確率的推論による連続時間運動計画・制御の近似最適化
Approximately Optimal Continuous-Time Motion Planning and Control via Probabilistic Inference
ガウス過程と確率的推論を用いて、高次非線形な評価指標を持つ連続時間確率システムの運動計画・制御を効率的に近似最適化するPIPCアルゴリズムを提案した。
著者: Mustafa Mukadam, Ching-An Cheng, Xinyan Yan, Byron Boots
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
The problem of optimal motion planing and control is fundamental in robotics. However, this problem is intractable for continuous-time stochastic systems in general and the solution is difficult to approximate if non-instantaneous nonlinear performance indices are present. In this work, we provide an efficient algorithm, PIPC (Probabilistic Inference for Planning and Control), that yields approximately optimal policies with arbitrary higher-order nonlinear performance indices. Using probabilistic inference and a Gaussian process representation of trajectories, PIPC exploits the underlying sparsity of the problem such that its complexity scales linearly in the number of nonlinear factors. We demonstrate the capabilities of our algorithm in a receding horizon setting with multiple systems in simulation.
