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dc.contributor.authorTranseth, Aksel Andreas
dc.contributor.authorStepanov, Alexey
dc.contributor.authorLinnerud, Ådne Solhaug
dc.contributor.authorEning, Klaus
dc.contributor.authorGjerstad, Tone Beate
dc.date.accessioned2023-03-09T13:05:39Z
dc.date.available2023-03-09T13:05:39Z
dc.date.created2020-08-27T11:46:24Z
dc.date.issued2020
dc.identifier.citationProceedings of 3rd International Symposium on Small-scale Intelligent Manufacturing Systems (SIMS). 2020.en_US
dc.identifier.isbn978-1-7281-6419-9
dc.identifier.urihttps://hdl.handle.net/11250/3057392
dc.description.abstractCompetitive robotized manufacturing of high specter variance, low volume product lines represents market opportunities for manufacturing companies, but cost-efficient production is challenging. In this paper, we present two main industry use cases which represent key challenges to be solved for cost-efficient low-volume, high-variance production. The use cases are found in collaboration with three manufacturing companies. We identify and describe these challenges which include perception and manipulation with shiny/high-reflectivity parts, human-machine interfaces for robot reconfiguration and calibration between simulated and real-world environments. In this paper, we present new methods for meeting these challenges: machine vision for handling sensor data with low quality in robot manipulation, automated robot programming based on CAD-models and automated calibration. Moreover, we implement and demonstrate the methods on the two identified industry use cases for robotized assembly.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.ispartofProceeding of 3rd International Symposium on Small-scale Intelligent Manufacturing Systems (SIMS2020)
dc.relation.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9121464&casa_token=fmxX-0VrHioAAAAA:_yxff6c9uyDbbp0gdde134AG1TEJignmcbWrbXcHHDXJCVTr5bMU8x8jXY-VulMfE3OG8CKeKV1s&tag=1
dc.titleCompetitive high variance, low volume manufacturing with robot manipulatorsen_US
dc.typeChapteren_US
dc.description.versionacceptedVersionen_US
dc.identifier.doi10.1109/SIMS49386.2020.9121464
dc.identifier.cristin1825490
dc.relation.projectNorges forskningsråd: 269747en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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