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

doi:10.1109/TCNS.2025.3608070

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

2025-09-09

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DOI：10.1109/TCNS.2025.3608070

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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

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

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