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A multi-agent partially observable Markov decision process (MPOMDP) is amodeling paradigm used for high-level planning of heterogeneous autonomousagents subject to uncertainty and partial observation. Despite their modelingefficiency, MPOMDPs have not received significant attention in safety-criticalsettings. In this paper, we use barrier functions to design policies forMPOMDPs that ensure safety. Notably, our method does not rely on discretizationof the belief space, or finite memory. To this end, we formulate sufficient andnecessary conditions for the safety of a given set based on discrete-timebarrier functions (DTBFs) and we demonstrate that our formulation also allowsfor Boolean compositions of DTBFs for representing more complicated safe sets.We show that the proposed method can be implemented online by a sequence ofone-step greedy algorithms as a standalone safe controller or as asafety-filter given a nominal planning policy. We illustrate the efficiency ofthe proposed methodology based on DTBFs using a high-fidelity simulation ofheterogeneous robots.

Safe Policy Synthesis in Multi-Agent POMDPs via Discrete-Time Barrier Functions