周波数整形コストによるロバストなモデル予測制御
Frequency-Aware Model Predictive Control
脚式ロボットの軌道最適化に周波数整形コスト関数を導入し、アクチュエータや接触ダイナミクスの帯域制限に適合した滑らかな動作計画を生成。ANYmalでの実験で追従性能と不整地歩行のロバスト性が向上した。
著者: Ruben Grandia, Farbod Farshidian, Alexey Dosovitskiy, René Ranftl, Marco Hutter
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Transferring solutions found by trajectory optimization to robotic hardware remains a challenging task. When the optimization fully exploits the provided model to perform dynamic tasks, the presence of unmodeled dynamics renders the motion infeasible on the real system. Model errors can be a result of model simplifications, but also naturally arise when deploying the robot in unstructured and nondeterministic environments. Predominantly, compliant contacts and actuator dynamics lead to bandwidth limitations. While classical control methods provide tools to synthesize controllers that are robust to a class of model errors, such a notion is missing in modern trajectory optimization, which is solved in the time domain. We propose frequency-shaped cost functions to achieve robust solutions in the context of optimal control for legged robots. Through simulation and hardware experiments we show that motion plans can be made compatible with bandwidth limits set by actuators and contact dynamics. The smoothness of the model predictive solutions can be continuously tuned without compromising the feasibility of the problem. Experiments with the quadrupedal robot ANYmal, which is driven by highly-compliant series elastic actuators, showed significantly improved tracking performance of the planned motion, torque, and force trajectories and enabled the machine to walk robustly on terrain with unmodeled compliance.
