Hauert, Sabine

Hogg, Elliott

Richards, Arthur G

English

Whilst swarms have potential in a range of applications,in practical real-world situations, we need easy ways to supervise andchange the behaviour of swarms to promote robust performance. In thispaper, we design artificial supervision of swarms to enable an agent tointeract with a swarm of robots and command it to efficiently searchcomplex partially known environments. This is implemented throughartificial evolution of human readable behaviour trees which representsupervisory strategies. In search and rescue (SAR) problems, considering uncertainty is crucial to achieve reliable performance. Therefore, wetask 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 producerobust search solutions which adapt to the uncertain conditions

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Evolving Robust Supervisors for Robot Swarms in Uncertain Complex Environments

https://research-information.bris.ac.uk/files/279008008/dars_swarm_paper_10_.pdf

Evolving Robust Supervisors for Robot Swarms in Uncertain Complex Environments

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