Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm
Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm
Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm
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<strong>Session</strong> WedCT3 <strong>Pegaso</strong> B <strong>Wednesday</strong>, <strong>October</strong> <strong>10</strong>, <strong>2012</strong>, 11:00–12:30<br />
Sensors, Sensor Networks and Networked Robots<br />
Chair<br />
Co-Chair<br />
11:00–11:15 WedCT3.1<br />
Semi-Autonomous Visual Inspection of Vessels<br />
Assisted by an Unmanned Micro Aerial Vehicle<br />
Francisco Bonnin-Pascual, Emilio Garcia-Fidalgo<br />
and Alberto Ortiz<br />
Department of Mathematics and Computer Science,<br />
University of Balearic Islands, Spain<br />
• Semi-autonomous approach to<br />
the vessel inspection problem<br />
making use of an autonomous<br />
Micro Aerial Vehicle<br />
• The vehicle provides the<br />
surveyors with images of the<br />
areas of the hull to be inspected<br />
• Supplied images can be<br />
processed by corrosion and<br />
crack detection algorithms<br />
based on texture, colour and<br />
morphology<br />
• Experimental results are<br />
provided, which show that the<br />
approach fulfils the application<br />
requirements<br />
11:30–11:45 WedCT3.3<br />
Prioritized Multi-Task Motion Control of<br />
Redundant Robots under Hard Joint Constraints<br />
Fabrizio Flacco Alessandro De Luca<br />
DIAG, Università di Roma “La Sapienza”, Italy<br />
Oussama Khatib<br />
Artificial Intelligence Laboratory, Stanford University, USA<br />
• Extension to multiple prioritized tasks of<br />
our recent SNS (Saturation in the Null<br />
Space) algorithm for acceleration-level<br />
control of redundant robots<br />
• Hard bounds on joint range, velocity, and<br />
acceleration/torque are always satisfied<br />
• A multi-task least scaling strategy is<br />
integrated in the SNS, when some of the<br />
original tasks turn out to be unfeasible<br />
• Efficient preemptive approach: A task of<br />
higher priority uses at best all the robot<br />
capabilities needed; lower priority tasks<br />
exploit the residual capabilities, without<br />
interfering with higher priority tasks<br />
A KUKA LWR robot cycles through<br />
Cartesian points, with self-motion<br />
damping as the secondary task and<br />
while satisfying all joint constraints<br />
12:00–12:15 WedCT3.5<br />
Entropy-aware Cluster-based Object Tracking<br />
for Camera Wireless Sensor Networks<br />
Alberto De San Bernabé, Jose Ramiro Martinez-de Dios and<br />
Anibal Ollero<br />
Robotics, Vision and Control Group, University of Seville, Spain<br />
• Entropy-based mechanisms for energy<br />
efficiency and robustness to transmission<br />
errors in object tracking systems are<br />
presented.<br />
• Activation/deactivation of camera-nodes<br />
using an active sensing method based on<br />
cost-gain analyses to reduce energy<br />
consumption.<br />
• Method that dynamically selects the<br />
cluster head using entropies and<br />
transmission error rates.<br />
• The proposed methods have been tested<br />
in experiments carried out in the CONET<br />
Robot-WSN Testbed (http://conet.us.es)<br />
Picture of one experiment in the<br />
CONET Robot-WSN Testbed<br />
11:15–11:30 WedCT3.2<br />
Web Mining Driven Object Locality Knowledge<br />
Acquisition for Efficient Robot Behavior<br />
Kai Zhou, Michael Zillich and Markus Vincze<br />
Automation and Control Institute (ACIN), Vienna University of Technology, Austria<br />
Hendrik Zender<br />
Language Technology Lab, German Research Center for Artificial Intelligence (DFKI), Germany<br />
• Probabilistic conceptual knowledge that<br />
represents the relations of object and its<br />
situated environments is obtained online.<br />
• More accurate quantification is achieved<br />
by fusing search engine query data and<br />
professional robotic database.<br />
• Diverse localities including various<br />
supporting surfaces and room categories<br />
have been investigated to find the<br />
dominant location of object.<br />
• Multiple objects search task has been<br />
performed using the discovered<br />
probabilistic knowledge.<br />
• Plentiful experimental results (200+<br />
objects/furniture, 3 surfaces, 7 rooms)<br />
validate the intuition of discovering object<br />
locality knowledge online.<br />
<strong>2012</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–141–<br />
Example scenario and object<br />
search task<br />
11:45–12:00 WedCT3.4<br />
Optical-Inertial Tracking with Active Markers and<br />
Changing Visibility<br />
Florian Steidle and Andreas Tobergte<br />
and Gerd Hirzinger<br />
Robotics and Mechatronics Center,<br />
German Aerospace Center, Germany<br />
• Extended Kalman Filter to fuse low latency measurements of inertial<br />
measurement unit with 2D marker measurements<br />
• Markers, identificated by individual activation, are subsequently locally<br />
tracked in the image pane<br />
• Robust with respect<br />
to temporary marker<br />
occlusions<br />
• Real time<br />
implementation and<br />
verification with<br />
experiments<br />
12:15–12:30 WedCT3.6<br />
Intelligent Sensor-Scheduling for<br />
Multi-Kinect-Tracking<br />
Florian Faion, Simon Friedberger, Antonio Zea,<br />
and Uwe D. Hanebeck<br />
Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT)<br />
• Scenario: target tracking with a Multi-<br />
Kinect-sensor-network<br />
• Challenge: high bandwidth, computational<br />
cost, interference<br />
• Idea: measuring the target exclusively with<br />
best available sensor<br />
• Contribution: uncertainty minimizing<br />
scheduling algorithm, stochastic Kinect<br />
sensor model, Kinect IR-projector<br />
modification