Logo do repositório
 
Publicação

Optimization of machine learning models applied to robot localization in the robotatfactory 4.0 competition

2024Comunicação em conferência Acesso restrito
Ver registo simples
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg04:Educação de Qualidade
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorKlein, Luan C.
dc.contributor.authorMendes, João
dc.contributor.authorBraun, João A.
dc.contributor.authorMartins, Felipe N.
dc.contributor.authorFabro, João Alberto
dc.contributor.authorCosta, Paulo
dc.contributor.authorPereira, Ana I.
dc.contributor.authorLima, José
dc.date.accessioned2026-03-18T16:54:52Z
dc.date.available2026-03-18T16:54:52Z
dc.date.issued2024
dc.description.abstractSeveral approaches have been developed over time aiming to improve the localization aspects, especially in mobile robotics. Besides the more traditional techniques, mainly based on analytical models, artificial intelligence has emerged as an interesting alternative. The current study proposes to explore the machine learning model structure optimization for pose estimation, using the RobotAtFactory 4.0 competition as the main context. Using a Bayesian Optimization-based framework, the parameters of a Multi-Layer Perceptron (MLP) model, trained to estimate the components of the 2D pose (x, y, and !) of the robot were optimized in four different scenarios of the same context. The results obtained showed a quality improvement of up to 60% on the estimation when compared with the modes without any optimization. Another aspect observed was the different optimizations found for each model, even in the same scenario. An additional interesting result was the possibility of the reuse of optimization between scenarios, presenting an interesting approach to reduce time and computational resources.eng
dc.description.sponsorshipF. Martins also thanks the Sensors and Smart Systems group, Research Centre in Bio-based Economy of Hanze University of Applied Sciences.
dc.identifier.citationKlein, Luan C.; Mendes, João; Braun, João A.; Martins, Felipe N.; Fabro, João Alberto; Costa, Paulo; Pereira, Ana I.; Lima, José (2024). Optimization of machine learning models applied to robot localization in the robotatfactory 4.0 competition. In 4th International Conference on Optimization, Learning Algorithms and Applications, OL2A 2024. 2280, p. 112-125. ISSN 1865-0929. DOI: 10.1007/978-3-031-77426-3_8
dc.identifier.doi10.1007/978-3-031-77426-3_8
dc.identifier.issn1865-0929
dc.identifier.urihttp://hdl.handle.net/10198/36141
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer
dc.relation.ispartofCommunications in Computer and Information Science
dc.relation.ispartofOptimization, Learning Algorithms and Applications
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/
dc.subjectLocalization
dc.subjectMachine learning optimization
dc.subjectRobotics
dc.titleOptimization of machine learning models applied to robot localization in the robotatfactory 4.0 competitioneng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.endPage125
oaire.citation.startPage112
oaire.citation.title4th International Conference on Optimization, Learning Algorithms and Applications, OL2A 2024
oaire.citation.volume2280
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameMendes
person.familyNameBraun
person.familyNamePereira
person.familyNameLima
person.givenNameJoão
person.givenNameJoão A.
person.givenNameAna I.
person.givenNameJosé
person.identifier2726655
person.identifierR-000-8GD
person.identifier.ciencia-idEA1F-844D-6BA9
person.identifier.ciencia-idBF13-D66B-7D08
person.identifier.ciencia-id0716-B7C2-93E4
person.identifier.ciencia-id6016-C902-86A9
person.identifier.orcid0000-0003-0979-8314
person.identifier.orcid0000-0003-0276-4314
person.identifier.orcid0000-0003-3803-2043
person.identifier.orcid0000-0001-7902-1207
person.identifier.ridF-3168-2010
person.identifier.ridL-3370-2014
person.identifier.scopus-author-id57225794972
person.identifier.scopus-author-id57211244317
person.identifier.scopus-author-id15071961600
person.identifier.scopus-author-id55851941311
relation.isAuthorOfPublicationb5c9de22-cf9e-47b8-b7a4-26e08fb12b28
relation.isAuthorOfPublicationb8dfcbd7-1b89-48f3-afee-3e7d3f3c90d4
relation.isAuthorOfPublicatione9981d62-2a2b-4fef-b75e-c2a14b0e7846
relation.isAuthorOfPublicationd88c2b2a-efc2-48ef-b1fd-1145475e0055
relation.isAuthorOfPublication.latestForDiscoveryb8dfcbd7-1b89-48f3-afee-3e7d3f3c90d4

Ficheiros

Principais
A mostrar 1 - 1 de 1
Miniatura indisponível
Nome:
Optimisation of Machine.pdf
Tamanho:
538.09 KB
Formato:
Adobe Portable Document Format
Ver/Abrir
Licença
A mostrar 1 - 1 de 1
Miniatura indisponível
Nome:
license.txt
Tamanho:
1.75 KB
Formato:
Item-specific license agreed upon to submission
Descrição:
Ver/Abrir

Coleções

ESTiG - Publicações em Proceedings Indexadas à WoS/Scopus