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Bin-picking solution for randomly placed automotive connectors based on machine learning techniques

2022Journal article Open access
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dc.contributor.authorTorres, Pedro
dc.contributor.authorArents, Janis
dc.contributor.authorMarques, Hugo
dc.contributor.authorMarques, Paulo
dc.date.accessioned2022-03-22T14:07:38Z
dc.date.available2022-03-22T14:07:38Z
dc.date.issued2022
dc.description.abstractThis paper presents the development of a bin-picking solution based on low-cost vision systems for the manipulation of automotive electrical connectors using machine learning techniques. The automotive sector has always been in a state of constant growth and change, which also implies constant challenges in the wire harnesses sector, and the emerging growth of electric cars is proof of this and represents a challenge for the industry. Traditionally, this sector is based on strong human work manufacturing and the need arises to make the digital transition, supported in the context of Industry 4.0, allowing the automation of processes and freeing operators for other activities with more added value. Depending on the car model and its feature packs, a connector can interface with a different number of wires, but the connector holes are the same. Holes not connected with wires need to be sealed, mainly to guarantee the tightness of the cable. Seals are inserted manually or, more recently, through robotic stations. Due to the huge variety of references and connector configurations, layout errors sometimes occur during seal insertion due to changed references or problems with the seal insertion machine. Consequently, faulty connectors are dumped into boxes, piling up different types of references. These connectors are not trash and need to be reused. This article proposes a bin-picking solution for classification, selection and separation, using a two-finger gripper, of these connectors for reuse in a new operation of removal and insertion of seals. Connectors are identified through a 3D vision system, consisting of an Intel RealSense camera for object depth information and the YOLOv5 algorithm for object classification. The advantage of this approach over other solutions is the ability to accurately detect and grasp small objects through a low-cost 3D camera even when the image resolution is low, benefiting from the power of machine learning algorithms.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationTORRES, P. [et al.] (2022) - Bin-picking solution for randomly placed automotive connectors based on machine learning techniques. Electronics DOI https://doi.org/10.3390/electronics11030476pt_PT
dc.identifier.doihttps://doi.org/10.3390/electronics11030476pt_PT
dc.identifier.issn2079-9292
dc.identifier.urihttp://hdl.handle.net/10400.11/7926
dc.language.isoengpt_PT
dc.peerreviewednopt_PT
dc.publisherGemma Piellapt_PT
dc.relation.ispartofseriesElectronics;
dc.relation.publisherversionhttps://www.mdpi.com/2079-9292/11/3/476#pt_PT
dc.subjectbin-pickingpt_PT
dc.subjectmachine learningpt_PT
dc.subjectroboticspt_PT
dc.subjectYOLOv5pt_PT
dc.subjectIndustry 4.0pt_PT
dc.titleBin-picking solution for randomly placed automotive connectors based on machine learning techniquespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceSwitzerlandpt_PT
oaire.citation.issue3pt_PT
oaire.citation.startPage476pt_PT
oaire.citation.titleElectronicspt_PT
oaire.citation.volume11pt_PT
person.familyNameBAPTISTA TORRES
person.familyNameMarques
person.familyNameMarques
person.givenNamePEDRO MIGUEL
person.givenNameHugo
person.givenNamePaulo
person.identifierK-5331-2015
person.identifier.ciencia-id2711-E707-519C
person.identifier.ciencia-id6313-B906-ED27
person.identifier.orcid0000-0003-4835-5022
person.identifier.orcid0000-0001-5762-4912
person.identifier.orcid0000-0002-1788-651X
person.identifier.scopus-author-id56261515100
person.identifier.scopus-author-id25225486200
person.identifier.scopus-author-id7006399225
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication9d9ad49f-3c45-4a99-be21-7f13965c2628
relation.isAuthorOfPublication5f8d6aed-47b9-4fa9-9205-dcbe5b6beae9
relation.isAuthorOfPublication5e02e874-d8e8-4a4d-9fe6-64741ff6bba7
relation.isAuthorOfPublication.latestForDiscovery5e02e874-d8e8-4a4d-9fe6-64741ff6bba7

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