In this paper, we propose a new class of Control Barrier Functions (CBFs) for Unmanned Ground Vehicles (UGVs) that help avoid collisions with kinematic (non-zero velocity) obstacles. While the current forms of CBFs have been successful in guaranteeing safety/collision avoidance with static obstacles, extensions for the dynamic case with torque/acceleration-controlled unicycle and bicycle models have seen limited success. Moreover, with these nonholonomic UGV models, applications of existing CBFs have been conservative in terms of control, i.e., steering/thrust control has not been possible under certain common scenarios. Drawing inspiration from the classical use of collision cones for obstacle avoidance in path planning, we introduce its novel CBF formulation with theoretical guarantees on safety for both the unicycle and bicycle models. The main idea is to ensure that the velocity of the obstacle w.r.t. the vehicle is always pointing away from the vehicle. Accordingly, we construct a constraint that ensures that the velocity vector always avoids a cone of vectors pointing at the vehicle. The efficacy of this new control methodology is experimentally verified on the Copernicus mobile robot. We further extend it to the bicycle model and demonstrate collision avoidance under various scenarios in the CARLA simulator.

翻译：本文提出了一类新型控制障碍函数（CBFs），用于无人地面车辆（UGVs）避开具有速度的（非零速度）运动障碍物。尽管现有CBF形式在保证静态障碍物安全/避碰方面取得了成功，但其在扭矩/加速度控制的独轮车和自行车模型动态场景中的扩展应用成效有限。此外，针对这些非完整UGV模型，现有CBF的应用在控制层面存在保守性，即在某些常见场景下无法实现转向/推力控制。受路径规划中经典碰撞锥避障方法的启发，我们提出了一种新颖的CBF公式，为独轮车和自行车模型提供了安全性的理论保证。核心思想是确保障碍物相对于车辆的速度始终远离车辆。据此，我们构建了一个约束条件，保证速度矢量始终避开指向车辆的速度矢量锥。这种新控制方法的有效性在Copernicus移动机器人上得到了实验验证。我们进一步将其拓展至自行车模型，并在CARLA仿真器中展示了多种场景下的避障效果。