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

Session WedGT10 Lince Wednesday, October 10, 2012, 17:30–18:30
Safety, Failure Handling and Recovery II
Chair Erwin Prassler, Bonn-Rhein-Sieg Univ. of Applied Sciences
Co-Chair
17:30–17:45 WedGT10.1
Dual back-stepping observer to anticipate the rollover
risk in under/over-steering situations. Application to
ATVs in off-road context.
Mathieu Richier, Roland Lenain and Christophe Debain
IRSTEA, France
Benoit Thuilot,
Institut Pascal, France
• Rollover prevention thanks to the
computation of a stability metric and a
predictive algorithm
• Observers estimating on-line the grip
conditions and the slope: application to
off-road conditions
• Limitation to a low cost sensing
equipment: allowing a practical
applicability
MF400H, Massey
Fergusson quad bike used
for experiments.
18:00–18:15 WedGT10.3
Psychological Experiments on Avoidance Action
Characteristics for Estimating Avoidability
of Harm to Eyes from Robots
Takamasa Hattori 1 , Yoji Yamada 1 , Shuji Mori 2 ,
Shogo Okamoto 1 , and Susumu Hara 1
1 Graduate School of Engineering, Nagoya University, Japan
2 Faculty of Information Science and Electrical Engineering,
Kyushu University, Japan
• Psychological experiments are conducted
to investigate harm-avoidance action
characteristics in humans in close contact
with robotic devices.
• A situation is created in which the sharp
end-effector tip of a robot suddenly
approaches the eyes of a facing participant.
• The results suggest that the reaction time
on avoidance actions does not depend on
the type of work being performed but on the
initial distance between the human’s eyes
and the approaching object.
Participant
Motion capture system
Bearing
End effector
Robot
17:45–18:00 WedGT10.2
Towards Learning of Safety Knowledge from
Human Demonstrations
P. Ertle 1 , M. Tokic 2,3 , R. Cubek 2 , H. Voos 4 , D. Söffker 1
1 University of Duisburg-Essen
2 University of Applied Sciences of Ravensburg-Weingarten
3 University of Ulm
4 University of Luxembourg
• Future autonomous service robots shall
operate in open and complex
environments which implies complications
ensuring safe operation.
• Hazardous environmental object
interactions can occur.
• A safety procedure is described, learning
safety knowledge from human
demonstration .
• Several supervised learning techniques
are evaluated.
• Results indicate that Decision Trees allow
interesting opportunities.
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
–192–
Learned decision tree for safely
handling an iron in an ironing task
18:15–18:30 WedGT10.4
A truly safe robot has to know what injury it may
cause
S. Haddadin, S. Haddadin, A. Khoury, T. Rokahr, S. Parusel,
R. Burgkart, A. Bicchi, and A. Albu-Schäffer
Robotics and Mechatronics Center, DLR
• Introduce soft-tissue injury classification
to robotics
• Generate missing medical data
(mass,velocity,curvature)� injury via
drop-testing
• Associate primitive based decomposition
of robot structure with drop-testing
results
• Take into account robot dynamics and
curvature for biomechanically safe
velocity control
• Effect: even in case of unwanted collision
with potentially dangerous curvatures the
robot is not able to cause any harm
• Relevance not only to robotics but also to
medicine and biomechanics