ThBT3.3, ThBT3.4, ThBT3.6, ThBT3.7,<br />
ThBT4.4, ThBT4.8, ThBT5.5, ThBT5.8,<br />
ThBT8.1, ThCPT10.7, ThCPT10.9,<br />
ThCPT10.10, ThCPT10.14, ThCT1.1,<br />
ThCT3.3, ThCT8.3, ThDT3.3, ThDT5.2,<br />
ThDT7.3, ThDT8.3, TuAT2.1, TuAT9.2,<br />
TuAT9.6, TuAT9.7, TuBPT10.24,<br />
TuBT3.5, TuBT5.6, TuBT8.6, TuBT9.8,<br />
TuCPT10.8, TuCPT10.12,<br />
TuCPT10.21, TuCT1.1, TuCT1.2,<br />
TuCT1.7, TuCT1.8, TuCT2.8, TuCT8.8,<br />
WeAT1.2, WeAT1.5, WeAT1.8,<br />
WeAT5.1, WeAT5.3, WeAT5.4,<br />
WeAT5.5, WeAT5.6, WeAT5.7,<br />
WeAT5.8, WeAT6.3, WeAT7.4,<br />
WeAT8.2, WeBPT10.2, WeBPT10.13,<br />
WeBPT10.14, WeBPT10.15,<br />
WeBPT10.19, WeBT3.5, WeBT5.1,<br />
WeBT5.3, WeBT5.4, WeBT5.5,<br />
WeBT5.6, WeBT5.7, WeBT5.8,<br />
WeBT6.5, WeCPT10.8, WeCPT10.13,<br />
WeCPT10.14, WeCPT10.15,<br />
WeCPT10.17, WeCT1.1, WeCT3.8,<br />
WeCT5.3, WeCT5.5, WeCT5.6,<br />
WeCT5.7, WeCT6.5, WeCT6.6,<br />
WeCT8.1, WeCT8.5, WeCT8.6,<br />
WeCT8.7, WeDPT10.14, WeDPT10.15,<br />
WeDPT10.17, WeDPT10.18,<br />
WeDPT10.23, WeDPT10.24, WeDT5.1,<br />
WeDT5.2, WeDT5.3, WeDT5.4,<br />
WeDT5.5, WeDT5.6, WeDT5.7,<br />
WeDT5.8, WeDT6.6, WeDT7.2<br />
Motion Control MoAT5.3, MoAT6.6, MoAT7.2,<br />
MoAT7.8, MoAT9.5, MoBPT10.18,<br />
MoBPT10.26, MoBT1.4, MoBT6.7,<br />
MoBT7.6, MoBT8.4, MoBT8.6,<br />
ThAPT10.20, ThAT1.1, ThAT1.8,<br />
ThAT3.6, ThAT3.8, ThAT4.5, ThAT4.8,<br />
ThAT5.3, ThAT5.7, ThAT8.1, ThAT8.6,<br />
ThBPT10.9, ThBPT10.11, ThBPT10.24,<br />
ThBT1.3, ThBT4.8, ThBT5.7, ThCT2.2,<br />
ThCT5.6, ThDPT10.15, ThDT7.4,<br />
ThDT8.3, TuAPT10.1, TuAPT10.18,<br />
TuAPT10.19, TuAPT10.21, TuAT8.4,<br />
TuBPT10.19, TuCT5.7, TuCT8.1,<br />
WeAT4.7, WeAT5.7, WeAT7.7,<br />
WeAT7.8, WeAT8.8, WeBT2.2,<br />
WeBT2.4, WeBT3.3, WeBT6.2,<br />
WeBT7.2, WeBT8.1, WeBT9.1,<br />
WeBT9.8, WeCPT10.4, WeCT4.5,<br />
WeCT5.2, WeCT5.8, WeCT7.7,<br />
WeDPT10.11, WeDT8.2, WeDT8.5<br />
Multifingered Hands ThBT1.4, ThBT1.5, ThCPT10.1,<br />
ThCPT10.2, TuAT6.3, TuBT6.3,<br />
TuBT6.4, TuBT6.5, TuBT6.8, TuBT7.4,<br />
TuBT7.7, TuCPT10.13, TuCPT10.14,<br />
TuCPT10.16, TuCT6.3, TuCT6.4,<br />
TuCT6.5, WeAPT10.13, WeAPT10.14<br />
N<br />
Navigation MoAT2.2, MoAT2.7, MoAT6.8,<br />
ThAPT10.11, ThAPT10.23, ThAT1.1,<br />
ThAT3.1, ThAT3.2, ThAT3.4, ThAT4.4,<br />
