A study of the Beeclust algorithm for robot swarm aggregation

dc.creatorAcevedo, Oscar
dc.creatorRios, Yuliana Y.
dc.creatorGarcia, Luis
dc.creatorNarvaez, Dayana
dc.date.accessioned2023-07-18T19:32:25Z
dc.date.accessioned2023-09-06T15:49:18Z
dc.date.available2023-07-18T19:32:25Z
dc.date.available2023-09-06T15:49:18Z
dc.date.created2023-07-18T19:32:25Z
dc.date.issued2022
dc.identifierAcevedo, O., Rios, Y. Y., Garcia, L., & Narvaez, D. (2022). A study of the Beeclust algorithm for robot swarm aggregation. 2022 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT), 1–6.
dc.identifierhttps://hdl.handle.net/20.500.12585/12143
dc.identifier10.1109/ICMLANT56191.2022.9996514
dc.identifierUniversidad Tecnológica de Bolívar
dc.identifierRepositorio Universidad Tecnológica de Bolívar
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8683182
dc.description.abstractSwarm robotics is a topic that has gained momentum in recent years thanks to its possibility to solve different engineering problems. Many robots are expected to work collaboratively to solve a given task. One of the main challenges is the design of the robot controller since it must be defined at the robot level to accomplish a task at the swarm level. The characteristics and properties of natural swarms have been studied to solve this problem. From these studies, basic behaviors have been defined, one of them being aggregation. This work explores the classical aggregation algorithm known as Beeclust. The Beeclust algorithm was implemented in MATLAB. Test were performed to determine its effectiveness in forming aggregates and the factors that affect its efficiency. © 2022 IEEE.
dc.languageeng
dc.publisherCartagena de Indias
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.sourceIEEE International Conference on Machine Learning and Applied Network Technologies
dc.titleA study of the Beeclust algorithm for robot swarm aggregation


Este ítem pertenece a la siguiente institución