TY  - CONF
AU  - Miljković, Zoran
AU  - Babić, Bojan
AU  - Petrović, Milica
AU  - Jokić, Aleksandar
AU  - Miljković, Katarina
AU  - Jevtić, Đorđe
AU  - Đokić, Lazar
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3969
AB  - Projekat MISSION4.0 podrazumevao je, u okviru nekoliko radnih paketa, razvoj inteligentnog stereo-vizuelnog upravljanja mobilnih robota, kao i optimalno planiranje i terminiranje tehnoloških procesa, i to baziranim na tehnikama veštačke inteligencije, posebno na konvolucionim veštačkim neuronskim mrežama i biološki inspirisanim algoritmima optimizacije. Tokom dvogodišnjih intenzivnih naučnih istraživanja razvijena je nova metodologija za autonomnu navigaciju i inteligentno upravljanje mobilnih robota sopstvenog razvoja, nazvanih RAICO i DOMINO. Generisanje optimalnog plana terminiranja tehnoloških procesa, u okviru koga se izvršava i inteligentni unutrašnji transport korišćenjem mobilnih robota, takođe je bio jedan od važnih ciljeva ovih naprednih istraživanja. U ovom radu, dat je pregled nekih od ključnih rezultata projekta MISSION4.0, poput publikovanih u vodećim međunarodnim i nacionalnim naučnim časopisima, objavljenih poglavlja u naučnim monografijama, saopštenih i odštampanih naučnih radova u zbornicima prestižnih konferencija održanih u inostranstvu i regionu, zatim u okviru verifikovanih tehničkih rešenja, kao i preko skupova podataka sa otvorenim pristupom.
C3  - 43. JUPITER Konferencija, 39. simpozijum „NU-ROBOTI-FTS“, Zbornik radova
T1  - Inteligentno stereo-vizuelno upravljanje mobilnih robota i optimalno terminiranje tehnoloških procesa - pregled rezultata istraživanja u okviru projekta MISSION4.0/Intelligent stereo-visual mobile robot control and optimal process planning and scheduling – overview of research results within the project MISSION4.0
SP  - 3.13-3.25
UR  - https://hdl.handle.net/21.15107/rcub_machinery_3969
ER  - 

@conference{
author = "Miljković, Zoran and Babić, Bojan and Petrović, Milica and Jokić, Aleksandar and Miljković, Katarina and Jevtić, Đorđe and Đokić, Lazar",
year = "2022",
abstract = "Projekat MISSION4.0 podrazumevao je, u okviru nekoliko radnih paketa, razvoj inteligentnog stereo-vizuelnog upravljanja mobilnih robota, kao i optimalno planiranje i terminiranje tehnoloških procesa, i to baziranim na tehnikama veštačke inteligencije, posebno na konvolucionim veštačkim neuronskim mrežama i biološki inspirisanim algoritmima optimizacije. Tokom dvogodišnjih intenzivnih naučnih istraživanja razvijena je nova metodologija za autonomnu navigaciju i inteligentno upravljanje mobilnih robota sopstvenog razvoja, nazvanih RAICO i DOMINO. Generisanje optimalnog plana terminiranja tehnoloških procesa, u okviru koga se izvršava i inteligentni unutrašnji transport korišćenjem mobilnih robota, takođe je bio jedan od važnih ciljeva ovih naprednih istraživanja. U ovom radu, dat je pregled nekih od ključnih rezultata projekta MISSION4.0, poput publikovanih u vodećim međunarodnim i nacionalnim naučnim časopisima, objavljenih poglavlja u naučnim monografijama, saopštenih i odštampanih naučnih radova u zbornicima prestižnih konferencija održanih u inostranstvu i regionu, zatim u okviru verifikovanih tehničkih rešenja, kao i preko skupova podataka sa otvorenim pristupom.",
journal = "43. JUPITER Konferencija, 39. simpozijum „NU-ROBOTI-FTS“, Zbornik radova",
title = "Inteligentno stereo-vizuelno upravljanje mobilnih robota i optimalno terminiranje tehnoloških procesa - pregled rezultata istraživanja u okviru projekta MISSION4.0/Intelligent stereo-visual mobile robot control and optimal process planning and scheduling – overview of research results within the project MISSION4.0",
pages = "3.13-3.25",
url = "https://hdl.handle.net/21.15107/rcub_machinery_3969"
}

