dc.contributor | Šibalija, Tatjana | |
dc.contributor | Davim, J. Paulo | |
dc.creator | Miljković, Zoran | |
dc.creator | Đokić, Lazar | |
dc.creator | Petrović, Milica | |
dc.date.accessioned | 2023-01-18T13:25:57Z | |
dc.date.available | 2023-01-18T13:25:57Z | |
dc.date.issued | 2021 | |
dc.identifier.isbn | 978-3-11-069317-1 | |
dc.identifier.uri | https://machinery.mas.bg.ac.rs/handle/123456789/3961 | |
dc.description.abstract | Intelligent mobile robots are foreseen as one of the possible solutions to efficiently
performing transportation and manipulation tasks in intelligent manufacturing
systems (IMS) of Industry 4.0. In the last few decades, deep learning models have been
recognized as a promising technique to enable the intelligent behavior of mobile robots
for performing such tasks. For the particular problems of object detection and classification,
a class of deep learning models, namely Convolutional Neural Networks (CNN),
is the most widely used. This chapter presents an application of Region-based
CNN (R-CNN) for advanced object identification tasks by using transfer learning.
The proposed learning approach is further used for the improvement of Image-
Based Visual Servoing (IBVS) algorithm used to control an intelligent mobile robot.
The proposed algorithms are implemented in the MATLAB software package, and
both simulation and the experimental verification of the proposed concept are performed
on intelligent mobile robot, DOMINO (Deep learning Omnidirectional Mobile
robot with INtelligent cOntrol). Four different CNN models are trained for object detection
and classification, and the most suitable CNN model is ResNet-18, with the
best recorded mean Average Precision (mAP) of 77%. Achieved experimental results
show the applicability of CNN for accurate detection and classification of different
manufacturing entities and the IBVS algorithm for efficient mobile robot control
within IMS. | sr |
dc.language.iso | en | sr |
dc.publisher | De Gruyter, © 2022 Walter de Gruyter GmbH, Berlin/Boston | sr |
dc.relation | info:eu-repo/grantAgreement/ScienceFundRS/AI/6523109/RS// | sr |
dc.rights | closedAccess | sr |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.source | Soft Computing in Smart Manufacturing - Solutions toward Industry 5.0 | sr |
dc.subject | intelligent manufacturing systems | sr |
dc.subject | intelligent mobile robots | sr |
dc.subject | deep learning | sr |
dc.subject | convolutional neural networks | sr |
dc.subject | visual servoing | sr |
dc.title | Application of convolutional neural networks for visual control of intelligent robotic systems | sr |
dc.type | bookPart | sr |
dc.rights.license | BY | sr |
dc.citation.rank | M13 | |
dc.citation.spage | 83/3 | |
dc.identifier.doi | 10.1515/9783110693225-003 | |
dc.type.version | publishedVersion | sr |