This paper proposes a safety-critical controller for dynamic and uncertain environments, leveraging a robust environment control barrier function (ECBF) to enhance the robustness against the measurement and prediction uncertainties associated with moving obstacles. The approach reduces conservatism, compared with a worst-case uncertainty approach, by incorporating a state observer for obstacles into the ECBF design. The controller, which guarantees safety, is achieved through solving a quadratic programming problem. The proposed method's effectiveness is demonstrated via a dynamic obstacle-avoidance problem for an autonomous vehicle, including comparisons with established baseline approaches.
@article{arxiv.2403.13288,
title = {Observer-Based Environment Robust Control Barrier Functions for Safety-critical Control with Dynamic Obstacles},
author = {Ying Shuai Quan and Jian Zhou and Erik Frisk and Chung Choo Chung},
journal= {arXiv preprint arXiv:2403.13288},
year = {2024}
}