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arXiv · 2402.12512

Learning Input Constrained Control Barrier Functions for Guaranteed Safety of Car-Like Robots

Sven Brüggemann·Dominic Nightingale·Jack Silberman·Maurício de Oliveira
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Abstract

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.

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Sven Brüggemann, Dominic Nightingale, Jack Silberman, Maurício de Oliveira. 2024-02-19. Learning Input Constrained Control Barrier Functions for Guaranteed Safety of Car-Like Robots. https://arxiv.org/abs/2402.12512

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