@inproceedings{c97133daef0e4fbb8a297075f73b5f68,
title = "Context-Aware Modelling for Multi-Robot Systems Under Uncertainty",
abstract = "Formal models of multi-robot behaviour are fundamental to planning, 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 models 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 interactions, 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 simulator against a Gazebo simulation in a range of multi-robot navigation scenarios, demonstrating that our model accurately captures high-level multi-robot behaviour.",
author = "Charlie Street and Bruno Lacerda and Michal Staniaszek and Manuel M{\"u}hlig and Nick Hawes",
year = "2022",
month = may,
day = "9",
language = "English",
isbn = "9781450392136",
series = "AAMAS: International Conference on Autonomous Agents and Multiagent Systems",
publisher = "International Foundation for Autonomous Agents and Multiagent Systems",
pages = "1228--1236",
booktitle = "AAMAS '22",
address = "United Kingdom",
note = "International Conference on Autonomous Agents and Multiagent Systems 2022, AAMAS 2022 ; Conference date: 09-05-2022 Through 13-05-2022",
}