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Context-Aware Modelling for Multi-Robot Systems Under Uncertainty

Charlie Street, Bruno Lacerda, Michal Staniaszek, Manuel Mühlig, Nick Hawes, "Context-Aware Modelling for Multi-Robot Systems Under Uncertainty", International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2022.


Formal models of multi-robot behaviour are fundamental to plan- ning, simulation, and model checking techniques. However, existing models are invalidated by strong assumptions that fail to capture execution-time multi-robot behaviour, such as simplistic duration models or synchronisation constraints. In this paper we propose a novel multi-robot Markov automaton formulation which mod- els asynchronous multi-robot execution in continuous time. Robot dynamics are captured using phase-type distributions over action durations. Moreover, we explicitly model the effects of robot inter- actions, as they are a key factor for the duration of action execution. We also present a scalable discrete-event simulator which yields realistic statistics over execution-time robot behaviour by sampling through the Markov automaton. We validate our model and simula- tor against a Gazebo simulation in a range of multi-robot navigation scenarios, demonstrating that our model accurately captures high- level multi-robot behaviour.

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