Show simple item record

dc.rights.licenseVisos teisės saugomos / All rights reserveden_US
dc.contributor.authorŠumanas, Marius
dc.contributor.authorPetronis, Algirdas
dc.contributor.authorBučinskas, Vytautas
dc.contributor.authorMacerauskas, Eugenijus
dc.contributor.authorMorkvėnaitė-Vilkončienė, Inga
dc.contributor.authorDzedzickis, Andrius
dc.date.accessioned2025-12-12T11:59:35Z
dc.date.available2025-12-12T11:59:35Z
dc.date.issued2020
dc.identifier.isbn9781728197807en_US
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/159535
dc.description.abstractImplementing of modern artificial intellect (AI) and machine learning (ML) for existing machinery can add value to their existing capabilities and technical characteristics. Machine learning is next step towards new innovations and stronger competitions in the market. Implementation of (ML) in the area of robotics requires some analysis of existing methods in order of correct of implemented method. This article sums up machine learning methods used in industry and presents successful implementation of deep Q-learning algorithm, implemented in robot static accuracy improvement using variable carrying load. Improvement reaches 0.07 mm for initial value equal to 0.1 mm. Finally, conclusions on implementing ML methods are drawn.en_US
dc.format.extent6 p.en_US
dc.format.mediumTekstas / Texten_US
dc.language.isoenen_US
dc.relation.urihttps://etalpykla.vilniustech.lt/handle/123456789/159395en_US
dc.source.urihttps://ieeexplore.ieee.org/document/9108858en_US
dc.subjectmachine learningen_US
dc.subjectindustryen_US
dc.subjectpositioning accuracyen_US
dc.subjectroboticsen_US
dc.subjectdeep Q-learningen_US
dc.titleImplementation of Machine Learning Method for Positioning Accuracy Improvement in Industrial Roboten_US
dc.typeKonferencijos publikacija / Conference paperen_US
dcterms.accrualMethodRankinis pateikimas / Manual submissionen_US
dcterms.issued2020-06-05
dcterms.references17en_US
dc.description.versionTaip / Yesen_US
dc.contributor.institutionVilniaus Gedimino technikos universitetasen_US
dc.contributor.institutionVilnius Gediminas Technical Universityen_US
dcterms.sourcetitle2020 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream), April 30, 2020, Vilnius, Lithuaniaen_US
dc.identifier.eisbn9781728197791en_US
dc.publisher.nameIEEEen_US
dc.publisher.countryUnited States of Americaen_US
dc.publisher.cityNew Yorken_US
dc.identifier.doihttps://doi.org/10.1109/eStream50540.2020.9108858en_US


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record