Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm

Session WedFVT2 Fenix 2 Wednesday, October 10, 2012, 16:15–17:30
Telerobotics & Brain-Machine Interfaces
Chair Susumu Tachi, Keio Univ.
Co-Chair
16:15–16:30 WedFVT2.1
A Collaborative Control System for
Telepresence Robots
Douglas G. Macharet
VeRLab, DCC, UFMG, Brazil
Dinei Florêncio
Microsoft Research, USA
• A potential field based framework to
facilitate the control of telepresence
robots
• Adjustable level of autonomy of the
robot, giving it full control on mid-range
navigation
• Users had significantly fewer hits (none
in most cases) and took less time to
complete a given task
• Most of the users agreed with the
possible directions pointed out by the
methodology
16:45–17:00 WedFVT2.3
Armrest Joystick
-Mechanism Design and Basic Experiments-
Hiroaki Ishida Tetsuo Hagiwara
Koji Ueda and Shigeo Hirose
Department of Mechanical and Aerospace Engineering,
Tokyo Institute of Technology, Japan
• We propose a robot arm’s controller,
“Armrest Joystick,” with high portability
and operability.
• The Armrest Joystick can operate 3DOF
position, 3DOF posture and a gripper
with force feedback.
• We study a design of the Armrest
Joystick in this paper.
• We conducted some experiments to
verify operability.
Armrest Joystick
17:15–17:20 WedFVT2.5
Towards Robotic Re-Embodiment using
a Brain-and-Body-Computer Interface
Nikolas Martens, Robert Jenke, Mohammad Abu-Alqumsan,
Angelika Peer, and Martin Buss
Institute of Automatic Control Engineering, TUM, Germany
Christoph Kapeller, Christoph Hintermüller, Christoph Guger
Guger Technologies, Austria
• 3 basic scenarios of a BBCI controlled
robot avatar: pick&place, door-opening,
and social interaction
• Development of task-adapted interfaces
for P300 and SSVEP paradigms
• High-level intentions determine what the
robot should execute
• Low-level intentions describe how those
commands are executed
Task-adapted BCI for (a) pickand-place
and (b) door-opening
16:30–16:45 WedFVT2.2
Design of TELESAR V for Transferring Bodily
Consciousness in Telexistence
Charith Lasantha Fernando, Masahiro Furukawa, Tadatoshi
Kurogi, Sho Kamuro, Katsunari Sato, Kouta Minamizawa and
Susumu Tachi
Graduate School of Media Design,
Keio University. Japan
• A 52 DOF robot for performing
telexistence operations.
• Un-grounded Master cockpit.
• Independent Spinal, Head, Arm, Hand
movements
• Wide Angle HD Head Mounted Display.
• Fingertip Force (Shearing, Vertical)
Sensation
• Fingertip Thermal Sensation
• Extend the bodily border up to robot
17:00–17:15 WedFVT2.4
Networked Teleoperation with Non-Passive
Environment: Application to Tele-Rehabilitation
S. Farokh Atashzar, Ilia G. Polushin
Dept. of Electrical and Computer Eng., University of Western Ontario, Canada
Rajni V. Patel
Dept. Electrical and Computer Engineering and Dept. of Surgery , University of
Western Ontario , Canada
• The problem of design of a master-slave
tele-rehabilitation system for
assistive/resistive therapy is addressed.
• During assistive therapy, the therapist
supplies the power to the teleoperator
system thus behaving as an active (nonpassive)
network.
• Dissipation of the power generated by the
therapist would defeat the purpose of the
assistive therapy.
• A small-gain approach is designed to
analyze/maintain the stability in both
assistive and resistive mode.
Top: Velocity of the patient’s hand vs.
therapist’s hand
Bottom: Energy generated by the therapist
17:20–17:25 WedFVT2.6
Rock-Paper-Scissors Prediction Experiments
using Muscle Activations
Giho Jang and Youngjin Choi*
Electronic Systems Engineering, Hanyang University, South Korea
Zhihua Qu
EECS, University of Central Florida, USA
• Initial experimental results for
hand posture prediciton are
presented in the video.
• Property: initial burst part of
muscle activation (EMG) is
prior to the oneset of acutal
movement by dozens to
hundreds milliseconds.
• Using this property, the proposed method makes the ternary choice
prediction among rock-paper- scissors as soon as 10% motion variation
of any finger is detected.
• It is shown experimentally that the success rate of the proposed
prediction method is over 95%.
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
–180–