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Distributed Sparse Bayesian Control Barrier Function and Its Application to Safe Persistent Exploration
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DOI：10.1109/TCNS.2025.3608070.png)
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Abstract
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Multirobot 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 multirobot 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 derived from this collaborative map. The effectiveness of the proposed algorithm is validated through both simulations and experiments with physical ground robots.
Keywords:
Control barrier function (CBF)
cooperative control
coverage control
distributed learning
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