Barrier Functions for Multiagent-POMDPs with DTL Specifications
This work addresses safety assurance for heterogeneous autonomous agents in uncertain environments, representing an incremental improvement by applying barrier functions to existing MPOMDP frameworks.
The paper tackles the problem of ensuring safety for multi-agent systems under uncertainty and partial observation by designing a minimally interfering safety-shield using barrier functions to satisfy high-level specifications in linear distribution temporal logic, demonstrating efficacy through experiments with a team of robots.
Multi-agent partially observable Markov decision processes (MPOMDPs) provide a framework to represent heterogeneous autonomous agents subject to uncertainty and partial observation. In this paper, given a nominal policy provided by a human operator or a conventional planning method, we propose a technique based on barrier functions to design a minimally interfering safety-shield ensuring satisfaction of high-level specifications in terms of linear distribution temporal logic (LDTL). To this end, we use sufficient and necessary conditions for the invariance of a given set based on discrete-time barrier functions (DTBFs) and formulate sufficient conditions for finite time DTBF to study finite time convergence to a set. We then show that different LDTL mission/safety specifications can be cast as a set of invariance or finite time reachability problems. We demonstrate that the proposed method for safety-shield synthesis can be implemented online by a sequence of one-step greedy algorithms. We demonstrate the efficacy of the proposed method using experiments involving a team of robots.