ThAT6.5, ThBPT10.7, ThBPT10.10,
ThBPT10.17, ThBT2.4, ThBT6.6,<br />
ThBT8.3, ThCPT10.4, ThCPT10.18,<br />
ThCT8.3, ThCT8.7, ThCT9.1, TuAT2.2,<br />
TuAT2.4, TuAT9.1, TuAT9.2, TuAT9.3,<br />
TuBPT10.4, TuCT1.3, TuCT2.5,<br />
TuCT8.7, WeAPT10.5, WeAT1.8,<br />
WeBT1.6, WeBT2.3, WeBT2.7,<br />
WeBT2.8, WeCPT10.3, WeCT3.3,<br />
WeCT5.2, WeCT6.6, WeDPT10.18,<br />
WeDT5.1, WeDT5.5, WeDT5.8,<br />
WeDT6.8, WeDT9.5<br />
Networked Robots ThAPT10.20, ThBT4.6, ThCPT10.12,<br />
ThCT4.6, ThCT9.1, ThDPT10.12,<br />
ThDT8.4, TuAT2.3, TuAT2.7, WeAT8.1,<br />
WeAT8.4, WeAT8.6, WeBPT10.22,<br />
WeBPT10.24, WeBT8.1, WeBT8.3,<br />
WeCT8.4, WeDPT10.22, WeDT8.2,<br />
WeDT8.5<br />
Networked Teleoperation MoAT4.4, MoAT4.6, MoAT4.7,<br />
MoAT4.8, MoBPT10.10, MoBPT10.12,<br />
MoBT7.3, MoBT7.7, ThAT8.8,<br />
WeCT4.2, WeCT4.3<br />
Neural and Fuzzy Control ThBT7.2, TuAT2.7, WeBT8.7,<br />
WeCT1.1<br />
Neurorobotics TuBT8.7, TuCT5.2, TuCT5.4,<br />
WeAPT10.10<br />
New Actuators for Robotics MoAT9.2, MoAT9.7, MoBT9.2,<br />
MoBT9.4, MoBT9.8, ThAT1.5,<br />
ThAT1.7, ThAT7.4, ThBPT10.2,<br />
ThBPT10.19, ThCT2.6, ThDPT10.6,<br />
Nonholonomic Motion<br />
Planning<br />
TuAPT10.22<br />
ThAPT10.10, ThAPT10.12, ThBT3.5,<br />
ThBT3.8, ThCPT10.8, WeAT5.4,<br />
WeAT8.2, WeBPT10.13, WeBT5.7,<br />
WeBT9.1, WeDT5.1, WeDT5.4,<br />
WeDT5.6<br />
O<br />
Omnidirectional Vision MoBT2.3, MoBT2.5, ThBT2.1,<br />
ThCT8.2, TuAPT10.5<br />
P<br />
Parallel Robots ThAT7.2, ThBT7.5, ThCPT10.20,<br />
ThCT7.5, ThDPT10.20, ThDT4.2,<br />
TuBT7.8, TuCT7.6, WeAPT10.18,<br />
WeAT4.6, WeBPT10.12, WeBT9.7,<br />
WeCT4.7<br />
Parts Feeding and TuBT6.7<br />
Fixturing<br />
Path Planning for<br />
Manipulators<br />
Path Planning for Multiple<br />
Mobile Robots or Agents<br />
ThAT3.8, ThBT3.4, ThCPT10.7,<br />
ThCT1.1, ThCT1.2, ThCT2.5,<br />
ThDPT10.5, TuBT7.8, WeAT5.5,<br />
WeAT5.6, WeBPT10.14, WeBPT10.15,<br />
WeBT5.6, WeCPT10.15, WeDT5.3<br />
MoBT1.6, ThAPT10.12, ThBT9.8,<br />
ThCT9.7, TuAPT10.3, TuCT1.1,<br />
WeAT8.2, WeBT5.4, WeBT5.6,<br />
WeBT8.4, WeCPT10.13, WeCPT10.15,<br />
WeCPT10.22, WeCT1.1, WeCT5.2,<br />
WeCT6.4, WeCT8.1, WeCT8.2,<br />
WeCT8.5, WeCT8.6, WeCT8.7,<br />
WeCT8.8, WeDPT10.16, WeDPT10.23,<br />
