论文标题
强大的脚步计划和LQR控制，用于动态四倍的运动
Robust Footstep Planning and LQR Control for Dynamic Quadrupedal Locomotion
论文作者
论文摘要
在本文中，我们旨在通过两个方面提高动态四倍运动的鲁棒性：1）快速模型预测的立足计划，以及2）将LQR应用于投影的反向动态控制以进行稳健运动跟踪。在我们提出的计划和控制框架中，考虑到当前的机器人状态，LQR控制器生成了最佳的反馈收益，以进行运动跟踪，以400 Hz更新了立足计划。具有非零离线元件的LQR最佳增益矩阵利用动力学的耦合来补偿系统不足。同时，投影的反向动态控制可以补充LQR以满足不平等约束。除了这些贡献外，我们还显示了控制框架对未建模的自适应脚的鲁棒性。在四足动物上进行的实验证明了在外部干扰和环境不确定性的情况下，提出的强大动态运动方法的有效性。
In this paper, we aim to improve the robustness of dynamic quadrupedal locomotion through two aspects: 1) fast model predictive foothold planning, and 2) applying LQR to projected inverse dynamic control for robust motion tracking. In our proposed planning and control framework, foothold plans are updated at 400 Hz considering the current robot state and an LQR controller generates optimal feedback gains for motion tracking. The LQR optimal gain matrix with non-zero off-diagonal elements leverages the coupling of dynamics to compensate for system underactuation. Meanwhile, the projected inverse dynamic control complements the LQR to satisfy inequality constraints. In addition to these contributions, we show robustness of our control framework to unmodeled adaptive feet. Experiments on the quadruped ANYmal demonstrate the effectiveness of the proposed method for robust dynamic locomotion given external disturbances and environmental uncertainties.
