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...
| 主要な著者: | , , |
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| フォーマット: | Conference item |
| 言語: | English |
| 出版事項: |
IEEE
2023
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| 要約: | 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. |
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