通过双控优化步态与反作用力，提升四足机器人在复杂地形下的稳定性。
Dual-MPC Footstep Planning for Robust Quadruped Locomotion
- 基于双重MPC模型，同步优化步态位置与地面反作用力。
- 实测显示身体振荡减少，支撑相与摆动相时长显著延长。
- 适合需要高稳定性的野外机器人运动控制场景。
本文提出一种基于模型预测控制（MPC）的步态规划策略，通过优化步态位置实现对机体姿态的鲁棒调节，有效抑制非期望的机体旋转。传统基于模型的步态方法通常采用启发式规则或线性倒立摆模型进行规划，仅考虑线速度而忽略角速度，导致角动量仅通过地面反作用力（GRF）处理。本方法将角速度纳入步态规划，将角动量控制重构为双输入协同机制，同时优化GRF与步态位置，而非仅优化GRF，从而提升跟踪性能。脚步规划器与GRF-MPC之间建立互反馈回路，彼此使用对方解迭代更新步态与力，利用最优解减少机体振荡，支持更长的支撑相与摆动相。该方法在四足机器人上验证，在多种地形下均表现出鲁棒运动能力，振荡显著降低，步态周期更长。
原文摘要 · Abstract (English)
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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