二足歩行ロボットの押され復帰のための反応的意思決定とロバスト運動計画
Reactive Locomotion Decision-Making and Robust Motion Planning for Real-Time Perturbation Recovery
二足歩行ロボットが外力で押された際の復帰動作を、時相論理に基づく反応的意思決定と全身力学・脚衝突制約を考慮した軌道最適化をビヘイビアツリーで統合して生成するフレームワークを提案した。
著者: Zhaoyuan Gu, Nathan Boyd, Ye Zhao
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
In this paper, we examine the problem of push recovery for bipedal robot locomotion and present a reactive decision-making and robust planning framework for locomotion resilient to external perturbations. Rejecting perturbations is an essential capability of bipedal robots and has been widely studied in the locomotion literature. However, adversarial disturbances and aggressive turning can lead to negative lateral step width (i.e., crossed-leg scenarios) with unstable motions and self-collision risks. These motion planning problems are computationally difficult and have not been explored under a hierarchically integrated task and motion planning method. We explore a planning and decision-making framework that closely ties linear-temporal-logic-based reactive synthesis with trajectory optimization incorporating the robot's full-body dynamics, kinematics, and leg collision avoidance constraints. Between the high-level discrete symbolic decision-making and the low-level continuous motion planning, behavior trees serve as a reactive interface to handle perturbations occurring at any time of the locomotion process. Our experimental results show the efficacy of our method in generating resilient recovery behaviors in response to diverse perturbations from any direction with bounded magnitudes.
