離散時間バリア関数によるロバスト歩行軌道の合成と多接触外骨格ロコモーションへの応用
Synthesizing Robust Walking Gaits via Discrete-Time Barrier Functions with Application to Multi-Contact Exoskeleton Locomotion
離散時間バリア関数とサンプリングで歩行のロバスト性を評価・最大化し、外骨格Atalanteで平地歩行と多接触歩行を実現した。
著者: Maegan Tucker, Kejun Li, Aaron D. Ames
分類: cs.RO
原文アブストラクト
Successfully achieving bipedal locomotion remains challenging due to real-world factors such as model uncertainty, random disturbances, and imperfect state estimation. In this work, we propose a novel metric for locomotive robustness -- the estimated size of the hybrid forward invariant set associated with the step-to-step dynamics. Here, the forward invariant set can be loosely interpreted as the region of attraction for the discrete-time dynamics. We illustrate the use of this metric towards synthesizing nominal walking gaits using a simulation-in-the-loop learning approach. Further, we leverage discrete-time barrier functions and a sampling-based approach to approximate sets that are maximally forward invariant. Lastly, we experimentally demonstrate that this approach results in successful locomotion for both flat-foot walking and multi-contact walking on the Atalante lower-body exoskeleton.
