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Descrição Detalhada da Publicação
Parallel Problem Solving from Nature – PPSN XIV. Lecture Notes in Computer Science
Ano (publicação definitiva)
2016
Língua
Inglês
País
Alemanha
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Web of Science®
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Abstract/Resumo
The potential of cooperative coevolutionary algorithms (CCEAs) as a tool for evolving control for heterogeneous multirobot teams has been shown in several previous works. The vast majority of these works have, however, been confined to simulation-based experiments. In this paper, we present one of the first demonstrations of a real multirobot system, operating outside laboratory conditions, with controllers synthesised by CCEAs. We evolve control for an aquatic multirobot system that has to perform a cooperative predator-prey pursuit task. The evolved controllers are transferred to real hardware, and their performance is assessed in a non-controlled outdoor environment. Two approaches are used to evolve control: a standard fitness-driven CCEA, and novelty-driven coevolution. We find that both approaches are able to evolve teams that transfer successfully to the real robots. Novelty-driven coevolution is able to evolve a broad range of successful team behaviours, which we test on the real multirobot system.
Agradecimentos/Acknowledgements
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Palavras-chave
Cooperative coevolution,Evolutionary robotics,Novelty search,Reality gap,Heterogeneous multirobot systems
Registos de financiamentos
Referência de financiamento | Entidade Financiadora |
---|---|
UID/MULTI/04046/2013 | Fundação para a Ciência e a Tecnologia |
UID/EEA/50008/2013 | Fundação para a Ciência e a Tecnologia |
SFRH/BD/89095/2012 | Fundação para a Ciência e a Tecnologia |
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