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Multi AGV industrial supervisory system

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dc.contributor.authorCruz, Ana Luísa Sampaio
dc.contributor.authorMatos, Diogo
dc.contributor.authorLima, José
dc.contributor.authorCosta, Paulo Gomes da
dc.contributor.authorCosta, Pedro
dc.date.accessioned2022-04-05T08:32:38Z
dc.date.available2022-04-05T08:32:38Z
dc.date.issued2021
dc.description.abstractAutomated guided vehicles (AGV) represent a key element in industries’ intralogistics and the use of AGV fleets bring multiple advantages. Nevertheless, coordinating a fleet of AGV is already a complex task but when exposed to delays in the trajectory and communication faults it can represent a threat, compromising the safety, productivity and efficiency of these systems. Concerning this matter, trajectory planning algorithms allied with supervisory systems have been studied and developed. This article aims to, based on work developed previously, implement and test a Multi AGV Supervisory System on real robots and analyse how the system responds to the dynamic of a real environment, analysing its intervention, what influences it and how the execution time is affected.pt_PT
dc.description.sponsorshipThis work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project UIDB/50014/2020.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationCruz, Ana; Matos, Diogo; Lima, José; Costa, Paulo; Costa, Pedro (2021). Multi AGV industrial supervisory system. In Pereira, Ana I.; Fernandes, Florbela P.; Coelho, João Paulo; Teixeira, João Paulo; Pacheco, Maria F.; Alves, Paulo; Lopes, Rui Pedro (Eds.) Optimization, learning algorithms and applications: first International Conference, OL2A 2021. Cham: Springer Nature. p. 203-218. ISBN 978-3-030-91884-2pt_PT
dc.identifier.doi10.1007/978-3-030-91885-9_15pt_PT
dc.identifier.isbn978-3-030-91884-2
dc.identifier.urihttp://hdl.handle.net/10198/25334
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.relationINESC TEC- Institute for Systems and Computer Engineering, Technology and Science
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectMulti AGV coordinationpt_PT
dc.subjectTime enhanced A*pt_PT
dc.subjectReal implementationpt_PT
dc.titleMulti AGV industrial supervisory systempt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleINESC TEC- Institute for Systems and Computer Engineering, Technology and Science
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50014%2F2020/PT
oaire.citation.endPage218pt_PT
oaire.citation.startPage203pt_PT
oaire.citation.titleOptimization, learning algorithms and applications: first International Conference, OL2A 2021pt_PT
oaire.citation.volume1488pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameLima
person.givenNameJosé
person.identifierR-000-8GD
person.identifier.ciencia-id6016-C902-86A9
person.identifier.orcid0000-0001-7902-1207
person.identifier.ridL-3370-2014
person.identifier.scopus-author-id55851941311
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationd88c2b2a-efc2-48ef-b1fd-1145475e0055
relation.isAuthorOfPublication.latestForDiscoveryd88c2b2a-efc2-48ef-b1fd-1145475e0055
relation.isProjectOfPublication2957d2e8-0cce-46ca-8e0e-d15ccf4f290e
relation.isProjectOfPublication.latestForDiscovery2957d2e8-0cce-46ca-8e0e-d15ccf4f290e

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