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dc.contributor.authorGarcia-Aunon, Pablo-
dc.contributor.authorBarrientos, Antonio-
dc.date.accessioned2018-10-08T11:16:44Z-
dc.date.available2018-10-08T11:16:44Z-
dc.date.issued2018-
dc.identifier.citationApplied Sciences (Switzerland) 8 (2018)-
dc.identifier.issn2076-3417-
dc.identifier.urihttp://hdl.handle.net/10261/170749-
dc.description.abstractThe search of a given area is one of the most studied tasks in swarm robotics. Different heuristic methods have been studied in the past taking into account the peculiarities of these systems (number of robots, limited communications and sensing and computational capacities). In this work, we introduce a behavioral network made up of different well-known behaviors that act together to achieve a good performance, while adapting to different scenarios. The algorithm is compared with six strategies based on movement patterns in terms of three performance models. For the comparison, four scenario types are considered: plain, with obstacles, with the target location probability distribution and a combination of obstacles and the target location probability distribution. For each scenario type, different variations are considered, such as the number of agents and area size. Results show that although simplistic solutions may be convenient for the simplest scenario type, for the more complex ones, the proposed algorithm achieves better results.-
dc.description.sponsorshipWe would like to thank the SAVIER (Situational Awareness Virtual EnviRonment) Project, which is both supported and funded by Airbus Defence & Space. The research leading to these results has received funding from the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. Fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and co-funded by Structural Funds of the EU, and from the DPI2014-56985-R project (Protección Robotizada de Infraestructuras Críticas (PRIC)) funded by the Ministerio de Economía y Competitividad of Gobierno de España.-
dc.publisherMultidisciplinary Digital Publishing Institute-
dc.relationCM/S2013/MIT-2748/RoboCity2030-III-CM-
dc.relation.isversionofPublisher's version-
dc.rightsopenAccess-
dc.subjectsurveillance-
dc.subjectcomparison-
dc.subjectpatterns-
dc.subjectbehaviors-
dc.subjectswarm robotics-
dc.subjectsearch-
dc.titleComparison of heuristic algorithms in discrete search and surveillance tasks using aerial swarms-
dc.typeartículo-
dc.identifier.doihttp://dx.doi.org/10.3390/app8050711-
dc.date.updated2018-10-08T11:16:45Z-
dc.description.versionPeer Reviewed-
dc.language.rfc3066eng-
dc.rights.licensehttp://creativecommons.org/licenses/by/4.0/-
dc.contributor.funderMinisterio de Economía y Competitividad (España)-
dc.relation.csic-
dc.identifier.funderhttp://dx.doi.org/10.13039/501100003329es_ES
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