Learning Input Constrained Control Barrier Functions for Guaranteed Safety of Car-Like Robots
<|reference_start|>Learning Input Constrained Control Barrier Functions for Guaranteed Safety of Car-Like Robots: 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.<|reference_end|>
@article{brüggemann2024learning,
    title={Learning Input Constrained Control Barrier Functions for Guaranteed
  Safety of Car-Like Robots},
    author={Sven Br"uggemann, Dominic Nightingale, Jack Silberman, Maur'icio de
  Oliveira},
    journal={arXiv preprint arXiv:2402.12512},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12512},
    primaryClass={cs.RO cs.SY eess.SY}
}