WeDPT10.24, WeDT5.6, WeDT5.7<br />
Personal Robots MoAT7.1, ThBT2.6, ThCPT10.6,<br />
ThCT6.3, ThCT6.7, WeAT1.4,<br />
WeBPT10.1, WeBT1.5, WeBT1.8,<br />
Physical Human-Robot<br />
Interaction<br />
WeCPT10.2<br />
MoAT7.8, MoAT8.6, MoAT9.1,<br />
MoBPT10.24, ThAPT10.1, ThAPT10.9,<br />
ThAT7.1, ThCT1.7, ThCT1.8, ThCT7.1,<br />
ThCT7.2, ThCT7.3, ThCT7.4, ThCT7.5,<br />
ThDPT10.19, ThDPT10.20, TuCT5.5,<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–268–<br />
Planning, Scheduling and<br />
Coordination<br />
TuCT5.8, WeAPT10.11, WeAT1.1,<br />
WeAT1.3, WeAT1.4, WeAT1.7,<br />
WeAT4.8, WeBPT10.1, WeBT1.3,<br />
WeBT1.7, WeBT4.2, WeBT9.2,<br />
WeCT1.5, WeCT4.4, WeCT4.7,<br />
WeCT4.8, WeDPT10.2, WeDPT10.10,<br />
WeDT1.3, WeDT1.4, WeDT3.6<br />
MoBT6.2, ThAPT10.14, ThAT3.5,<br />
ThBPT10.8, ThBT3.3, ThBT3.5,<br />
ThCPT10.8, ThCT1.2, ThCT2.1,<br />
ThCT2.8, ThCT4.6, ThCT4.8, ThCT8.6,<br />
ThDPT10.12, ThDPT10.24, TuAT9.2,<br />
TuAT9.3, TuAT9.4, TuAT9.5,<br />
TuBPT10.22, TuBPT10.23, WeAT1.1,<br />
WeBT8.8, WeCT8.6, WeDPT10.24,<br />
WeDT6.5, WeDT8.1, WeDT8.8<br />
Programming Environment TuAT7.3, TuAT7.6, TuBPT10.18,<br />
WeCT7.1<br />
R<br />
Range Sensing MoAT9.8, ThAT2.2, ThAT2.3, ThAT2.5,<br />
ThAT2.6, ThAT2.8, ThAT8.2,<br />
ThBPT10.5, ThBPT10.6, ThBT2.6,<br />
ThBT9.2, ThCPT10.6, ThCT6.5,<br />
ThDPT10.17, TuAT1.8, TuBT2.4,<br />
TuBT7.6, TuCPT10.4, TuCPT10.18,<br />
TuCT9.4, TuCT9.7, WeAPT10.22,<br />
WeAT2.1, WeCT9.5, WeDPT10.26,<br />
Reactive and Sensor-<br />
Based Planning<br />
WeDT7.1<br />
MoBT8.7, ThAPT10.15, ThCT8.5,<br />
ThCT8.7, ThDPT10.23, TuAT9.7,<br />
WeBT1.7, WeBT2.8, WeCT5.1,<br />
WeCT5.5, WeDPT10.14, WeDT5.2,<br />
WeDT6.6, WeDT8.7<br />
Recognition MoBT3.5, ThAT2.1, ThAT2.8, ThCT5.4,<br />
ThCT6.4, ThCT6.5, ThCT6.8,<br />
ThDPT10.13, ThDPT10.16,<br />
ThDPT10.17, TuAPT10.8, TuAT1.1,<br />
TuAT1.2, TuAT1.3, TuAT1.4, TuAT1.5,<br />
TuAT1.6, TuAT1.8, TuBPT10.1,<br />
TuBPT10.2, TuBPT10.3, TuBT1.1,<br />
TuBT1.4, TuBT2.4, TuBT2.7, TuBT9.2,<br />
TuBT9.5, TuCPT10.1, TuCPT10.4,<br />
TuCPT10.23, WeAT2.2, WeAT2.4,<br />
WeAT3.3, WeAT3.8, WeAT9.2,<br />
WeAT9.6, WeBPT10.4, WeBPT10.27,<br />
WeCT2.2, WeCT2.4, WeDPT10.4,<br />
WeDT2.1, WeDT2.3, WeDT2.8,<br />
WeDT3.1, WeDT7.1<br />
