# Adaptive Sampling-based Motion Planning with Control Barrier Functions

arXiv: https://arxiv.org/abs/2206.00795 · HTML: https://mlsanity.com/paper/2206.00795/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2022.

## Built on by (most cited first, 1 of 1)

- [Efficient Motion Planning for Manipulators with Control Barrier Function-Induced Neural Controller](https://mlsanity.com/paper/2404.01184/) (2024)

## Abstract (arXiv)

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.