Task allocation and coordinated motion planning for autonomous multi-robot optical inspection systems

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dc.contributor.authorLiu, Yinhuaen
dc.contributor.authorZhao, Wenzhengen
dc.contributor.authorLutz, Timen
dc.contributor.authorYue, Xiaoweien
dc.date.accessioned2022-02-05T15:39:52Zen
dc.date.available2022-02-05T15:39:52Zen
dc.date.issued2021-07-02en
dc.date.updated2022-02-05T15:39:51Zen
dc.description.abstractAutonomous multi-robot optical inspection systems are increasingly applied for obtaining inline measurements in process monitoring and quality control. Numerous methods for path planning and robotic coordination have been developed for static and dynamic environments and applied to different fields. However, these approaches may not work for the autonomous multi-robot optical inspection system due to fast computation requirements of inline optimization, unique characteristics on robotic end-effector orientations, and complex large-scale free-form product surfaces. This paper proposes a novel task allocation methodology for coordinated motion planning of multi-robot inspection. Specifically, (1) a local robust inspection task allocation is proposed to achieve efficient and well-balanced measurement assignment among robots; (2) collision-free path planning and coordinated motion planning are developed via dynamic searching in robotic coordinate space and perturbation of probe poses or local paths in the conflicting robots. A case study shows that the proposed approach can mitigate the risk of collisions between robots and environments, resolve conflicts among robots, and reduce the inspection cycle time significantly and consistently.en
dc.description.versionAccepted versionen
dc.format.extent14 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1007/s10845-021-01803-1en
dc.identifier.eissn1572-8145en
dc.identifier.issn0956-5515en
dc.identifier.orcidYue, Xiaowei [0000-0001-6019-0940]en
dc.identifier.urihttp://hdl.handle.net/10919/108146en
dc.language.isoenen
dc.publisherSpringeren
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000669189700001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectTechnologyen
dc.subjectComputer Science, Artificial Intelligenceen
dc.subjectEngineering, Manufacturingen
dc.subjectComputer Scienceen
dc.subjectEngineeringen
dc.subjectOptical inspectionen
dc.subjectTask allocationen
dc.subjectMulti-roboten
dc.subjectCoordinated motion planningen
dc.subjectQuality controlen
dc.subjectMOBILE ROBOTSen
dc.subjectGENETIC ALGORITHMen
dc.subjectPATHen
dc.subjectOPTIMIZATIONen
dc.subjectIndustrial Engineering & Automationen
dc.subject0801 Artificial Intelligence and Image Processingen
dc.subject0899 Other Information and Computing Sciencesen
dc.subject0910 Manufacturing Engineeringen
dc.titleTask allocation and coordinated motion planning for autonomous multi-robot optical inspection systemsen
dc.title.serialJournal of Intelligent Manufacturingen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherEarly Accessen
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Industrial and Systems Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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