双足机器人的鲁棒推力恢复：利用多域混合系统与降阶模型预测控制
Robust Push Recovery on Bipedal Robots: Leveraging Multi-Domain Hybrid Systems with Reduced-Order Model Predictive Control
AI总结：
本文提出一种将双足运动的混合系统模型与降阶模型预测控制相统一的框架，用于实现双足机器人运动中的鲁棒推力恢复，在仿真中验证了其在动态环境下的稳定性和实时可行性。
摘要：
本文提出了一种新颖的控制框架，用于在双足机器人运动过程中实现鲁棒的推力恢复。核心贡献是将运动的混合系统模型与一个降阶模型预测控制器相统一，该控制器决定：落脚点、步态时序和踝关节控制。所提出的降阶模型是一个带有零力矩点坐标的增广线性倒立摆模型；该模型被集成到一个模型预测控制框架中，以在外部干扰下实现鲁棒稳定。通过显式利用运动的混合动力学，我们的方法显著提高了在不同行走高度、速度和步态持续时间下的稳定性和鲁棒性，并且对平足和更复杂的多域后跟到脚趾行走模式均有效。该框架在 Cassie（一个3D欠驱动机器人）上进行了高保真仿真验证，展示了实时可行性并大幅提高了稳定性。结果证明了所提方法在动态环境中的鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