Miljković, Z., Babić, B., Petrović, M., Jokić, A., Miljković, K., Jevtić, Đ.,& Đokić, L.. (2022). Inteligentno stereo-vizuelno upravljanje mobilnih robota i optimalno terminiranje tehnoloških procesa - pregled rezultata istraživanja u okviru projekta MISSION4.0/Intelligent stereo-visual mobile robot control and optimal process planning and scheduling – overview of research results within the project MISSION4.0. in 43. JUPITER Konferencija, 39. simpozijum „NU-ROBOTI-FTS“, Zbornik radova, 3.13-3.25.
https://hdl.handle.net/21.15107/rcub_machinery_3969

Miljković Z, Babić B, Petrović M, Jokić A, Miljković K, Jevtić Đ, Đokić L. Inteligentno stereo-vizuelno upravljanje mobilnih robota i optimalno terminiranje tehnoloških procesa - pregled rezultata istraživanja u okviru projekta MISSION4.0/Intelligent stereo-visual mobile robot control and optimal process planning and scheduling – overview of research results within the project MISSION4.0. in 43. JUPITER Konferencija, 39. simpozijum „NU-ROBOTI-FTS“, Zbornik radova. 2022;:3.13-3.25.
https://hdl.handle.net/21.15107/rcub_machinery_3969 .

Miljković, Zoran, Babić, Bojan, Petrović, Milica, Jokić, Aleksandar, Miljković, Katarina, Jevtić, Đorđe, Đokić, Lazar, "Inteligentno stereo-vizuelno upravljanje mobilnih robota i optimalno terminiranje tehnoloških procesa - pregled rezultata istraživanja u okviru projekta MISSION4.0/Intelligent stereo-visual mobile robot control and optimal process planning and scheduling – overview of research results within the project MISSION4.0" in 43. JUPITER Konferencija, 39. simpozijum „NU-ROBOTI-FTS“, Zbornik radova (2022):3.13-3.25,
https://hdl.handle.net/21.15107/rcub_machinery_3969 .

TY  - JOUR
AU  - Jokić, Aleksandar
AU  - Đokić, Lazar
AU  - Petrović, Milica
AU  - Miljković, Zoran
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3967
AB  - Data augmentation has become a standard technique for increasing deep learning models’ accuracy and robustness. Different pixel intensity modifications, image transformations, and noise additions represent the most utilized data augmentation methods. In this paper, a comprehensive evaluation of data augmentation techniques for mobile robot perception system is performed. The perception system based on a deep learning model for semantic segmentation is augmented by 17 techniques to obtain better generalization characteristics during the training process. The deep learning model is trained and tested on a custom dataset and utilized in real-time scenarios. The experimental results show the increment of 6.2 in mIoU (mean Intersection over Union) for the best combination of data augmentation strategies.
T2  - Serbian Journal of Electrical Engineering
T1  - Data Augmentation Methods for Semantic Segmentation-based Mobile Robot Perception System
IS  - 3
SP  - 291-302
VL  - 19
DO  - https://doi.org/10.2298/SJEE2203291J
ER  - 

@article{
author = "Jokić, Aleksandar and Đokić, Lazar and Petrović, Milica and Miljković, Zoran",
year = "2022",
abstract = "Data augmentation has become a standard technique for increasing deep learning models’ accuracy and robustness. Different pixel intensity modifications, image transformations, and noise additions represent the most utilized data augmentation methods. In this paper, a comprehensive evaluation of data augmentation techniques for mobile robot perception system is performed. The perception system based on a deep learning model for semantic segmentation is augmented by 17 techniques to obtain better generalization characteristics during the training process. The deep learning model is trained and tested on a custom dataset and utilized in real-time scenarios. The experimental results show the increment of 6.2 in mIoU (mean Intersection over Union) for the best combination of data augmentation strategies.",
journal = "Serbian Journal of Electrical Engineering",
title = "Data Augmentation Methods for Semantic Segmentation-based Mobile Robot Perception System",
number = "3",
pages = "291-302",
volume = "19",
doi = "https://doi.org/10.2298/SJEE2203291J"
}

Jokić, A., Đokić, L., Petrović, M.,& Miljković, Z.. (2022). Data Augmentation Methods for Semantic Segmentation-based Mobile Robot Perception System. in Serbian Journal of Electrical Engineering, 19(3), 291-302.
https://doi.org/https://doi.org/10.2298/SJEE2203291J

Jokić A, Đokić L, Petrović M, Miljković Z. Data Augmentation Methods for Semantic Segmentation-based Mobile Robot Perception System. in Serbian Journal of Electrical Engineering. 2022;19(3):291-302.
doi:https://doi.org/10.2298/SJEE2203291J .

