信号時相論理に基づくモデル予測制御による外乱に頑健な二足歩行
Signal Temporal Logic-Guided Model Predictive Control for Robust Bipedal Locomotion Resilient to Runtime External Perturbations
信号時相論理(STL)をモデル予測制御のコスト関数に組み込み、実行時に加わる外乱から回復する二足歩行の軌道最適化フレームワークを提案し、脚交差動作の生成で安定性を高めた。
著者: Zhaoyuan Gu, Rongming Guo, William Yates, Yipu Chen, Ye Zhao
分類: cs.RO
原文アブストラクト
This study investigates formal-method-based trajectory optimization (TO) for bipedal locomotion, focusing on scenarios where the robot encounters external perturbations at unforeseen times. Our key research question centers around the assurance of task specification correctness and the maximization of specification robustness for a bipedal robot in the presence of external perturbations. Our contribution includes the design of an optimization-based task and motion planning framework that generates optimal control sequences with formal guarantees of external perturbation recovery. As a core component of the framework, a model predictive controller (MPC) encodes signal temporal logic (STL)-based task specifications as a cost function. In particular, we investigate challenging scenarios where the robot is subjected to lateral perturbations that increase the risk of failure due to leg self-collision. To address this, we synthesize agile and safe crossed-leg maneuvers to enhance locomotion stability. This work marks the first study to incorporate formal guarantees offered by STL into a TO for perturbation recovery of bipedal locomotion. We demonstrate the efficacy of the framework via perturbation experiments in simulations.
