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dc.contributor.authorFrigstad, Fredrik
dc.date.accessioned2021-10-29T07:53:00Z
dc.date.available2021-10-29T07:53:00Z
dc.date.issued2021
dc.identifier.citationFrigstad, F. (2021) Bolts detection and a combination of conventional and reinforcement learning based control of UR5 industrial robot (Master's thesis). University of Agder, Grimstad.en_US
dc.identifier.urihttps://hdl.handle.net/11250/2826435
dc.descriptionMaster's thesis in Mechatronics (MAS500)en_US
dc.description.abstractThe main objective of this paper is to investigate the possibilities for using reinforcement learning to control a UR-5 robot. The paper also looks at how well reinforcement learning works to control a UR-5 robot. These questions are answered by constructing of matlab and simulink programes. Based on different mathworks example programs and scripts. In this study, reinforcement learning only works in the situation it is trained to perform. The author believe that it could work better if it were given other configurations/parameters. This will still be an interesting subject for further studies. According to the research done in this paper, the conventional control have the best control accuracy.en_US
dc.language.isoengen_US
dc.publisherUniversity of Agderen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.subjectMAS500en_US
dc.titleBolts detection and a combination of conventional and reinforcement learning based control of UR5 industrial roboten_US
dc.typeMaster thesisen_US
dc.rights.holder© 2021 Fredrik Frigstaden_US
dc.subject.nsiVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Teknisk kybernetikk: 553en_US
dc.source.pagenumber76en_US


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