Fast-reactive probabilistic motion planning for high-dimensional robots

TL;DR

p-Chekov is a fast, probabilistic motion planning system designed for high-dimensional robots, providing safety guarantees and efficient collision avoidance in complex environments despite process and observation noises.

Contribution

This paper introduces p-Chekov, a novel probabilistic motion planner that integrates machine learning and trajectory optimization for high-dimensional robots, enabling real-time safety guarantees.

Findings

p-Chekov outperforms existing methods in collision avoidance and planning speed.

It effectively satisfies chance constraints in complex manipulation tasks.

Theoretical analysis confirms robustness under process and observation noises.

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.