剛体力学の自動微分による最適制御と状態推定
Automatic Differentiation of Rigid Body Dynamics for Optimal Control and Estimation
ロボットの剛体力学モデルに自動微分を適用し、高効率な微分コードを自動生成する手法を提案。四足歩行ロボットの軌道最適化と6自由度アームの動的再計画実験で有効性を示した。
著者: Markus Giftthaler, Michael Neunert, Markus Stäuble, Marco Frigerio, Claudio Semini, Jonas Buchli
分類: cs.RO
原文アブストラクト
Many algorithms for control, optimization and estimation in robotics depend on derivatives of the underlying system dynamics, e.g. to compute linearizations, sensitivities or gradient directions. However, we show that when dealing with Rigid Body Dynamics, these derivatives are difficult to derive analytically and to implement efficiently. To overcome this issue, we extend the modelling tool `RobCoGen' to be compatible with Automatic Differentiation. Additionally, we propose how to automatically obtain the derivatives and generate highly efficient source code. We highlight the flexibility and performance of the approach in two application examples. First, we show a Trajectory Optimization example for the quadrupedal robot HyQ, which employs auto-differentiation on the dynamics including a contact model. Second, we present a hardware experiment in which a 6 DoF robotic arm avoids a randomly moving obstacle in a go-to task by fast, dynamic replanning.
