Aggregation Through Adaptive Random Walks in a Minimalist Robot Swarm

Luigi Feola, Antoine SION, Vito Trianni, Andreagiovanni Reina, Elio Tuci

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Résumé

In swarm robotics, random walks have proven to be efficient behaviours to explore unknown environments. By adapting the parameters of the random walk to environmental and social contingencies, it is possible to obtain interesting collective behaviours. In this paper, we introduce two novel aggregation behaviours based on different parameterisations of random walks tuned through numerical optimisation. Cue-based aggregation allows the swarm to reach the centre of an arena relying only on local discrete sampling, but does not guarantee the formation of a dense cluster. Neighbour-based aggregation instead allows the swarm to cluster in a single location based on the local detection of neighbours, but ignores the environmental cue. We then investigate a heterogeneous swarm made up of the two robot types. Results show that a trade-off can be found in terms of robot proportions to achieve cue-based aggregation while keeping the majority of the swarm in a single dense cluster.

langue originaleAnglais
titreGECCO 2023 - Proceedings of the 2023 Genetic and Evolutionary Computation Conference
Pages21-29
Nombre de pages9
ISBN (Electronique)9798400701191
Les DOIs
Etat de la publicationPublié - 15 juil. 2023

Série de publications

NomGECCO 2023 - Proceedings of the 2023 Genetic and Evolutionary Computation Conference

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