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Robust Footstep Planning and LQR Control for Dynamic Quadrupedal Locomotion

机译:动态四轮车运动的强大脚步规划和LQR控制

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In this letter, 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.
机译:在这封信中,我们的目标是通过两个方面提高动态四边形运动的鲁棒性:1)快速模型预测立足规划,以及2)将LQR应用于强大的运动跟踪的逆动态控制。在我们提出的规划和控制框架中,考虑到当前机器人状态,LQR控制器产生运动跟踪的最佳反馈增益,立足计划在400 Hz处更新。具有非零性对角线元件的LQR最佳增益矩阵利用动力学的耦合来补偿系统欠扰。同时,预计的逆动态控制补充了LQR以满足不平等约束。除了这些贡献外,我们还向未刻度的自适应脚显示了我们控制框架的稳健性。 Quadruped的实验Anymal展示了所提出的方法对鲁棒动态运动的有效性,因为外部干扰和环境不确定性。

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