Redundant Robots MoAT5.8, MoBT7.5, MoBT7.7,<br />
MoBT8.2, MoBT8.3, ThAT1.2,<br />
ThAT9.8, ThBT1.3, TuAPT10.17,<br />
TuAT8.2, TuAT8.4, TuBPT10.19,<br />
TuCT1.8, WeBT7.7, WeBT7.8,<br />
WeCT7.4, WeCT7.8, WeDPT10.19<br />
Rehabilitation Robotics MoBT7.6, ThAT5.8, ThBT1.1, ThBT7.2,<br />
ThBT7.3, ThBT7.4, ThCPT10.19,<br />
ThCT1.7, ThCT7.1, ThCT7.2, ThCT7.3,<br />
ThCT7.5, ThCT7.6, ThDPT10.20,<br />
ThDPT10.21, TuAPT10.18, TuBT5.2,<br />
TuCT5.1, TuCT5.2, TuCT5.4, TuCT5.5,<br />
TuCT5.6, TuCT5.7, TuCT5.8, TuCT7.1,<br />
WeAPT10.10, WeAPT10.11,<br />
WeAPT10.12, WeBT1.1, WeBT1.4,<br />
Robot Companions and<br />
Social Human-Robot<br />
Interaction<br />
WeCPT10.1, WeCT4.8, WeDT9.2<br />
ThAPT10.17, WeAT1.4, WeAT1.8,<br />
WeBPT10.1, WeBT1.1, WeBT1.2,<br />
WeCT1.4, WeCT1.5, WeCT3.4,<br />
WeDPT10.1, WeDPT10.2,<br />
WeDPT10.7, WeDT1.2, WeDT1.7,<br />
WeDT7.5
Robot Safety MoAT6.4, MoBPT10.16, ThAT8.1,<br />
ThBT3.8, ThBT9.1, TuCT7.8,<br />
WeCT3.1, WeCT3.2, WeCT3.3,<br />
WeCT3.4, WeCT3.5, WeCT5.6,<br />
WeDPT10.7, WeDPT10.8,<br />
Robotic Software,<br />
Middleware and<br />
Programming<br />
Environments<br />
WeDPT10.15<br />
ThAPT10.19, ThCT2.3, ThCT4.4,<br />
ThCT9.8, ThDPT10.10, TuAT7.2,<br />
TuAT7.4, TuAT7.5, TuAT7.6, TuAT7.8,<br />
TuBPT10.16, TuBPT10.17,<br />
TuBPT10.18, TuCT6.2, WeAT2.7,<br />
WeDT8.4<br />
ThBT2.5, ThCPT10.5, TuBT8.3,<br />
TuBT8.5, TuCPT10.20, WeAT9.1<br />
Robotics in Agriculture and<br />
Forestry<br />
Robotics in Construction ThAT8.2, ThCT4.8, WeDT9.7<br />
Robotics in Hazardous MoAT5.6, MoAT6.4, MoAT6.5,<br />
Fields<br />
MoBPT10.15, MoBPT10.16,<br />
MoBPT10.17, ThCT4.7<br />
S<br />
Search and Rescue MoAT5.6, MoBPT10.15, MoBT8.7,<br />
Robots<br />
ThBT8.1, ThBT8.2, ThBT8.3, ThBT8.4,<br />
ThBT8.5, ThCPT10.22, ThCPT10.23,<br />
TuAT8.3, TuCT2.4, TuCT8.5, TuCT8.8,<br />
WeAPT10.4, WeAPT10.20, WeBT2.3,<br />
WeDT1.2<br />
Sensor Fusion MoAT2.3, MoAT3.8, MoAT6.7,<br />
MoAT7.4, MoBPT10.19, MoBT3.7,<br />
MoBT9.6, ThAPT10.6, ThAT2.6,<br />
ThAT4.7, ThAT8.5, ThBPT10.6,<br />
ThBPT10.23, ThBT2.2, ThBT6.3,<br />
ThBT8.6, ThBT9.2, ThCPT10.24,<br />
ThDT3.4, ThDT6.3, ThDT8.1, ThDT8.2,<br />
TuAPT10.24, TuAT2.4, TuAT2.8,<br />
TuAT8.2, TuBPT10.4, TuBT5.7,<br />
TuBT9.2, TuBT9.5, TuCPT10.23,<br />