Jokić, Aleksandar, Đokić, Lazar, Petrović, Milica, Miljković, Zoran, "Data Augmentation Methods for Semantic Segmentation-based Mobile Robot Perception System" in Serbian Journal of Electrical Engineering, 19, no. 3 (2022):291-302,
https://doi.org/https://doi.org/10.2298/SJEE2203291J . .

TY  - CHAP
AU  - Miljković, Zoran
AU  - Đokić, Lazar
AU  - Petrović, Milica
PY  - 2021
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3961
AB  - 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.
PB  - De Gruyter, © 2022 Walter de Gruyter GmbH, Berlin/Boston
T2  - Soft Computing in Smart Manufacturing - Solutions toward Industry 5.0
T1  - Application of convolutional neural networks for visual control of intelligent robotic systems
SP  - 83/3
DO  - 10.1515/9783110693225-003
ER  - 

@inbook{
author = "Miljković, Zoran and Đokić, Lazar and Petrović, Milica",
year = "2021",
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.",
publisher = "De Gruyter, © 2022 Walter de Gruyter GmbH, Berlin/Boston",
journal = "Soft Computing in Smart Manufacturing - Solutions toward Industry 5.0",
booktitle = "Application of convolutional neural networks for visual control of intelligent robotic systems",
pages = "83/3",
doi = "10.1515/9783110693225-003"
}

Miljković, Z., Đokić, L.,& Petrović, M.. (2021). Application of convolutional neural networks for visual control of intelligent robotic systems. in Soft Computing in Smart Manufacturing - Solutions toward Industry 5.0
De Gruyter, © 2022 Walter de Gruyter GmbH, Berlin/Boston., 83/3.
https://doi.org/10.1515/9783110693225-003

Miljković Z, Đokić L, Petrović M. Application of convolutional neural networks for visual control of intelligent robotic systems. in Soft Computing in Smart Manufacturing - Solutions toward Industry 5.0. 2021;:83/3.
doi:10.1515/9783110693225-003 .

Miljković, Zoran, Đokić, Lazar, Petrović, Milica, "Application of convolutional neural networks for visual control of intelligent robotic systems" in Soft Computing in Smart Manufacturing - Solutions toward Industry 5.0 (2021):83/3,
https://doi.org/10.1515/9783110693225-003 . .

TY  - CONF
AU  - Miljković, Zoran
AU  - Đokić, Lazar
AU  - Petrović, Milica
PY  - 2021
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4471
AB  - Contemporary manufacturing systems imply the utilization of autonomous robotic systems, mainly for the execution of manipulation and transportation tasks. With a goal to reduce transportation and manipulation time, improve efficiency, and achieve flexibility of intelligent manufacturing systems, two or more intelligent mobile robots can be exploited. Such multi-robot systems require coordination and some level of communication between heterogeneous or homogeneous robotic systems. In this paper, we propose the utilization of two heterogeneous robotic systems, original intelligent mobile robots RAICO (Robot with Artificial Intelligence based COgnition) and DOMINO (Deep learning-based Omnidirectional Mobile robot with Intelligent cOntrol), for transportation tasks within a laboratory model of a manufacturing environment. In order to reach an adequate cooperation level and avoid collision while moving along predefined paths, our own developed intelligent mobile robots RAICO and DOMINO will communicate their current poses, and object detection and tracking system is developed. A stereo vision system equipped with two parallelly placed industrial-grade cameras is used for image acquisition, while convolutional neural networks are utilized for object detection, classification, and tracking. The proposed object detection and tracking system enables real-time tracking of another mobile robot within the same manufacturing environment. Furthermore, continuous information about mobile robot poses and the size of the bounding box generated by the convolutional neural network in the process of detection of another mobile robot is used for estimation of object movement and collision avoidance. Mobile robot localization through time is performed based on kinematic models of two intelligent mobile robots, and conducted experiments within a laboratory model of manufacturing environment confirm the applicability of the proposed framework for object detection and collision avoidance.
PB  - University of Kragujevac, Faculty of Technical Sciences Čačak
C3  - Proceedings of the 38th International Conference on Production Engineering - ICPE-S 2021, 14 – 15. October 2021, Čačak, Serbia
T1  - Object Detection and Tracking in Cooperative Multi-Robot Transportation
EP  - 143
SP  - 137
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4471
ER  - 

