高次元ロボットのための高速反応型確率的動作計画
Fast-reactive probabilistic motion planning for high-dimensional robots
高次元ヒューマノイドロボットの動作計画において、プロセスノイズや観測ノイズ下でも衝突リスクの確率的保証を満たしつつ高速に反応できるp-Chekovを提案した論文。
著者: Siyu Dai, Andreas Hofmann, Brian C. Williams
分類: cs.RO, cs.LG
原文アブストラクト
Many real-world robotic operations that involve high-dimensional humanoid robots require fast-reaction to plan disturbances and probabilistic guarantees over collision risks, whereas most probabilistic motion planning approaches developed for car-like robots can not be directly applied to high-dimensional robots. In this paper, we present probabilistic Chekov (p-Chekov), a fast-reactive motion planning system that can provide safety guarantees for high-dimensional robots suffering from process noises and observation noises. Leveraging recent advances in machine learning as well as our previous work in deterministic motion planning that integrated trajectory optimization into a sparse roadmap framework, p-Chekov demonstrates its superiority in terms of collision avoidance ability and planning speed in high-dimensional robotic motion planning tasks in complex environments without the convexification of obstacles. Comprehensive theoretical and empirical analysis provided in this paper shows that p-Chekov can effectively satisfy user-specified chance constraints over collision risk in practical robotic manipulation tasks.