TuCT2.1, TuCT9.7, WeAT2.5,<br />
WeAT3.7, WeAT6.7, WeAT6.8,<br />
WeBPT10.5, WeBT2.1, WeBT2.2,<br />
WeBT2.3, WeBT2.4, WeBT2.5,<br />
WeBT2.6, WeBT6.6, WeCPT10.4,<br />
WeCPT10.5, WeCPT10.6,<br />
WeCPT10.18, WeCT9.1, WeCT9.2,<br />
WeCT9.3, WeCT9.8, WeDT2.6,<br />
WeDT7.8<br />
Sensor Networks MoAT3.5, MoBPT10.8, MoBT2.6,<br />
ThAPT10.5, ThAPT10.14, ThDT8.4,<br />
TuAPT10.6, TuAT2.8, TuCT9.2,<br />
WeAT6.6, WeAT8.1, WeAT8.6,<br />
WeBPT10.18, WeBPT10.24, WeBT8.2,<br />
WeBT8.5, WeCPT10.23, WeCT5.4,<br />
WeCT6.1, WeCT6.4, WeDPT10.13,<br />
WeDPT10.16, WeDT2.5, WeDT6.5<br />
Service Robots ThAPT10.18, ThBT1.2, ThCT6.6,<br />
ThDPT10.18, TuAT1.1, TuAT1.4,<br />
TuBPT10.1, TuBT7.1, TuBT7.2,<br />
TuBT9.1, WeAT2.7, WeAT9.3,<br />
WeBT1.6, WeBT1.7, WeCPT10.3,<br />
WeDT7.6<br />
SLAM MoAT2.1, MoAT2.3, MoAT2.7,<br />
MoBT3.4, MoBT6.8, ThAPT10.22,<br />
ThAT6.4, ThAT6.5, ThAT6.8,<br />
ThBPT10.16, ThBPT10.17, ThBT6.5,<br />
ThCPT10.17, ThCT1.5, ThDPT10.2,<br />
ThDT7.2, ThPT11.1, TuAPT10.7,<br />
TuAT2.1, TuAT2.2, TuAT2.3, TuAT2.4,<br />
TuAT2.5, TuAT2.6, TuAT2.8,<br />
TuBPT10.4, TuBPT10.5, TuBPT10.6,<br />
TuBT2.1, TuBT2.2, TuBT2.3, TuBT2.4,<br />
TuBT2.5, TuBT2.6, TuBT2.7, TuBT2.8,<br />
TuCPT10.4, TuCPT10.5, TuCPT10.6,<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–269–<br />
TuCT2.4, TuCT2.5, TuCT2.6,<br />
WeAPT10.4, WeAPT10.5, WeAPT10.6,<br />
WeCT9.4, WeCT9.6, WeDPT10.25,<br />
WeDPT10.27, WeDT6.7, WeDT9.4<br />
Smart Actuators MoAT1.8, MoAT9.3, MoAT9.6,<br />
MoAT9.8, MoBPT10.27, MoBT9.3,<br />
MoBT9.7, TuBT8.8, TuCT9.5,<br />
WeAPT10.23<br />
Sonars WeCT1.7, WeCT6.7, WeDT6.7<br />
Space Robotics MoBT6.1, MoBT6.2, MoBT6.3,<br />
MoBT6.4, MoBT6.5, MoBT6.6,<br />
MoBT6.7, MoBT6.8, ThBT6.2,<br />
ThCT1.6, ThDPT10.3, TuAPT10.13,<br />
TuAPT10.14, TuAPT10.15, WeAT4.8,<br />
WeCT3.6, WeDPT10.9<br />
Surveillance Systems ThBT8.2, ThCT8.4, ThDPT10.22,<br />
TuBT1.6, TuBT1.7, TuCPT10.3,<br />
TuCT2.7, WeAT2.3, WeAT9.8,<br />
WeBT8.6, WeCPT10.24, WeCT3.1,<br />
WeCT5.4, WeDPT10.13, WeDT1.3,<br />
WeDT7.3<br />
T<br />
Telerobotics MoAT4.1, MoAT4.3, MoAT4.4,<br />
MoAT4.5, MoAT4.6, MoAT4.7,<br />
MoAT4.8, MoBPT10.10, MoBPT10.11,<br />
MoBPT10.12, MoBT1.5, MoBT7.1,<br />
MoBT7.2, MoBT7.3, MoBT7.4,<br />
