dc.contributor.author | Šumanas, Marius | |
dc.contributor.author | Bučinskas, Vytautas | |
dc.contributor.author | Morkvėnaitė-Vilkončienė, Inga | |
dc.contributor.author | Dzedzickis, Andrius | |
dc.contributor.author | Lenkutis, Tadas | |
dc.date.accessioned | 2023-09-18T18:58:14Z | |
dc.date.available | 2023-09-18T18:58:14Z | |
dc.date.issued | 2019 | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/133819 | |
dc.description.abstract | Machine 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.format | PDF | |
dc.format.extent | p. 1-3 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.relation.isreferencedby | Conference Proceedings Citation Index - Science (Web of Science) | |
dc.relation.isreferencedby | INSPEC | |
dc.relation.isreferencedby | IEEE Xplore | |
dc.relation.isreferencedby | Scopus | |
dc.title | Implementation of machine learning algorithms for autonomous robot trajectory resolving | |
dc.type | Straipsnis konferencijos darbų leidinyje Web of Science DB / Paper in conference publication in Web of Science DB | |
dcterms.references | 17 | |
dc.type.pubtype | P1a - Straipsnis konferencijos darbų leidinyje Web of Science DB / Article in conference proceedings Web of Science DB | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
dc.contributor.faculty | Mechanikos fakultetas / Faculty of Mechanics | |
dc.subject.researchfield | T 009 - Mechanikos inžinerija / Mechanical enginering | |
dc.subject.vgtuprioritizedfields | MC03 - Išmaniosios įterptinės sistemos / Smart embedded systems | |
dc.subject.ltspecializations | L104 - Nauji gamybos procesai, medžiagos ir technologijos / New production processes, materials and technologies | |
dc.subject.en | machine learning | |
dc.subject.en | trajectory generation | |
dc.subject.en | autonomous robotics | |
dc.subject.en | robot communication | |
dc.subject.en | robot teaching | |
dcterms.sourcetitle | 2019 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.name | IEEE | |
dc.publisher.city | New York | |
dc.identifier.doi | 2-s2.0-85068382484 | |
dc.identifier.doi | 000492889800021 | |
dc.identifier.doi | 10.1109/eStream.2019.8732166 | |
dc.identifier.elaba | 39722611 | |