dc.creator | Jokić, Aleksandar | |
dc.creator | Petrović, Milica | |
dc.creator | Miljković, Zoran | |
dc.date.accessioned | 2023-02-09T09:35:27Z | |
dc.date.available | 2023-02-09T09:35:27Z | |
dc.date.issued | 2022 | |
dc.identifier.isbn | 978-86-7466-930-3 | |
dc.identifier.uri | https://machinery.mas.bg.ac.rs/handle/123456789/4230 | |
dc.description.abstract | One of the major aspects of Industry 4.0 is enabling
the manufacturing entities to operate in the dynamical systems
autonomously. Therefore, to be autonomous, manufacturing
entities need to have sensors to perceive their environment and
utilize that information to make decisions regarding their
actions. Having that in mind, in this paper, the authors propose a
mobile robot decision-making system based on the integration of
visual data and mobile robot pose. Mobile robot pose (current
position and orientation) is integrated with two images gathered
by two cameras and utilized to predict the possibility of gripping
the part to be manufactured. A decision-making system is
created by utilizing the deep learning model Resnet18 with an
additional input for the mobile robot pose. The model is trained
end-to-end and experimental evaluation is performed by using
the mobile robot RACIO (Robot with Artificial Intelligence
based COgnition). | sr |
dc.language.iso | en | sr |
dc.relation | info:eu-repo/grantAgreement/MESTD/inst-2020/200105/RS// | sr |
dc.relation | info:eu-repo/grantAgreement/ScienceFundRS/AI/6523109/RS// | sr |
dc.rights | openAccess | sr |
dc.source | Proceedings / 9th International Conference on Electrical, Electronics and Computing Engineering (IcETRAN 2022) Novi Pazar, Serbia, 6-9, June, 2022 | sr |
dc.subject | Decision-making system | sr |
dc.subject | Mobile robots | sr |
dc.subject | Deep learning | sr |
dc.title | Mobile robot decision-making system based on deep machine learning | sr |
dc.type | conferenceObject | sr |
dc.rights.license | ARR | sr |
dc.citation.epage | 638 | |
dc.citation.rank | M33 | |
dc.citation.spage | 635 | |
dc.identifier.fulltext | http://machinery.mas.bg.ac.rs/bitstream/id/9989/078-ROI1.1.pdf | |
dc.identifier.rcub | https://hdl.handle.net/21.15107/rcub_machinery_4230 | |
dc.type.version | publishedVersion | sr |