Honeybee-like collective decision making in a kilobot swarm

David March, Julia Múgica, Ezequiel E. Ferrero, M. Carmen Miguel
Condensed Matter, Disordered Systems and Neural Networks, Disordered Systems and Neural Networks (cond-mat.dis-nn), Soft Condensed Matter (cond-mat.soft), Statistical Mechanics (cond-mat.stat-mech), Adaptation and Self-Organizing Systems (nlin.AO), Biological Physics (
2023-10-23 16:00:00
Drawing inspiration from honeybee swarms' nest-site selection process, we assess the ability of a kilobot robot swarm to replicate this captivating example of collective decision-making. Honeybees locate the optimal site for their new nest by aggregating information about potential locations and exchanging it through their waggle-dance. The complexity and elegance of solving this problem relies on two key abilities of scout honeybees: self-discovery and imitation, symbolizing independence and interdependence, respectively. We employ a mathematical model to represent this nest-site selection problem and program our kilobots to follow its rules. Our experiments demonstrate that the kilobot swarm can collectively reach consensus decisions in a decentralized manner, akin to honeybees. However, the strength of this consensus depends not only on the interplay between independence and interdependence but also on critical factors such as swarm density and the motion of kilobots. These factors enable the formation of a percolated communication network, through which each robot can receive information beyond its immediate vicinity. By shedding light on this crucial layer of complexity --the crowding and mobility conditions during the decision-making--, we emphasize the significance of factors typically overlooked but essential to living systems and life itself.
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