Safe and Dynamically-Feasible Motion Planning using Control Lyapunov and Barrier Functions

TL;DR

This paper introduces a new motion planning algorithm that ensures collision-free, safe, and dynamically feasible paths for control-affine systems by combining RRTs with control Lyapunov and barrier functions, validated through simulations and hardware tests.

Contribution

The paper presents the C-CLF-CBF-RRT algorithm, integrating CLFs and CBFs with RRTs for efficient, safe, and dynamically feasible motion planning in control-affine systems.

Findings

Algorithm is computationally efficient for linear systems with constraints.

Probabilistic completeness of the method is established.

Successful simulation and hardware experiments demonstrate effectiveness.

Abstract

This paper considers the problem of designing motion planning algorithms for control-affine systems that generate collision-free paths from an initial to a final destination and can be executed using safe and dynamically-feasible controllers. We introduce the C-CLF-CBF-RRT algorithm, which produces paths with such properties and leverages rapidly exploring random trees (RRTs), control Lyapunov functions (CLFs) and control barrier functions (CBFs). We show that C-CLF-CBF-RRT is computationally efficient for linear systems with polytopic and ellipsoidal constraints, and establish its probabilistic completeness. We showcase the performance of C-CLF-CBF-RRT in different simulation and hardware experiments.