We propose a design method for a robust safety filter based on Input Constrained Control Barrier Functions (ICCBF) for car-like robots moving in complex environments. A robust ICCBF that can be efficiently implemented is obtained by learning a smooth function of the environment using Support Vector Machine regression. The method takes into account steering constraints and is validated in simulation and a real experiment.

翻译：我们提出了一种基于输入受限控制障碍函数（ICCBF）的鲁棒安全滤波器设计方法，适用于在复杂环境中运行的汽车类机器人。通过使用支持向量机回归学习环境的平滑函数，我们获得了一种可高效实现的鲁棒ICCBF。该方法考虑了转向约束，并在仿真和实际实验中得到了验证。