Honeybee-like collective decision making in a kilobot swarm
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 an...
| Authors: | , , , |
|---|---|
| Format: | article |
| Publication Date: | 2024 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2117/415690 |
| Online Access: | https://hdl.handle.net/2117/415690 https://dx.doi.org/10.1103/PhysRevResearch.6.033149 |
| Access Level: | Open access |
| Keyword: | Percolation (Statistical physics) Swarm intelligence Communication networks Living matter & active matter Decision making models Percolation Swarming Percolació (Física estadística) Intel·ligència col·lectiva Àrees temàtiques de la UPC::Física::Termodinàmica |
| Summary: | 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 rely on two key abilities of scout honeybees: self-discovery and imitation, symbolizing and , 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. |
|---|