Distributed Sparse Bayesian Control Barrier Function and Its Application to Safe Persistent Exploration

Multi-robot systems offer significant advantages for autonomously exploring large-scale, unknown environments. A critical challenge in these applications, however, is ensuring the safety of all agents in a scalable and efficient manner, especially when operating with decentralized coordination and limited communication. To overcome this limitation, this article presents a fully distributed algorithm that enables a multi-robot team to explore cooperatively while maintaining formal safety guarantees. The core of our approach is a framework where each robot individually trains a sparse Bayesian classifier using its local LiDAR data to probabilistically model unsafe regions. To achieve collaborative awareness, the robots exchange these compact, learned models, not high-volume raw data, and fuse them to construct a shared safety map. Safety is then formally guaranteed through a control barrier function (CBF) derived from this collaborative map. The effectiveness of the proposed algorithm are validated through both simulations and experiments with physical ground robots.