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：在现实场景中部署机器人系统时，在非结构化环境中安全导航的能力至关重要。近年来，基于控制屏障函数的方法在合成安全关键控制器方面已证明极为有效。在本工作中，我们提出了一种新颖的基于CBF的局部规划器，它由两个组件构成：Vessel与Mariner。Vessel是一种基于新颖缩放因子的CBF公式，它仅使用点云数据来合成CBF。Mariner是一种基于CBF的预览控制框架，用于缓解导航过程中陷入伪平衡点的问题。为了证明我们提出的方法的有效性，我们首先将所提出的基于点云的CBF公式与其他基于点云的CBF公式进行比较。随后，通过在Unitree B1和Unitree Go2四足机器人上于多种环境中进行实验研究，我们展示了所提方法的性能及其与全局规划器的集成效果。