Sensor-Based Distributionally Robust Control for Safe Robot Navigation in Dynamic Environments

Autor:	Long, Kehan, Yi, Yinzhuang, Dai, Zhirui, Herbert, Sylvia, Cortés, Jorge, Atanasov, Nikolay
Rok vydání:	2024
Předmět:	Computer Science - Robotics Electrical Engineering and Systems Science - Systems and Control Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	We introduce a novel method for safe mobile robot navigation in dynamic, unknown environments, utilizing onboard sensing to impose safety constraints without the need for accurate map reconstruction. Traditional methods typically rely on detailed map information to synthesize safe stabilizing controls for mobile robots, which can be computationally demanding and less effective, particularly in dynamic operational conditions. By leveraging recent advances in distributionally robust optimization, we develop a distributionally robust control barrier function (DR-CBF) constraint that directly processes range sensor data to impose safety constraints. Coupling this with a control Lyapunov function (CLF) for path tracking, we demonstrate that our CLF-DR-CBF control synthesis method achieves safe, efficient, and robust navigation in uncertain dynamic environments. We demonstrate the effectiveness of our approach in simulated and real autonomous robot navigation experiments, marking a substantial advancement in real-time safety guarantees for mobile robots. Comment: Submitted to the International Journal of Robotics Research (IJRR). Project page: https://existentialrobotics.org/DR_Safe_Navigation_Webpage
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.18251 Zobrazit plný text záznamu View this record from Arxiv