@conference{
author = "Miljković, Zoran and Đokić, Lazar and Petrović, Milica",
year = "2021",
abstract = "Contemporary manufacturing systems imply the utilization of autonomous robotic systems, mainly for the execution of manipulation and transportation tasks. With a goal to reduce transportation and manipulation time, improve efficiency, and achieve flexibility of intelligent manufacturing systems, two or more intelligent mobile robots can be exploited. Such multi-robot systems require coordination and some level of communication between heterogeneous or homogeneous robotic systems. In this paper, we propose the utilization of two heterogeneous robotic systems, original intelligent mobile robots RAICO (Robot with Artificial Intelligence based COgnition) and DOMINO (Deep learning-based Omnidirectional Mobile robot with Intelligent cOntrol), for transportation tasks within a laboratory model of a manufacturing environment. In order to reach an adequate cooperation level and avoid collision while moving along predefined paths, our own developed intelligent mobile robots RAICO and DOMINO will communicate their current poses, and object detection and tracking system is developed. A stereo vision system equipped with two parallelly placed industrial-grade cameras is used for image acquisition, while convolutional neural networks are utilized for object detection, classification, and tracking. The proposed object detection and tracking system enables real-time tracking of another mobile robot within the same manufacturing environment. Furthermore, continuous information about mobile robot poses and the size of the bounding box generated by the convolutional neural network in the process of detection of another mobile robot is used for estimation of object movement and collision avoidance. Mobile robot localization through time is performed based on kinematic models of two intelligent mobile robots, and conducted experiments within a laboratory model of manufacturing environment confirm the applicability of the proposed framework for object detection and collision avoidance.",
publisher = "University of Kragujevac, Faculty of Technical Sciences Čačak",
journal = "Proceedings of the 38th International Conference on Production Engineering - ICPE-S 2021, 14 – 15. October 2021, Čačak, Serbia",
title = "Object Detection and Tracking in Cooperative Multi-Robot Transportation",
pages = "143-137",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4471"
}

Miljković, Z., Đokić, L.,& Petrović, M.. (2021). Object Detection and Tracking in Cooperative Multi-Robot Transportation. in Proceedings of the 38th International Conference on Production Engineering - ICPE-S 2021, 14 – 15. October 2021, Čačak, Serbia
University of Kragujevac, Faculty of Technical Sciences Čačak., 137-143.
https://hdl.handle.net/21.15107/rcub_machinery_4471

Miljković Z, Đokić L, Petrović M. Object Detection and Tracking in Cooperative Multi-Robot Transportation. in Proceedings of the 38th International Conference on Production Engineering - ICPE-S 2021, 14 – 15. October 2021, Čačak, Serbia. 2021;:137-143.
https://hdl.handle.net/21.15107/rcub_machinery_4471 .

Miljković, Zoran, Đokić, Lazar, Petrović, Milica, "Object Detection and Tracking in Cooperative Multi-Robot Transportation" in Proceedings of the 38th International Conference on Production Engineering - ICPE-S 2021, 14 – 15. October 2021, Čačak, Serbia (2021):137-143,
https://hdl.handle.net/21.15107/rcub_machinery_4471 .

TY  - CONF
AU  - Jokić, Aleksandar
AU  - Đokić, Lazar
AU  - Petrović, Milica
AU  - Miljković, Zoran
PY  - 2021
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4233
AB  - In this paper, we present the novel mobile robot
perception system based on a deep learning framework. The
hardware subsystem consists of an Nvidia Jetson Nano
development board integrated with two parallelly positioned
Basler daA1600-60uc cameras, while the software subsystem is
based on the convolutional neural networks utilized for semantic
segmentation of the environment scene. A Fully Convolutional
neural Network (FCN) based on the ResNet18 backbone
architecture is utilized to provide accurate information about
machine tool models and background position in the image. FCN
model is trained on our custom-developed dataset of a laboratory
model of manufacturing environment and implemented on
mobile robot RAICO (Robot with Artificial Intelligence based
COgnition).
PB  - Belgrade : Društvo za ETRAN
PB  - Beograd : Akademska misao
C3  - Зборник радова ‐ 65. Конференција за електронику, телекомуникације, рачунарство, аутоматику и нуклеарну технику, Етно село Станишићи, 08‐10.09.2021. године / Proceedings of Papers – 8th International Conference on Electrical, Electronic and Computing Engineering, IcETRAN 2021, Ethno willage Stanišići, Republic of Srpska, Bosnia and Herzegovina, 2021
T1  - A Mobile Robot Visual Perception System based on Deep Learning Approach
EP  - 572
SP  - 568
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4233
ER  - 

