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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