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屏障函数，用于无人地面车辆规避运动（非零速度）障碍物。尽管现有形式的控制屏障函数在保证与静态障碍物的安全避碰方面已取得成效，但针对受转矩/加速度控制的独轮车和自行车模型的动态场景扩展，其效果较为有限。此外，在这些非完整约束无人地面车辆模型中，现有控制屏障函数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。受经典路径规划中利用碰撞锥进行避障方法的启发，我们提出了具有安全理论保证的独轮车和自行车模型的新型控制屏障函数公式。核心思想是确保障碍物相对于车辆的速度始终指向远离车辆的方向。据此，我们构建了一个约束条件，保证速度矢量始终避开所有指向车辆的矢量构成的锥体。该新型控制方法的有效性在科学生移动机器人上得到了实验验证。我们进一步将其扩展至自行车模型，并在CARLA仿真器中展示了不同场景下的避障效果。