Rodyti trumpą aprašą

dc.contributor.authorŠumanas, Marius
dc.contributor.authorBučinskas, Vytautas
dc.contributor.authorMorkvėnaitė-Vilkončienė, Inga
dc.contributor.authorDzedzickis, Andrius
dc.contributor.authorLenkutis, Tadas
dc.date.accessioned2023-09-18T18:58:14Z
dc.date.available2023-09-18T18:58:14Z
dc.date.issued2019
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/133819
dc.description.abstractMachine learning in the modern industry and transport became a hot issue and its implementation finding broad field in recent activities in industry and daily life. Robotic is widely using machine learning of different type for image recognizing and for robotic trajectory generation in unknown environment. This paper provides overview of existing feature and tries to predict trend of machine learning development in the robotics. Use of connection between units and machine learning features can improve intelligence and capabilities of robots for trajectory generation. Reviewed cases provides nice results, available on the field of robotic trajectory and brings some breakthrough movement towards fully autonomous devices. Finally, discussion and conclusions are provided in the paper.eng
dc.formatPDF
dc.format.extentp. 1-3
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyConference Proceedings Citation Index - Science (Web of Science)
dc.relation.isreferencedbyINSPEC
dc.relation.isreferencedbyIEEE Xplore
dc.relation.isreferencedbyScopus
dc.titleImplementation of machine learning algorithms for autonomous robot trajectory resolving
dc.typeStraipsnis konferencijos darbų leidinyje Web of Science DB / Paper in conference publication in Web of Science DB
dcterms.references17
dc.type.pubtypeP1a - Straipsnis konferencijos darbų leidinyje Web of Science DB / Article in conference proceedings Web of Science DB
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.facultyMechanikos fakultetas / Faculty of Mechanics
dc.subject.researchfieldT 009 - Mechanikos inžinerija / Mechanical enginering
dc.subject.vgtuprioritizedfieldsMC03 - Išmaniosios įterptinės sistemos / Smart embedded systems
dc.subject.ltspecializationsL104 - Nauji gamybos procesai, medžiagos ir technologijos / New production processes, materials and technologies
dc.subject.enmachine learning
dc.subject.entrajectory generation
dc.subject.enautonomous robotics
dc.subject.enrobot communication
dc.subject.enrobot teaching
dcterms.sourcetitle2019 Open Conference of Electrical, Electronic and Information Sciences (eStream), 25 April 2019, Vilnius, Lithuania : proceedings of the conference / organized by: Vilnius Gediminas Technical University
dc.publisher.nameIEEE
dc.publisher.cityNew York
dc.identifier.doi2-s2.0-85068382484
dc.identifier.doi000492889800021
dc.identifier.doi10.1109/eStream.2019.8732166
dc.identifier.elaba39722611


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