Fast-reactive probabilistic motion planning for high-dimensional robots

• URL: http://arxiv.org/abs/2012.02118v1
• Date: Thu, 3 Dec 2020 17:51:07 GMT
• Title: Fast-reactive probabilistic motion planning for high-dimensional robots
• Authors: Siyu Dai, Andreas Hofmann and Brian C. Williams
• Abstract summary: p-Chekov is a fast-reactive motion planning system that can provide safety guarantees for high-dimensional robots suffering from process noises and observation noises. Comprehensive theoretical and empirical analysis shows that p-Chekov can effectively satisfy user-specified chance constraints over collision risk in practical robotic manipulation tasks.
• Score: 15.082715993594121
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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