脚式ロボットの重心運動生成のための非線形確率軌道最適化
Nonlinear Stochastic Trajectory Optimization for Centroidal Momentum Motion Generation of Legged Robots
モデル動力学の加法的不確かさと接触位置のパラメトリック不確かさを考慮し、確率的軌道最適化を用いて脚式ロボットの頑健な重心運動軌道を生成する手法を提案。四足ロボットのシミュレーションで足滑りを低減し、決定論的計画より優れた性能を示した。
著者: Ahmad Gazar, Majid Khadiv, Sébastien Kleff, Andrea Del Prete, Ludovic Righetti
分類: cs.RO
原文アブストラクト
Generation of robust trajectories for legged robots remains a challenging task due to the underlying nonlinear, hybrid and intrinsically unstable dynamics which needs to be stabilized through limited contact forces. Furthermore, disturbances arising from unmodelled contact interactions with the environment and model mismatches can hinder the quality of the planned trajectories leading to unsafe motions. In this work, we propose to use stochastic trajectory optimization for generating robust centroidal momentum trajectories to account for additive uncertainties on the model dynamics and parametric uncertainties on contact locations. Through an alternation between the robust centroidal and whole-body trajectory optimizations, we generate robust momentum trajectories while being consistent with the whole-body dynamics. We perform an extensive set of simulations subject to different uncertainties on a quadruped robot showing that our stochastic trajectory optimization problem reduces the amount of foot slippage for different gaits while achieving better performance over deterministic planning.
