Sailing Through Point Clouds: Safe Navigation Using Point Cloud Based Control Barrier Functions

TL;DR

This paper introduces a novel point cloud based control barrier function framework for safe robotic navigation, combining local planning with global integration, validated through experiments on quadruped robots in unstructured environments.

Contribution

It presents a new CBF formulation using point cloud data and a preview control framework to improve safety and navigation robustness in unstructured environments.

Findings

Effective point cloud based CBF formulation demonstrated

Improved navigation safety shown in robot experiments

Integration with global planners enhances performance

Abstract

The capability to navigate safely in an unstructured environment is crucial when deploying robotic systems in real-world scenarios. Recently, control barrier function (CBF) based approaches have been highly effective in synthesizing safety-critical controllers. In this work, we propose a novel CBF-based local planner comprised of two components: Vessel and Mariner. The Vessel is a novel scaling factor based CBF formulation that synthesizes CBFs using only point cloud data. The Mariner is a CBF-based preview control framework that is used to mitigate getting stuck in spurious equilibria during navigation. To demonstrate the efficacy of our proposed approach, we first compare the proposed point cloud based CBF formulation with other point cloud based CBF formulations. Then, we demonstrate the performance of our proposed approach and its integration with global planners using experimental studies on the Unitree B1 and Unitree Go2 quadruped robots in various environments.