二足歩行ロボットの堅牢な押し戻し回復：多領域ハイブリッド系と低次元モデル予測制御の融合
Robust Push Recovery on Bipedal Robots: Leveraging Multi-Domain Hybrid Systems with Reduced-Order Model Predictive Control
歩行中の二足ロボットが外力を受けても転倒せず復帰できるよう、ハイブリッド系モデルと低次元モデル予測制御を組み合わせ、足配置・歩行タイミング・足首制御を決定する手法を提案し、Cassieのシミュレーションで有効性を示した。
著者: Min Dai, Aaron D. Ames
分類: cs.RO
原文アブストラクト
In this paper, we present a novel control framework to achieve robust push recovery on bipedal robots while locomoting. The key contribution is the unification of hybrid system models of locomotion with a reduced-order model predictive controller determining: foot placement, step timing, and ankle control. The proposed reduced-order model is an augmented Linear Inverted Pendulum model with zero moment point coordinates; this is integrated within a model predictive control framework for robust stabilization under external disturbances. By explicitly leveraging the hybrid dynamics of locomotion, our approach significantly improves stability and robustness across varying walking heights, speeds, step durations, and is effective for both flat-footed and more complex multi-domain heel-to-toe walking patterns. The framework is validated with high-fidelity simulation on Cassie, a 3D underactuated robot, showcasing real-time feasibility and substantially improved stability. The results demonstrate the robustness of the proposed method in dynamic environments.
