Adaptive Sampling-based Motion Planning with Control Barrier Functions

TL;DR

This paper introduces CBF-RRT* combining sampling-based motion planning with control barrier functions for safety, enhanced by an adaptive sampling method to improve efficiency, and proves its probabilistic completeness.

Contribution

It presents a novel integration of CBFs with RRT* and introduces an adaptive sampling strategy based on CEM, improving safety guarantees and sampling efficiency in motion planning.

Findings

CBF-RRT* preserves probabilistic completeness.

Adaptive sampling improves planning efficiency.

Simulation results demonstrate effectiveness.

Abstract

Sampling-based algorithms, such as Rapidly Exploring Random Trees (RRT) and its variants, have been used extensively for motion planning. Control barrier functions (CBFs) have been recently proposed to synthesize controllers for safety-critical systems. In this paper, we combine the effectiveness of RRT-based algorithms with the safety guarantees provided by CBFs in a method called CBF-RRT$^\ast$. CBFs are used for local trajectory planning for RRT$^\ast$, avoiding explicit collision checking of the extended paths. We prove that CBF-RRT$^\ast$ preserves the probabilistic completeness of RRT$^\ast$. Furthermore, in order to improve the sampling efficiency of the algorithm, we equip the algorithm with an adaptive sampling procedure, which is based on the cross-entropy method (CEM) for importance sampling (IS). The procedure exploits the tree of samples to focus the sampling in promising regions of the configuration space. We demonstrate the efficacy of the proposed algorithms through simulation examples.