Sparse Bayesian Approach for Learning Control Barrier Functions and Safe Persistent Coverage Control

Abstract

In this paper, we propose a persistent coverage control method to safely explore unknown environments using an environmental model learned by the sparse Bayesian approach. A sparse Bayesian classification model is introduced to estimate safety from the obtained partial environmental data by LiDAR sensors. Then, based on the control barrier function method, we propose a control law to cover the unknown environment while guaranteeing the safety of robots using a sparse Bayesian classification model. We also propose an algorithm sequentially updating the sparse Bayesian classification model with new datasets obtained through safe coverage control. Finally, we verify the effectiveness of the proposed algorithm through simulations.