通过分层模型预测控制调节步态频率以实现姿态调节的仿人机器人推力恢复
Adapting Gait Frequency for Posture-regulating Humanoid Push-recovery via Hierarchical Model Predictive Control
AI总结：
本文提出一种基于分层MPC的仿人机器人推力恢复方法，通过适应步态频率和姿态感知规划器，在未知干扰下快速恢复并调节身体姿态，仿真显示最大可恢复冲量平均提升131%，硬件实验中恢复步态时机提前125毫秒。
摘要：
当前的仿人机器人推力恢复策略通常使用全身运动，但往往忽略了姿态调节。例如，在操作任务中，上半身可能需要保持直立并具有最小的恢复位移。本文提出了一种新方法，通过定制恢复步态策略来增强未知干扰下的仿人机器人推力恢复性能并调节身体姿态。我们提出了一种基于分层MPC的方案，该方案分析并检测预测窗口中的不稳定性，并通过适应步态频率快速恢复。我们的方法集成了高层非线性MPC、姿态感知步态频率适应规划器和低层凸运动MPC。规划器预测质心（CoM）状态轨迹，该轨迹可用于评估潜在不稳定性和姿态偏差的前兆。在仿真中，我们展示了与基线方法相比，最大可恢复冲量平均提高了131%。在硬件实验中，观察到所提出的方法在恢复步态时机/反射上提前了125毫秒。我们还展示了改进的推力恢复性能和最小化的身体姿态变化在0.2弧度以下。
英文摘要：
Current humanoid push-recovery strategies often use whole-body motion, yet they tend to overlook posture regulation. For instance, in manipulation tasks, the upper body may need to stay upright and have minimal recovery displacement. This paper introduces a novel approach to enhancing humanoid push-recovery performance under unknown disturbances and regulating body posture by tailoring the recovery stepping strategy. We propose a hierarchical-MPC-based scheme that analyzes and detects instability in the prediction window and quickly recovers through adapting gait frequency. Our approach integrates a high-level nonlinear MPC, a posture-aware gait frequency adaptation planner, and a low-level convex locomotion MPC. The planners predict the center of mass (CoM) state trajectories that can be assessed for precursors of potential instability and posture deviation. In simulation, we demonstrate improved maximum recoverable impulse by 131% on average compared with baseline approaches. In hardware experiments, a 125 ms advancement in recovery stepping timing/reflex has been observed with the proposed approach. We also demonstrate improved push-recovery performance and minimized body attitude change under 0.2 rad.
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。
