| dc.rights.license | Visos teisės saugomos / All rights reserved | en_US |
| dc.contributor.author | Šumanas, Marius | |
| dc.contributor.author | Bučinskas, Vytautas | |
| dc.contributor.author | Morkvėnaitė-Vilkončienė, Inga | |
| dc.contributor.author | Dzedziskis, Andrius | |
| dc.contributor.author | Lenkutis, Tadas | |
| dc.date.accessioned | 2025-12-10T11:00:14Z | |
| dc.date.available | 2025-12-10T11:00:14Z | |
| dc.date.issued | 2019 | |
| dc.identifier.isbn | 9781728125008 | en_US |
| dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/159511 | |
| 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. | en_US |
| dc.format.extent | 3 p. | en_US |
| dc.format.medium | Tekstas / Text | en_US |
| dc.language.iso | en | en_US |
| dc.relation.uri | https://etalpykla.vilniustech.lt/handle/123456789/159393 | en_US |
| dc.source.uri | https://ieeexplore.ieee.org/document/8732166 | en_US |
| dc.subject | machine learning | en_US |
| dc.subject | trajectory generation | en_US |
| dc.subject | autonomous robotics | en_US |
| dc.subject | robot communication | en_US |
| dc.subject | robot teaching | en_US |
| dc.title | Implementation of Machine Learning Algorithms for Autonomous Robot Trajectory Resolving | en_US |
| dc.type | Konferencijos publikacija / Conference paper | en_US |
| dcterms.accrualMethod | Rankinis pateikimas / Manual submission | en_US |
| dcterms.issued | 2019-06-06 | |
| dcterms.references | 17 | en_US |
| dc.description.version | Taip / Yes | en_US |
| dc.contributor.institution | Vilniaus Gedimino technikos universitetas | en_US |
| dc.contributor.institution | Vilnius Gediminas Technical University | en_US |
| dcterms.sourcetitle | 2019 Open Conference of Electrical, Electronic and Information Sciences (eStream), April 25, 2019, Vilnius, Lithuania | en_US |
| dc.identifier.eisbn | 9781728124995 | en_US |
| dc.publisher.name | IEEE | en_US |
| dc.publisher.country | United States of America | en_US |
| dc.publisher.city | New York | en_US |
| dc.description.fundingorganization | European Social Fund | en_US |
| dc.description.grantname | Development of Competences of Scientists, other Researchers and Students through Practical Research Activities | en_US |
| dc.description.grantnumber | 09.3.3-LMT-K-712 | en_US |
| dc.identifier.doi | https://doi.org/10.1109/eStream.2019.8732166 | en_US |