Evolving Robust Supervisors for Robot Swarms in Uncertain Complex Environments

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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.
Original languageEnglish
Number of pages14
Publication statusPublished - 1 Jun 2021
EventDARS-SWARM 2021: Joint conference between distributed autonomous robotic systems (DARS) and SWARM robotics conference -
Duration: 1 Jun 20214 Jun 2021


ConferenceDARS-SWARM 2021
Internet address


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