@conference{
author = "Jokić, Aleksandar and Đokić, Lazar and Petrović, Milica and Miljković, Zoran",
year = "2021",
abstract = "In this paper, we present the novel mobile robot
perception system based on a deep learning framework. The
hardware subsystem consists of an Nvidia Jetson Nano
development board integrated with two parallelly positioned
Basler daA1600-60uc cameras, while the software subsystem is
based on the convolutional neural networks utilized for semantic
segmentation of the environment scene. A Fully Convolutional
neural Network (FCN) based on the ResNet18 backbone
architecture is utilized to provide accurate information about
machine tool models and background position in the image. FCN
model is trained on our custom-developed dataset of a laboratory
model of manufacturing environment and implemented on
mobile robot RAICO (Robot with Artificial Intelligence based
COgnition).",
publisher = "Belgrade : Društvo za ETRAN, Beograd : Akademska misao",
journal = "Зборник радова ‐ 65. Конференција за електронику, телекомуникације, рачунарство, аутоматику и нуклеарну технику, Етно село Станишићи, 08‐10.09.2021. године / Proceedings of Papers – 8th International Conference on Electrical, Electronic and Computing Engineering, IcETRAN 2021, Ethno willage Stanišići, Republic of Srpska, Bosnia and Herzegovina, 2021",
title = "A Mobile Robot Visual Perception System based on Deep Learning Approach",
pages = "572-568",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4233"
}

Jokić, A., Đokić, L., Petrović, M.,& Miljković, Z.. (2021). A Mobile Robot Visual Perception System based on Deep Learning Approach. in Зборник радова ‐ 65. Конференција за електронику, телекомуникације, рачунарство, аутоматику и нуклеарну технику, Етно село Станишићи, 08‐10.09.2021. године / Proceedings of Papers – 8th International Conference on Electrical, Electronic and Computing Engineering, IcETRAN 2021, Ethno willage Stanišići, Republic of Srpska, Bosnia and Herzegovina, 2021
Belgrade : Društvo za ETRAN., 568-572.
https://hdl.handle.net/21.15107/rcub_machinery_4233

Jokić A, Đokić L, Petrović M, Miljković Z. A Mobile Robot Visual Perception System based on Deep Learning Approach. in Зборник радова ‐ 65. Конференција за електронику, телекомуникације, рачунарство, аутоматику и нуклеарну технику, Етно село Станишићи, 08‐10.09.2021. године / Proceedings of Papers – 8th International Conference on Electrical, Electronic and Computing Engineering, IcETRAN 2021, Ethno willage Stanišići, Republic of Srpska, Bosnia and Herzegovina, 2021. 2021;:568-572.
https://hdl.handle.net/21.15107/rcub_machinery_4233 .

Jokić, Aleksandar, Đokić, Lazar, Petrović, Milica, Miljković, Zoran, "A Mobile Robot Visual Perception System based on Deep Learning Approach" in Зборник радова ‐ 65. Конференција за електронику, телекомуникације, рачунарство, аутоматику и нуклеарну технику, Етно село Станишићи, 08‐10.09.2021. године / Proceedings of Papers – 8th International Conference on Electrical, Electronic and Computing Engineering, IcETRAN 2021, Ethno willage Stanišići, Republic of Srpska, Bosnia and Herzegovina, 2021 (2021):568-572,
https://hdl.handle.net/21.15107/rcub_machinery_4233 .

TY  - JOUR
AU  - Đokić, Lazar
AU  - Jokić, Aleksandar
AU  - Petrović, Milica
AU  - Slavković, Nikola
AU  - Miljković, Zoran
PY  - 2021
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3663
AB  - Visual Servoing (VS) of a mobile robot requires advanced digital image processing, and one of the techniques especially fitting for this complex task is Image Registration (IR). In general, IR involves the geometrical alignment of images, and it can be viewed as an optimization problem. Therefore, we propose Metaheuristic Optimization Algorithms (MOA) for IR in VS of a mobile robot. The comprehensive comparison study of three state-of-the-art MOA, namely the Slime Mould Algorithm (SMA), Harris Hawks Optimizer (HHO), and Whale Optimization Algorithm (WOA) is presented. The previously mentioned MOA used for IR are evaluated on 12 pairs of stereo images obtained by a mobile robot stereo vision system in a laboratory model of a manufacturing environment. The MATLAB software package is used for the implementation of the considered optimization algorithms. Acquired experimental results show that SMA outperforms HHO and WOA, while all three algorithms perform satisfactory alignment of images captured from various mobile robot poses.
PB  - Univerzitet u Kragujevcu - Fakultet tehničkih nauka, Čačak
T2  - Serbian Journal of Electrical Engineering
T1  - Application of metaheuristic optimization algorithms for image registration in mobile robot visual control
EP  - 170
IS  - 2
SP  - 155
VL  - 18
DO  - 10.2298/SJEE2102155D
ER  - 

