Fast-Reactive Probabilistic Motion Planning for High-Dimensional Robots

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 carlike robots cannot 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.

Chance-Constrained Iterative Linear-Quadratic Stochastic Games

“…However, calculating chance constraint violations is intractable in general, which makes chanceconstrained planning challenging. Previous works [15], [16], [17] leverage sampling to approximate chance constraint violations and solve the planning problem. However, a common flaw of the sampling-based approaches is scalability regarding the number and dimensions of chance constraints, which limits their practicability.…”