Learning Input Constrained Control Barrier Functions for Guaranteed Safety of Car-Like Robots
Sven Brüggemann, Dominic Nightingale, Jack Silberman, Maurício Carvalho de Oliveira · arXiv (Cornell University) · 2024
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.