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dc.contributor.authorOsaba, Eneko
dc.contributor.authorDel Ser, Javier
dc.contributor.authorJubeto, Xabier
dc.contributor.authorIglesias, Andrés
dc.contributor.authorFister, Iztok
dc.contributor.authorGálvez, Akemi
dc.date.accessioned2020-12-15T11:10:13Z
dc.date.available2020-12-15T11:10:13Z
dc.date.issued2020-10-27
dc.identifier.citationOsaba, Eneko, Javier Del Ser, Xabier Jubeto, Andrés Iglesias, Iztok Fister, Akemi Gálvez, and Iztok Fister. “Distributed Coordination of Heterogeneous Robotic Swarms Using Stochastic Diffusion Search.” Intelligent Data Engineering and Automated Learning – IDEAL 2020 (2020): 79–91. doi:10.1007/978-3-030-62365-4_8.en
dc.identifier.isbn978-3-030-62365-4; 978-3-030-62364-7en
dc.identifier.issn0302-9743en
dc.identifier.urihttp://hdl.handle.net/11556/1033
dc.description.abstractThe term Swarm Robotics collectively refers to a population of robotic devices that efficiently undertakes diverse tasks in a collaborative way by virtue of computational intelligence techniques. This paradigm has given rise to a profitable stream of contributions in recent years, all sharing a clear consensus on the performance benefits derived from the increased exploration capabilities offered by Swarm Robotics. This manuscript falls within this topic: specifically, it gravitates on an heterogeneous Swarm Robotics system that relies on Stochastic Diffusion Search (SDS) as the coordination heuristics for the exploration, location and delimitation of areas scattered over the area in which robots are deployed. The swarm is composed by agents of diverse kind, which can be ground robots or flying devices. These agents communicate to each other and cooperate towards the accomplishment of the exploration tasks comprising the mission of the overall swarm. Furthermore, maps contain several obstacles and dangers, implying that in order to enter a specific area, robots should meet certain conditions. Experiments are conducted over three different maps and three implemented solving approaches. Conclusions are drawn from the obtained results, confirming that i) SDS allows for a lightweight, heuristic mechanism for the coordination of the robots; and ii) the most efficient swarming approach is the one comprising a heterogeneity of ground and aerial robots.en
dc.description.sponsorshipBasque Government for its support through the EMAITEK and ELKARTEK (Elkarbot project) programs. Javier Del Ser also receives support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of this institution.en
dc.language.isoengen
dc.publisherSpringeren
dc.titleDistributed Coordination of Heterogeneous Robotic Swarms Using Stochastic Diffusion Searchen
dc.typeconferenceObjecten
dc.identifier.doi10.1007/978-3-030-62365-4_8en
dc.rights.accessRightsembargoedAccessen
dc.subject.keywordsSwarm Roboticsen
dc.subject.keywordsStochastic Diffusion Searchen
dc.subject.keywordsSwarm Intelligenceen
dc.subject.keywordsUnmanned Aerial Vehiclesen
dc.subject.keywordsRoboticsen
dc.identifier.essn1611-3349en
dc.journal.titleLecture Notes in Computer Science book seriesen
dc.page.final91en
dc.page.initial79en
dc.volume.number12490en
dc.conference.titleIntelligent Data Engineering and Automated Learning – IDEAL 2020en


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