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Deep Learning Framework for Controlling Work Sequence in Collaborative Human–Robot Assembly Processes

2023-01-03Artigo científico Acesso aberto
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dc.contributor.authorGarcia, Pedro P.
dc.contributor.authorSantos, Telmo G.
dc.contributor.authorMachado, Miguel A.
dc.contributor.authorMendes, Nuno
dc.contributor.institutionDEMI - Departamento de Engenharia Mecânica e Industrial
dc.contributor.institutionUNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial
dc.contributor.pblMDPI - Multidisciplinary Digital Publishing Institute
dc.date.accessioned2023-01-05T22:17:48Z
dc.date.available2023-01-05T22:17:48Z
dc.date.issued2023-01-03
dc.descriptionproject UIDB/EMS/00667/2020 (UNIDEMI)
dc.description.abstractThe human–robot collaboration (HRC) solutions presented so far have the disadvantage that the interaction between humans and robots is based on the human’s state or on specific gestures purposely performed by the human, thus increasing the time required to perform a task and slowing down the pace of human labor, making such solutions uninteresting. In this study, a different concept of the HRC system is introduced, consisting of an HRC framework for managing assembly processes that are executed simultaneously or individually by humans and robots. This HRC framework based on deep learning models uses only one type of data, RGB camera data, to make predictions about the collaborative workspace and human action, and consequently manage the assembly process. To validate the HRC framework, an industrial HRC demonstrator was built to assemble a mechanical component. Four different HRC frameworks were created based on the convolutional neural network (CNN) model structures: Faster R-CNN ResNet-50 and ResNet-101, YOLOv2 and YOLOv3. The HRC framework with YOLOv3 structure showed the best performance, showing a mean average performance of 72.26% and allowed the HRC industrial demonstrator to successfully complete all assembly tasks within a desired time window. The HRC framework has proven effective for industrial assembly applicationsen
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent18
dc.format.extent3907992
dc.identifier.doi10.3390/s23010553
dc.identifier.issn1424-8220
dc.identifier.otherPURE: 49789001
dc.identifier.otherPURE UUID: 19329988-61dc-4a69-820b-465646956242
dc.identifier.othercrossref: 10.3390/s23010553
dc.identifier.otherScopus: 85145966718
dc.identifier.otherWOS: 000908944300001
dc.identifier.otherPubMed: 36617153
dc.identifier.otherPubMedCentral: PMC9823442
dc.identifier.urihttp://hdl.handle.net/10362/147038
dc.identifier.urlhttps://www.mdpi.com/1424-8220/23/1/553
dc.language.isoeng
dc.peerreviewedyes
dc.subjectvisual assembly task recognition
dc.subjecthuman–robot collaborative assembly
dc.subjectonline class detection
dc.subjectdeep learning
dc.subjectAnalytical Chemistry
dc.subjectInformation Systems
dc.subjectAtomic and Molecular Physics, and Optics
dc.subjectBiochemistry
dc.subjectInstrumentation
dc.subjectElectrical and Electronic Engineering
dc.titleDeep Learning Framework for Controlling Work Sequence in Collaborative Human–Robot Assembly Processesen
dc.typejournal article
degois.publication.issue1
degois.publication.titleSensors
degois.publication.volume23
dspace.entity.typePublication
rcaap.rightsopenAccess

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