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Using machine learning approaches to localization in an embedded system on RobotAtFactory 4.0 competition: a case study

2023Conference paper Open access
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dc.contributor.authorKlein, Luan C.
dc.contributor.authorBraun, João
dc.contributor.authorMartins, Felipe N.
dc.contributor.authorWörtche, Heinrich
dc.contributor.authorOliveira, Andre Schneider
dc.contributor.authorMendes, João
dc.contributor.authorPinto, Vítor H.
dc.contributor.authorCosta, Paulo Gomes da
dc.date.accessioned2013-01-15T11:03:24Z
dc.date.available2013-01-15T11:03:24Z
dc.date.issued2023
dc.description.abstractThe use of machine learning in embedded systems is an interesting topic, especially with the growth in popularity of the Internet of Things (IoT). The capacity of a system, such as a robot, to self-localize, is a fundamental skill for its navigation and decision-making processes. This work focuses on the feasibility of using machine learning in a Raspberry Pi 4 Model B, solving the localization problem using images and fiducial markers (ArUco markers) in the context of the RobotAtFactory 4.0 competition. The approaches were validated using a realistically simulated scenario. Three algorithms were tested, and all were shown to be a good solution for a limited amount of data. Results also show that when the amount of data grows, only Multi-Layer Perception (MLP) is feasible for the embedded application due to the required training time and the resulting size of the model.por
dc.description.sponsorshipThis work has been supported by FCT - Fundac¸ ˜ao para a Ciˆencia e Tecnologia within the Project Scope: UIDB/05757/2020 and UIDP/05757/2020 and SusTEC (LA/P/0007/2021). The 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 PhD student at Faculty of Engineering of University of Porto.
dc.identifier.citationKlein, Luan C.; Braun, João; Martins, Felipe N.; Wörtche, Heinrich; Oliveira, Andre Schneider; Mendes, João; Pinto, Vítor H.; Costa, Paulo (2023). Using machine learning approaches to localization in an embedded system on RobotAtFactory 4.0 competition: a case study. In IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC). Tomar. p. p. 69-74. ISBN 979-835030121-2por
dc.identifier.doi10.1109/ICARSC58346.2023.10129619
dc.identifier.eissn2573-9387
dc.identifier.isbn979-835030121-2
dc.identifier.urihttp://hdl.handle.net/10198/7916
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherIEEEpor
dc.subjectIndoor Localizationpor
dc.subjectMachine Learningpor
dc.subjectRobotAtFactory 4.0por
dc.subjectRobotics Competitions
dc.subjectEmbedded systems
dc.titleUsing machine learning approaches to localization in an embedded system on RobotAtFactory 4.0 competition: a case studypor
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.titleIEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)por
person.familyNameBraun
person.familyNameMendes
person.givenNameJoão A.
person.givenNameJoão
person.identifier2726655
person.identifier.ciencia-idBF13-D66B-7D08
person.identifier.ciencia-idEA1F-844D-6BA9
person.identifier.orcid0000-0003-0276-4314
person.identifier.orcid0000-0003-0979-8314
person.identifier.scopus-author-id57211244317
person.identifier.scopus-author-id57225794972
rcaap.rightsopenAccesspor
rcaap.typeconferenceObjectpor
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relation.isAuthorOfPublication.latestForDiscoveryb5c9de22-cf9e-47b8-b7a4-26e08fb12b28

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