TY - GEN
T1 - Unified Multi-Contact Fall Mitigation Planning for Humanoids via Contact Transition Tree Optimization
AU - Wang, Shihao
AU - Hauser, Kris
N1 - Funding Information:
*This work was supported by NSF grant NRI #1527826 1Shihao Wang is with the Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708, USA shihao.wang@duke.edu 2Kris Hauser is with the Departments of Electrical and Computer Engineering and Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708, USA kris.hauser@duke.edu
Funding Information:
This work was supported by NSF grant NRI #1527826
Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/23
Y1 - 2019/1/23
N2 - This paper presents a multi-contact approach to generalized humanoid fall mitigation planning that unifies inertial shaping, protective stepping, and hand contact strategies. The planner optimizes both the contact sequence and the robot state trajectories. A high-level tree search is conducted to iteratively grow a contact transition tree. At each edge of the tree, trajectory optimization is used to calculate robot stabilization trajectories that produce the desired contact transition while minimizing kinetic energy. Also, at each node of the tree, the optimizer attempts to find a self-motion (inertial shaping movement)to eliminate kinetic energy. This paper also presents an efficient and effective method to generate initial seeds to facilitate trajectory optimization. Experiments demonstrate show that our proposed algorithm can generate complex stabilization strategies for a simulated planar robot under varying initial pushes and environment shapes.
AB - This paper presents a multi-contact approach to generalized humanoid fall mitigation planning that unifies inertial shaping, protective stepping, and hand contact strategies. The planner optimizes both the contact sequence and the robot state trajectories. A high-level tree search is conducted to iteratively grow a contact transition tree. At each edge of the tree, trajectory optimization is used to calculate robot stabilization trajectories that produce the desired contact transition while minimizing kinetic energy. Also, at each node of the tree, the optimizer attempts to find a self-motion (inertial shaping movement)to eliminate kinetic energy. This paper also presents an efficient and effective method to generate initial seeds to facilitate trajectory optimization. Experiments demonstrate show that our proposed algorithm can generate complex stabilization strategies for a simulated planar robot under varying initial pushes and environment shapes.
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U2 - 10.1109/HUMANOIDS.2018.8625018
DO - 10.1109/HUMANOIDS.2018.8625018
M3 - Conference contribution
AN - SCOPUS:85062270061
T3 - IEEE-RAS International Conference on Humanoid Robots
SP - 33
EP - 39
BT - 2018 IEEE-RAS 18th International Conference on Humanoid Robots, Humanoids 2018
PB - IEEE Computer Society
T2 - 18th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2018
Y2 - 6 November 2018 through 9 November 2018
ER -