Fast-reactive probabilistic motion planning for high-dimensional robots
This work addresses the problem of fast-reactive probabilistic motion planning for high-dimensional robots, which is crucial for real-world robotic operations, offering improved safety guarantees.
This paper introduces probabilistic Chekov (p-Chekov), a motion planning system for high-dimensional robots that provides fast reaction to disturbances and probabilistic guarantees on collision risks. It demonstrates superior collision avoidance and planning speed in complex environments without obstacle convexification, effectively satisfying user-specified chance constraints.
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.