@article{
author = "Đokić, Lazar and Jokić, Aleksandar and Petrović, Milica and Slavković, Nikola and Miljković, Zoran",
year = "2021",
abstract = "Visual Servoing (VS) of a mobile robot requires advanced digital image processing, and one of the techniques especially fitting for this complex task is Image Registration (IR). In general, IR involves the geometrical alignment of images, and it can be viewed as an optimization problem. Therefore, we propose Metaheuristic Optimization Algorithms (MOA) for IR in VS of a mobile robot. The comprehensive comparison study of three state-of-the-art MOA, namely the Slime Mould Algorithm (SMA), Harris Hawks Optimizer (HHO), and Whale Optimization Algorithm (WOA) is presented. The previously mentioned MOA used for IR are evaluated on 12 pairs of stereo images obtained by a mobile robot stereo vision system in a laboratory model of a manufacturing environment. The MATLAB software package is used for the implementation of the considered optimization algorithms. Acquired experimental results show that SMA outperforms HHO and WOA, while all three algorithms perform satisfactory alignment of images captured from various mobile robot poses.",
publisher = "Univerzitet u Kragujevcu - Fakultet tehničkih nauka, Čačak",
journal = "Serbian Journal of Electrical Engineering",
title = "Application of metaheuristic optimization algorithms for image registration in mobile robot visual control",
pages = "170-155",
number = "2",
volume = "18",
doi = "10.2298/SJEE2102155D"
}

Đokić, L., Jokić, A., Petrović, M., Slavković, N.,& Miljković, Z.. (2021). Application of metaheuristic optimization algorithms for image registration in mobile robot visual control. in Serbian Journal of Electrical Engineering
Univerzitet u Kragujevcu - Fakultet tehničkih nauka, Čačak., 18(2), 155-170.
https://doi.org/10.2298/SJEE2102155D

Đokić L, Jokić A, Petrović M, Slavković N, Miljković Z. Application of metaheuristic optimization algorithms for image registration in mobile robot visual control. in Serbian Journal of Electrical Engineering. 2021;18(2):155-170.
doi:10.2298/SJEE2102155D .

Đokić, Lazar, Jokić, Aleksandar, Petrović, Milica, Slavković, Nikola, Miljković, Zoran, "Application of metaheuristic optimization algorithms for image registration in mobile robot visual control" in Serbian Journal of Electrical Engineering, 18, no. 2 (2021):155-170,
https://doi.org/10.2298/SJEE2102155D . .

TY  - CONF
AU  - Đokić, Lazar
AU  - Jokić, Aleksandar
AU  - Petrović, Milica
AU  - Miljković, Zoran
PY  - 2020
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4235
AB  - Image registration (IR) represents image processing
technique that is suitable for use in Visual Servoing (VS). This
paper proposes the use of Biologically Inspired Optimization
(BIO) methods for IR in VS of nonholonomic mobile robot. The
comparison study of three different BIO methods is conducted,
namely Genetic Algorithm (GA), Particle Swarm Optimization
(PSO), and Grey Wolf Optimizer (GWO). The aforementioned
optimization algorithms utilized for IR are tested on 24 images of
manufacturing entities acquired by mobile robot stereo vision
system. The considered algorithms are implemented in the
MATLAB environment. The experimental results suggest
satisfactory geometrical alignment after IR, whilst GA and PSO
outperform GWO.
C3  - Proceedings : 7th International Conference on Electrical, Electronics and Computing Engineering (IcETRAN 2020), 28-29. September 2020
T1  - Biologically Inspired Optimization Methods for Image Registration in Visual Servoing of a Mobile Robot
EP  - 720
SP  - 715
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4235
ER  - 

@conference{
author = "Đokić, Lazar and Jokić, Aleksandar and Petrović, Milica and Miljković, Zoran",
year = "2020",
abstract = "Image registration (IR) represents image processing
technique that is suitable for use in Visual Servoing (VS). This
paper proposes the use of Biologically Inspired Optimization
(BIO) methods for IR in VS of nonholonomic mobile robot. The
comparison study of three different BIO methods is conducted,
namely Genetic Algorithm (GA), Particle Swarm Optimization
(PSO), and Grey Wolf Optimizer (GWO). The aforementioned
optimization algorithms utilized for IR are tested on 24 images of
manufacturing entities acquired by mobile robot stereo vision
system. The considered algorithms are implemented in the
MATLAB environment. The experimental results suggest
satisfactory geometrical alignment after IR, whilst GA and PSO
outperform GWO.",
journal = "Proceedings : 7th International Conference on Electrical, Electronics and Computing Engineering (IcETRAN 2020), 28-29. September 2020",
title = "Biologically Inspired Optimization Methods for Image Registration in Visual Servoing of a Mobile Robot",
pages = "720-715",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4235"
}

