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.

翻译：在非结构化环境中实现安全导航能力对于机器人系统在现实场景中的部署至关重要。近年来，基于控制障碍函数的方法在合成安全关键控制器方面展现出显著效果。本文提出一种由"Vessel"与"Mariner"两部分组成的新型CBF局部规划器：Vessel作为基于缩放因子的新型CBF公式体系，仅利用点云数据即可合成控制障碍函数；Mariner则作为基于CBF的预览控制框架，旨在规避导航过程中陷入伪平衡点的问题。为验证所提方法的有效性，我们首先将提出的点云CBF公式与其他点云CBF公式进行对比。随后，通过Unitree B1与Unitree Go2四足机器人在不同环境中的实验研究，展示了所提方法及其与全局规划器集成的实际性能。