MoBT7.5, ThAT8.8, ThBT7.1, ThCT3.7,<br />
ThCT4.7, TuAPT10.2, TuAPT10.16,<br />
TuAPT10.17, TuAT3.3, TuBT5.8,<br />
WeAT3.5, WeAT6.4, WeBPT10.8,<br />
WeBPT10.16, WeBT3.1, WeBT3.4,<br />
WeCPT10.7, WeCT4.3<br />
Tendon/Wire Mechanism MoBT5.7, MoBT9.5, ThBT1.7,<br />
ThCT8.8, TuAPT10.23, TuAT5.2<br />
U<br />
Ubiquitous Robotics ThAPT10.5, WeDT2.5<br />
Underactuated Robots MoBT6.4, ThAT3.7, ThAT7.6, ThAT9.5,<br />
ThBPT10.21, ThBPT10.26, ThBT3.7,<br />
ThCT3.6, ThCT4.3, ThDPT10.9,<br />
ThDT5.2, TuAPT10.13, TuBT6.4,<br />
TuBT6.5, TuCPT10.13, TuCPT10.14,<br />
TuCT7.3, TuCT8.5, WeAPT10.20,<br />
WeBT6.4, WeBT7.2, WeBT7.4,<br />
WeBT7.8, WeCPT10.16, WeCPT10.19<br />
V<br />
Virtual Reality and<br />
Interfaces<br />
MoAT2.6, MoAT4.4, MoBPT10.6,<br />
MoBPT10.10, MoBT3.5, ThAPT10.8,<br />
ThCT1.8, TuAPT10.8, TuBT5.3,<br />
TuBT5.4, TuBT5.8, TuCPT10.10,<br />
WeBT3.4, WeBT4.6, WeBT4.8,<br />
WeCPT10.7, WeCPT10.12, WeDT3.3,<br />
WeDT3.5<br />
Visual Learning ThAT6.3, ThBT6.1, ThCT1.3, ThCT6.4,<br />
ThCT6.8, ThDPT10.16, TuAT1.3,<br />
TuAT1.5, TuAT2.6, TuAT9.8,<br />
TuBPT10.2, TuBPT10.6, TuBT1.7,<br />
TuBT9.2, TuBT9.5, TuCPT10.23,<br />
WeAT2.2, WeAT2.6, WeAT9.2,<br />
WeAT9.7, WeBPT10.6, WeDT1.8<br />
Visual Navigation MoAT2.1, MoAT2.5, MoAT6.7,<br />
MoBPT10.5, MoBT2.5, ThAT6.4,<br />
ThAT8.3, ThAT8.8, ThBPT10.16,<br />
ThBT2.3, ThBT4.7, ThBT6.1, ThBT6.6,<br />
ThBT6.8, ThCPT10.18, ThDT3.3,<br />
ThPT11.1, TuAPT10.5, TuBT2.1,<br />
TuBT2.5, TuBT2.6, TuCPT10.5,<br />
TuCPT10.6, TuCT1.5, TuCT2.1,<br />
TuCT2.2, TuCT2.3, TuCT2.4, TuCT2.5,<br />
WeAPT10.2, WeAPT10.4, WeAPT10.5,
WeAT6.7, WeBT2.6, WeBT6.4,<br />
WeBT6.6, WeCPT10.6, WeCPT10.16,<br />
WeCPT10.18, WeCT9.3<br />
Visual Servoing MoBT6.6, MoBT9.6, ThAT6.1,<br />
ThBT1.5, ThBT6.2, ThCPT10.2,<br />
ThCT8.1, TuAPT10.15, TuAPT10.24,<br />
TuAT6.6, TuBPT10.15, TuBT1.2,<br />
TuCT1.5, WeAPT10.2, WeBT9.1,<br />
WeBT9.2, WeBT9.3, WeBT9.4,<br />
WeBT9.5, WeBT9.6, WeBT9.7,<br />
WeBT9.8, WeCPT10.25, WeCPT10.26,<br />
WeCPT10.27, WeCT2.7<br />
Visual Tracking MoAT1.4, MoAT2.5, MoAT3.4,<br />
MoAT8.3, MoBPT10.1, MoBPT10.5,<br />
MoBPT10.7, MoBT6.6, ThAPT10.3,<br />
ThAPT10.6, ThAPT10.17, ThAT2.4,<br />
ThAT2.7, ThAT4.7, ThAT6.1, ThAT8.3,<br />