Đokić, L., Jokić, A., Petrović, M.,& Miljković, Z.. (2020). Biologically Inspired Optimization Methods for Image Registration in Visual Servoing of a Mobile Robot. in Proceedings : 7th International Conference on Electrical, Electronics and Computing Engineering (IcETRAN 2020), 28-29. September 2020, 715-720.
https://hdl.handle.net/21.15107/rcub_machinery_4235

Đokić L, Jokić A, Petrović M, Miljković Z. Biologically Inspired Optimization Methods for Image Registration in Visual Servoing of a Mobile Robot. in Proceedings : 7th International Conference on Electrical, Electronics and Computing Engineering (IcETRAN 2020), 28-29. September 2020. 2020;:715-720.
https://hdl.handle.net/21.15107/rcub_machinery_4235 .

Đokić, Lazar, Jokić, Aleksandar, Petrović, Milica, Miljković, Zoran, "Biologically Inspired Optimization Methods for Image Registration in Visual Servoing of a Mobile Robot" in Proceedings : 7th International Conference on Electrical, Electronics and Computing Engineering (IcETRAN 2020), 28-29. September 2020 (2020):715-720,
https://hdl.handle.net/21.15107/rcub_machinery_4235 .

TY  - CONF
AU  - Đokić, Lazar
AU  - Jokić, Aleksandar
AU  - Petrović, Milica
AU  - Miljković, Zoran
PY  - 2019
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4237
AB  - U cilju ostvarivanja efikasnog i pouzdanog transportnog sistema, savremeni tehnološki
sistemi se baziraju na upotrebi inteligentnih mobilnih robota za manipulaciju i unutrašnji transport
materijala. Da bi se smanjila neodređenost u okviru dinamičkog tehnološkog okruženja, mobilni
roboti su opremljeni sa stereo vizuelnim sistemom, pomoću koga pouzadno mogu da ocene
udaljenost tehnoloških entiteta. U ovom radu, predstavljen je novi algoritam za stereo vizuelno
upravljanje neholonomnog mobilnog robota. Glavni upravljački algoritam, zasnovan na računanju
greške u parametrima slike (IBVS - Image based visual servoing), koristi se za tačno pozicioniranje
mobilnog robota u željeni položaj. Da bi se izračunale greške u parametrima slike, koristi se
algoritam za identifikaciju karakterisitčnih objekata na trenutnim i ciljnim slikama. Poređenje ovih
algoritama izvršeno je na setu slika laboratorijskog modela tehnološkog okruženja, čija je akvizicija
izvršena kamerama Basler acA1920-25uc. Na osnovu rezultata poređenja, KAZE algoritam za
identifikaciju karakterističnih objekata je pokazao najbolje performance. Da bi se testirao i
verifikovao rad stereo vizuelnog upravljačkog sistema, pored simulacije, izvršena su i dva
eksperimenta na mobilnom robotu RAICO (Robot with Artificial Intelligence based COgnition) u
laboratorijskom modelu tehnološkog okruženja. Eksperimentalni rezultati pokazuju efikasnost
predloženog stereo vizuelnog upravljačkog sistema u ostvarivanju željenog položaja mobilnog
robota, uz minimalnu ostvarenu grešku.
AB  - Requirements for an effective and reliable material transport system within advanced
manufacturing environment can be fulfilled by using intelligent mobile robots to perform material
handling and transportation tasks. In order to reduce the degree of ambiguity occurring in a
dynamic manufacturing environment, mobile robots are equipped with a stereo vision system that
can reliably estimate distance to manufacturing entities. In this paper, a new stereo vision-based
algorithm for control of nonholonomic mobile robot is proposed. The main control algorithm, based
on an error in image parameters (IBVS - Image based visual servoing), is used for positioning of a
mobile robot in the desired location. For estimation of the error in image parameters, point features
are extracted from the current and target camera view via feature detection and description
algorithm. A comparison of these algorithms is made on a set of images obtained in laboratory
model of the manufacturing environment by using Basler acA1920-25uc cameras. Based on the
results of comparison, KAZE feature detection and description algorithm is proven to be best suited
for this specific case. In order to verify the stereo visual control system, simulation and real-world
experiments are performed. Two experiments are conducted on a mobile robot RAICO (Robot with
Artificial Intelligence based COgnition) in a laboratory model of the manufacturing environment.
Experimental results show the effectiveness of the proposed stereo visual control system and its
applicability in reaching the desired location with minimal accuracy error.
C3  - Proceedings of selected papers and abstracts of the The Third International Students Scientific Conference "Multidisciplinary Approach to Contemporary Research - Cultural and Industrial Heritage", Belgrade, 21-22.12. 2019
T1  - Stereo vision-based algorithm for control of nonholonomic mobile robot
EP  - 82
SP  - 69
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4237
ER  - 

