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A machine learning approach for collaborative robot smart manufacturing inspection for quality control systems1-10

2020Conference object Open access
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dc.contributor.authorBrito, Thadeu
dc.contributor.authorQueiroz, Jonas
dc.contributor.authorPiardi, Luis
dc.contributor.authorFernandes, Lucas de Azevedo
dc.contributor.authorLima, José
dc.contributor.authorLeitão, Paulo
dc.date.accessioned2022-01-12T14:16:26Z
dc.date.available2022-01-12T14:16:26Z
dc.date.issued2020
dc.description.abstractThe 4th industrial revolution promotes the automatic inspection of all products towards a zero-defect and high-quality manufacturing. In this context, collaborative robotics, where humans and machines share the same space, comprises a suitable approach that allows combining the accuracy of a robot and the ability and flexibility of a human. This paper describes an innovative approach that uses a collaborative robot to support the smart inspection and corrective actions for quality control systems in the manufacturing process, complemented by an intelligent system that learns and adapts its behavior according to the inspected parts. This intelligent system that implements the reinforcement learning algorithm makes the approach more robust once it can learn and be adapted to the trajectory. In the preliminary experiments, it was used a UR3 robot equipped with a Force-Torque sensor that was trained to perform a path regarding a product quality inspection task. © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the FAIM 2021.pt_PT
dc.description.sponsorshipThis work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationBrito, Thadeu; Queiroz, Jonas; Piardi, Luis; Fernandes, Lucas A.; Lima, José; Leitão, Paulo (2020). A machine learning approach for collaborative robot smart manufacturing inspection for quality control systems1-10. In 30th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM. Athens. p. 11-18pt_PT
dc.identifier.doi10.1016/j.promfg.2020.10.003pt_PT
dc.identifier.urihttp://hdl.handle.net/10198/24582
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectActor-criticpt_PT
dc.subjectCollaborative robotspt_PT
dc.subjectHuman-robot interactionpt_PT
dc.subjectQuality control systemspt_PT
dc.subjectReinforcement learningpt_PT
dc.subjectRobot learningpt_PT
dc.titleA machine learning approach for collaborative robot smart manufacturing inspection for quality control systems1-10pt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.endPage18pt_PT
oaire.citation.startPage11pt_PT
oaire.citation.title30th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2021pt_PT
oaire.citation.volume51pt_PT
person.familyNameBrito
person.familyNameQueiroz
person.familyNamePiardi
person.familyNameLima
person.familyNameLeitão
person.givenNameThadeu
person.givenNameJonas
person.givenNameLuís
person.givenNameJosé
person.givenNamePaulo
person.identifierBjSISEAAAAAJ
person.identifierhttps://scholar.google.com/citations?user=UnhjE9gAAAAJ
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person.identifier.orcid0000-0003-1627-8210
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person.identifier.ridL-3370-2014
person.identifier.scopus-author-id57200694948
person.identifier.scopus-author-id57188655139
person.identifier.scopus-author-id55851941311
person.identifier.scopus-author-id35584388900
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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