Abstract
Whilst swarms have potential in a range of applications, in practical real-world situations, we need easy ways to supervise and change the behaviour of swarms to promote robust performance. In this paper, we design artificial supervision of swarms to enable an agent to interact with a swarm of robots and command it to efficiently search complex partially known environments. This is implemented through artificial evolution of human readable behaviour trees which represent supervisory strategies. In search and rescue (SAR) problems, considering uncertainty is crucial to achieve reliable performance. Therefore, we task supervisors to explore two complex environments subject to varying blockages which greatly hinder accessibility. We demonstrate the improved performance achieved with the evolved supervisors and produce robust search solutions which adapt to the uncertain conditions.
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Acknowledgements
This work was funded and delivered in partnership between the Thales Group and the University of Bristol, and with the support of the UK Engineering and Physical Sciences Research Council Grant Award EP/R004757/1 entitled “Thales-Bristol Partnership in Hybrid Autonomous Systems Engineering (T-B PHASE)”.
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Hogg, E., Harvey, D., Hauert, S., Richards, A. (2022). Evolving Robust Supervisors for Robot Swarms in Uncertain Complex Environments. In: Matsuno, F., Azuma, Si., Yamamoto, M. (eds) Distributed Autonomous Robotic Systems. DARS 2021. Springer Proceedings in Advanced Robotics, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-030-92790-5_10
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DOI: https://doi.org/10.1007/978-3-030-92790-5_10
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