ThBPT10.4, ThBT4.7, ThBT6.2,<br />
ThBT6.4, ThBT8.6, ThCPT10.16,<br />
ThCPT10.24, ThCT8.1, ThCT8.4,<br />
ThDPT10.22, ThDT8.1, TuAPT10.15,<br />
TuAT3.3, TuBT1.6, TuBT3.5, TuBT7.4,<br />
TuCPT10.3, TuCPT10.8, TuCPT10.16,<br />
WeAT2.5, WeAT3.4, WeAT9.4,<br />
WeBPT10.5, WeBPT10.7,<br />
<strong>2011</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–270–<br />
Voice, Speech Synthesis<br />
and Recognition<br />
WeBPT10.25, WeBT6.3, WeBT9.3,<br />
WeBT9.5, WeCPT10.26, WeCT2.7,<br />
WeCT2.8, WeCT7.4, WeCT9.7,<br />
WeDPT10.19, WeDT1.6, WeDT2.6,<br />
WeDT2.8, WeDT7.5<br />
MoAT3.1, MoAT3.2, MoAT3.3,<br />
MoAT3.6, MoBPT10.9, MoBT3.1,<br />
MoBT3.2, MoBT3.3, MoBT3.5,<br />
MoBT3.6, MoBT3.7, MoBT3.8,<br />
TuAPT10.8, TuAPT10.9, WeCT1.6,<br />
WeDPT10.3, WeDT1.1
Alan is looking for new colleagues!<br />
Bosch Research and Technology Center<br />
www.boschresearch.com
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Service robots. Human-robot interaction. Edutainment.<br />
The age of service robotics has long since dawned.<br />
In close cooperation with renowned universities and institutes<br />
around the world, KUKA Laboratories is developing new<br />
solutions and setting new technological trends. Already<br />
now, the product portfolio of KUKA Laboratories ranges from<br />
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sector; from motion simulators to technologies for<br />
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for research and education. KUKA Laboratories is shaping the<br />
age of service robotics with its high-tech solutions.<br />
Capture this QR Code with your mobile phone<br />
and learn more about KUKA Laboratories:<br />
www.kuka-labs.com
Redefining the Design<br />
Space for Robotics<br />
• Taurus fieldable dexterous telemanipulation<br />
• Wall climbing robots and new grasping solutions<br />
• KARTO mapping capability<br />
• And more…<br />
About SRI International<br />
Visit Us in the<br />
Exhibit Area<br />
to Learn about<br />
Our Latest<br />
Robotics<br />
Innovations<br />
Silicon Valley-based SRI International, a nonprofit research and development<br />
organization, performs sponsored R&D for governments, businesses, and<br />
