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dc.contributor.authorVaughan, Neil*
dc.date.accessioned2018-12-17T15:34:59Z
dc.date.available2018-12-17T15:34:59Z
dc.date.issued2018-07-07
dc.identifier.citationVaughan N. (2018) Evolutionary Robot Swarm Cooperative Retrieval. In: Vouloutsi V. et al. (eds) Biomimetic and Biohybrid Systems. Living Machines 2018. Lecture Notes in Computer Science, vol 10928. Springer.en
dc.identifier.isbn9783319959719
dc.identifier.doi10.1007/978-3-319-95972-6_55
dc.identifier.urihttp://hdl.handle.net/10034/621678
dc.descriptionThe final publication is available at Springer via https://doi.org/10.1007/978-3-319-95972-6_55en
dc.description.abstractIn nature bees and leaf-cutter ants communicate to improve cooperation during food retrieval. This research aims to model communication in a swarm of auton-omous robots. When food is identified robot communication is emitted within a limited range. Other robots within the range receive the communication and learn of the location and size of the food source. The simulation revealed that commu-nication improved the rate of cooperative food retrieval tasks. However a counter-productive chain reaction can occur when robots repeat communications from other robots causing cooperation errors. This can lead to a large number of robots travelling towards the same food source at the same time. The food becomes de-pleted, before some robots have arrived. Several robots continue to communicate food presence, before arriving at the food source to find it gone. Nature-inspired communication can enhance swarm behaviour without requiring a central control-ler and may be useful in autonomous drones or vehicles.
dc.language.isoenen
dc.publisherSpringeren
dc.relation.urlhttps://www.emeraldinsight.com/doi/full/10.1108/TLO-09-2018-0149en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectrobot simulationen
dc.subjectswarm communicationen
dc.subjectagents based modellingen
dc.subjectcooperative retrievalen
dc.titleEvolutionary Robot Swarm Cooperative Retrievalen
dc.typeBook chapteren
dc.contributor.departmentRoyal Academy of Engineering; University of Chesteren
dc.date.accepted2018-05-28
or.grant.openaccessYesen
rioxxterms.funderRoyal Academy of Engineering - Dr Neil Vaughan Research Fellowshipen_US
rioxxterms.identifier.projectCSIS17/03en_US
rioxxterms.versionAMen
rioxxterms.licenseref.startdate2020-07-07


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