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Deep learning-based localization approach for autonomous robots in the robotAtFactory 4.0 competition

2024Conference object Open access
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dc.contributor.authorKlein, Luan C.
dc.contributor.authorMendes, João
dc.contributor.authorBraun, João
dc.contributor.authorMartins, Felipe N.
dc.contributor.authorOliveira, Andre Schneider
dc.contributor.authorCosta, Paulo Gomes da
dc.contributor.authorWörtche, Heinrich
dc.contributor.authorLima, José
dc.date.accessioned2024-05-21T13:50:48Z
dc.date.available2024-05-21T13:50:48Z
dc.date.issued2024
dc.description.abstractAccurate localization in autonomous robots enables effective decision-making within their operating environment. Various methods have been developed to address this challenge, encompassing traditional techniques, fiducial marker utilization, and machine learning approaches. This work proposes a deep-learning solution employing Convolutional Neural Networks (CNN) to tackle the localization problem, specifically in the context of the RobotAtFactory 4.0 competition. The proposed approach leverages transfer learning from the pre-trained VGG16 model to capitalize on its existing knowledge. To validate the effectiveness of the approach, a simulated scenario was employed. The experimental results demonstrated an error within the millimeter scale and rapid response times in milliseconds. Notably, the presented approach offers several advantages, including a consistent model size regardless of the number of training images utilized and the elimination of the need to know the absolute positions of the fiducial markers.pt_PT
dc.description.sponsorshipThe project that gave rise to these results received the support of a fellowship from “la Caixa” Foundation (ID 100010434). The fellowship code is LCF/BQ/DI20/11780028. Jo˜ao Braun is a PhD Student at the Faculty of Engineering, University of Porto (FEUP).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationKlein, Luan C.; Mendes, João; Braun, João; Martins, Felipe N.; Oliveira, Andre Schneider de; Costa, Paulo; Wörtche, Heinrich; Lima, José (2024). Deep learning-based localization approach for autonomous robots in the robotAtFactory 4.0 competition. Communications in Computer and Information Science. In Third International Conference, OL2A 2023. Ponta Delgada,Portugal. 1982, p. 181-194pt_PT
dc.identifier.doi10.1007/978-3-031-53036-4_13pt_PT
dc.identifier.issn1865-0929
dc.identifier.urihttp://hdl.handle.net/10198/29789
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectCNNpt_PT
dc.subjectIndoor localizationpt_PT
dc.subjectRobotic competitionpt_PT
dc.titleDeep learning-based localization approach for autonomous robots in the robotAtFactory 4.0 competitionpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlacePonta Delgada,Portugalpt_PT
oaire.citation.endPage194pt_PT
oaire.citation.startPage181pt_PT
oaire.citation.titleThird International Conference, OL2A 2023pt_PT
oaire.citation.volume1982pt_PT
person.familyNameMendes
person.familyNameBraun
person.familyNameLima
person.givenNameJoão
person.givenNameJoão A.
person.givenNameJosé
person.identifier2726655
person.identifierR-000-8GD
person.identifier.ciencia-idEA1F-844D-6BA9
person.identifier.ciencia-idBF13-D66B-7D08
person.identifier.ciencia-id6016-C902-86A9
person.identifier.orcid0000-0003-0979-8314
person.identifier.orcid0000-0003-0276-4314
person.identifier.orcid0000-0001-7902-1207
person.identifier.ridL-3370-2014
person.identifier.scopus-author-id57225794972
person.identifier.scopus-author-id57211244317
person.identifier.scopus-author-id55851941311
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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relation.isAuthorOfPublicationb8dfcbd7-1b89-48f3-afee-3e7d3f3c90d4
relation.isAuthorOfPublicationd88c2b2a-efc2-48ef-b1fd-1145475e0055
relation.isAuthorOfPublication.latestForDiscoveryd88c2b2a-efc2-48ef-b1fd-1145475e0055

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