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A machine learning approach to robot localization using fiducial markers in robotatfactory 4.0 competition

2023Journal article Open access
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dc.contributor.authorKlein, Luan C.
dc.contributor.authorBraun, João
dc.contributor.authorMendes, João
dc.contributor.authorPinto, Vítor H.
dc.contributor.authorMartins, Felipe N.
dc.contributor.authorOliveira, Andre Schneider
dc.contributor.authorOliveira, Andre Schneider
dc.contributor.authorWörtche, Heinrich
dc.contributor.authorCosta, Paulo Gomes da
dc.contributor.authorLima, José
dc.date.accessioned2018-06-15T15:18:21Z
dc.date.available2018-06-15T15:18:21Z
dc.date.issued2023
dc.description.abstractLocalization is a crucial skill in mobile robotics because the robot needs to make reasonable navigation decisions to complete its mission. Many approaches exist to implement localization, but artificial intelligence can be an interesting alternative to traditional localization techniques based on model calculations. This work proposes a machine learning approach to solve the localization problem in the RobotAtFactory 4.0 competition. The idea is to obtain the relative pose of an onboard camera with respect to fiducial markers (ArUcos) and then estimate the robot pose with machine learning. The approaches were validated in a simulation. Several algorithms were tested, and the best results were obtained by using Random Forest Regressor, with an error on the millimeter scale. The proposed solution presents results as high as the analytical approach for solving the localization problem in the RobotAtFactory 4.0 scenario, with the advantage of not requiring explicit knowledge of the exact positions of the fiducial markers, as in the analytical approach.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationKlein, Luan C.; Braun, João; Mendes, João; Pinto, Vítor H.; Martins, Felipe N.; Oliveira, Andre Schneider; Oliveira, Andre Schneider; Wörtche, Heinrich; Costa, Paulo José; Lima, José (2023). A machine learning approach to robot localization using fiducial markers in robotatfactory 4.0 competition. Sensors. eISSN 1424-8220. 23:6, p. 1-17pt_PT
dc.identifier.doi10.3390/s23063128
dc.identifier.eissn1424-8220
dc.identifier.urihttp://hdl.handle.net/10198/17690
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPI
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectIndoor localization
dc.subjectMachine learning
dc.subjectFiducial markers
dc.subjectIndustry 4.0
dc.subjectRobotics competitions
dc.titleA machine learning approach to robot localization using fiducial markers in robotatfactory 4.0 competitionpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titleSensorspt_PT
person.familyNameBraun Neto
person.familyNameMendes
person.familyNameLima
person.givenNameJoão Afonso
person.givenNameJoão
person.givenNameJosé
person.identifier2726655
person.identifierR-000-8GD
person.identifier.ciencia-idBF13-D66B-7D08
person.identifier.ciencia-idEA1F-844D-6BA9
person.identifier.ciencia-id6016-C902-86A9
person.identifier.orcid0000-0003-0276-4314
person.identifier.orcid0000-0003-0979-8314
person.identifier.orcid0000-0001-7902-1207
person.identifier.ridL-3370-2014
person.identifier.scopus-author-id57211244317
person.identifier.scopus-author-id57225794972
person.identifier.scopus-author-id55851941311
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublicationb5c9de22-cf9e-47b8-b7a4-26e08fb12b28
relation.isAuthorOfPublicationd88c2b2a-efc2-48ef-b1fd-1145475e0055
relation.isAuthorOfPublication.latestForDiscoveryb5c9de22-cf9e-47b8-b7a4-26e08fb12b28

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