Autonomy advances have enabled robots in diverse environments and close human interaction, necessitating controllers with formal safety guarantees. This paper introduces an experimental platform designed for the validation and demonstration of a novel class of Control Barrier Functions (CBFs) tailored for Unmanned Ground Vehicles (UGVs) to proactively prevent collisions with kinematic obstacles by integrating the concept of collision cones. While existing CBF formulations excel with static obstacles, extensions to torque/acceleration-controlled unicycle and bicycle models have seen limited success. Conventional CBF applications in nonholonomic UGV models have demonstrated control conservatism, particularly in scenarios where steering/thrust control was deemed infeasible. Drawing inspiration from collision cones in path planning, we present a pioneering CBF formulation ensuring theoretical safety guarantees for both unicycle and bicycle models. The core premise revolves around aligning the obstacle's velocity away from the vehicle, establishing a constraint to perpetually avoid vectors directed towards it. This control methodology is rigorously validated through simulations and experimental verification on the Copernicus mobile robot (Unicycle Model) and FOCAS-Car (Bicycle Model).

翻译：自主性进步使机器人能够在多样环境中与人类紧密交互，这要求控制器具备形式化安全保障。本文介绍了一个专为验证和展示新型控制障碍函数（CBF）而设计的实验平台，该平台针对无人地面车辆（UGV），通过融合碰撞锥概念，主动预防与运动障碍物的碰撞。现有CBF公式在静态障碍处理中表现优异，但扩展到力矩/加速度控制的两轮差速模型和自行车模型时效果有限。非完整UGV模型中的传统CBF应用显露出控制保守性问题，尤其在转向/推力控制被认为不可行的场景中。受路径规划领域碰撞锥概念的启发，我们提出了一种开创性的CBF公式，为两轮差速模型和自行车模型提供理论安全保证。其核心原理在于调整障碍物速度方向使其远离自车，建立约束以持续规避指向自车的速度矢量。该控制方法通过Copernicus移动机器人（两轮差速模型）和FOCAS-Car（自行车模型）的仿真与实验验证得到了严格验证。