Distributed safety verification for multi-agent systems

The control barrier function (CBF) framework is a powerful tool for safe controller design and safety analysis. Given a dynamical system and a CBF, the system is safe if the CBF-induced constraints are satisfied for every state inside an invariant set, which is a subset of the safe set. In this pape...

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Hlavní autoři: Wang, H, Papachristodoulou, A, Margellos, K
Médium: Conference item
Jazyk:English
Vydáno: IEEE 2023
Popis
Shrnutí:The control barrier function (CBF) framework is a powerful tool for safe controller design and safety analysis. Given a dynamical system and a CBF, the system is safe if the CBF-induced constraints are satisfied for every state inside an invariant set, which is a subset of the safe set. In this paper we propose a safety verification algorithm for networked nonlinear multi-agent systems. In our proposed algorithm, we independently sample scenarios from the invariant set, and subsequently quantify safety for the multi-agent system by solving a scenario program in a distributed manner. Both the scenario sampling and safety verification algorithms are fully distributed. The efficacy of our algorithm is demonstrated by an example on multi-robot collision avoidance.