foundations. SRI brings its innovations to the marketplace through technology<br />
licensing, new products, and spin-off ventures. Commemorating its 65th<br />
anniversary in <strong>2011</strong>, SRI is known for world-changing innovations in computing<br />
and robotics, health and pharmaceuticals, chemistry and materials, sensing,<br />
energy, education, and national defense.<br />
www.sri.com/robotics
Transform your workforce with ABB robotics.<br />
Jumpstart your business with ABB robotics.<br />
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It’ll only coSt you An ARm.<br />
Announcing the PR2 SE for under $200,000.<br />
As you can see, there’s something quite distinct about this<br />
model. The PR2 SE is a single-armed robot with an updated<br />
sensor suite.<br />
Particularly in light of such new programs as the NSF<br />
National Robotics Initiative, we’ve been encouraged to offer<br />
our personal robot platform at a more affordable price. The<br />
PR2 SE is priced at $285,000 but a 30% discount is offered<br />
to individuals with a proven track record of contributions to<br />
the open source community.<br />
While it doesn’t enable two-handed tasks, the PR2 SE is able<br />
to leverage the work of the PR2 community on navigation,<br />
perception, manipulation and grasping. And if you find<br />
you need a second arm, you can add one later. For more<br />
information, email us at pr2info@willowgarage.com or visit<br />
willowgarage.com.<br />
©<strong>2011</strong> Willow Garage.
Join the 450 prestigious<br />
universities already in the<br />
NAO community !<br />
www.aldebaran-robotics.com<br />
Intuitive Surgical leads in the development and commercialization of medical robotic technology. The da Vinci ® Surgical System<br />
enables surgeons to perform delicate and complex operations minimally-‐invasively, with increased vision, precision, dexterity and<br />
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movements of the instruments working inside the patient’s body. The end result: a breakthrough in surgical capabilities.<br />
Taking surgical precision beyond the limits of the human hand<br />
SOON<br />
OPEN SOURCE<br />
americas@aldebaran-robotics.com
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