用于鲁棒四足机器人运动的双MPC步足规划
Dual-MPC Footstep Planning for Robust Quadruped Locomotion
AI总结：
针对四足机器人运动中角动量仅靠地面反作用力控制导致的问题，提出双MPC步足规划策略，通过互反馈耦合步足规划与GRF优化，减少振荡并延长支撑和摆动相，提升鲁棒性。
摘要：
本文提出一种基于模型预测控制（MPC）的步足规划策略，通过优化步足放置实现对非期望身体旋转的鲁棒姿态调节。基于模型的运动方法通常采用启发式方法或基于线性倒立摆模型的规划，这些方法在步足规划中考虑线速度但忽略角速度，导致角动量仅通过地面反作用力（GRF）处理。考虑角速度的MPC步足规划将角动量控制问题重构为双输入方法，协调GRF与步足放置而非仅优化GRF，从而提升跟踪性能。步足规划器与GRF MPC通过互反馈回路耦合，两者利用对方的解迭代更新步足与GRF。最优解的使用减少了身体振荡，实现了更长的支撑相和摆动相。该方法在四足机器人上得到验证，在多种地形中展现出鲁棒运动，振荡减少且支撑相和摆动相更长。
英文摘要：
In this paper, we propose a footstep planning strategy based on model predictive control (MPC) that enables robust regulation of body orientation against undesired body rotations by optimizing footstep placement. Model-based locomotion approaches typically adopt heuristic methods or planning based on the linear inverted pendulum model. These methods account for linear velocity in footstep planning, while excluding angular velocity, which leads to angular momentum being handled exclusively via ground reaction force (GRF). Footstep planning based on MPC that takes angular velocity into account recasts the angular momentum control problem as a dual-input approach that coordinates GRFs and footstep placement, instead of optimizing GRFs alone, thereby improving tracking performance. A mutual-feedback loop couples the footstep planner and the GRF MPC, with each using the other's solution to iteratively update footsteps and GRFs. The use of optimal solutions reduces body oscillation and enables extended stance and swing phases. The method is validated on a quadruped robot, demonstrating robust locomotion with reduced oscillations, longer stance and swing phases across various terrains.
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