@conference{
author = "Đokić, Lazar and Jokić, Aleksandar and Petrović, Milica and Miljković, Zoran",
year = "2019",
abstract = "U cilju ostvarivanja efikasnog i pouzdanog transportnog sistema, savremeni tehnološki
sistemi se baziraju na upotrebi inteligentnih mobilnih robota za manipulaciju i unutrašnji transport
materijala. Da bi se smanjila neodređenost u okviru dinamičkog tehnološkog okruženja, mobilni
roboti su opremljeni sa stereo vizuelnim sistemom, pomoću koga pouzadno mogu da ocene
udaljenost tehnoloških entiteta. U ovom radu, predstavljen je novi algoritam za stereo vizuelno
upravljanje neholonomnog mobilnog robota. Glavni upravljački algoritam, zasnovan na računanju
greške u parametrima slike (IBVS - Image based visual servoing), koristi se za tačno pozicioniranje
mobilnog robota u željeni položaj. Da bi se izračunale greške u parametrima slike, koristi se
algoritam za identifikaciju karakterisitčnih objekata na trenutnim i ciljnim slikama. Poređenje ovih
algoritama izvršeno je na setu slika laboratorijskog modela tehnološkog okruženja, čija je akvizicija
izvršena kamerama Basler acA1920-25uc. Na osnovu rezultata poređenja, KAZE algoritam za
identifikaciju karakterističnih objekata je pokazao najbolje performance. Da bi se testirao i
verifikovao rad stereo vizuelnog upravljačkog sistema, pored simulacije, izvršena su i dva
eksperimenta na mobilnom robotu RAICO (Robot with Artificial Intelligence based COgnition) u
laboratorijskom modelu tehnološkog okruženja. Eksperimentalni rezultati pokazuju efikasnost
predloženog stereo vizuelnog upravljačkog sistema u ostvarivanju željenog položaja mobilnog
robota, uz minimalnu ostvarenu grešku., Requirements for an effective and reliable material transport system within advanced
manufacturing environment can be fulfilled by using intelligent mobile robots to perform material
handling and transportation tasks. In order to reduce the degree of ambiguity occurring in a
dynamic manufacturing environment, mobile robots are equipped with a stereo vision system that
can reliably estimate distance to manufacturing entities. In this paper, a new stereo vision-based
algorithm for control of nonholonomic mobile robot is proposed. The main control algorithm, based
on an error in image parameters (IBVS - Image based visual servoing), is used for positioning of a
mobile robot in the desired location. For estimation of the error in image parameters, point features
are extracted from the current and target camera view via feature detection and description
algorithm. A comparison of these algorithms is made on a set of images obtained in laboratory
model of the manufacturing environment by using Basler acA1920-25uc cameras. Based on the
results of comparison, KAZE feature detection and description algorithm is proven to be best suited
for this specific case. In order to verify the stereo visual control system, simulation and real-world
experiments are performed. Two experiments are conducted on a mobile robot RAICO (Robot with
Artificial Intelligence based COgnition) in a laboratory model of the manufacturing environment.
Experimental results show the effectiveness of the proposed stereo visual control system and its
applicability in reaching the desired location with minimal accuracy error.",
journal = "Proceedings of selected papers and abstracts of the The Third International Students Scientific Conference "Multidisciplinary Approach to Contemporary Research - Cultural and Industrial Heritage", Belgrade, 21-22.12. 2019",
title = "Stereo vision-based algorithm for control of nonholonomic mobile robot",
pages = "82-69",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4237"
}

Đokić, L., Jokić, A., Petrović, M.,& Miljković, Z.. (2019). Stereo vision-based algorithm for control of nonholonomic mobile robot. in Proceedings of selected papers and abstracts of the The Third International Students Scientific Conference "Multidisciplinary Approach to Contemporary Research - Cultural and Industrial Heritage", Belgrade, 21-22.12. 2019, 69-82.
https://hdl.handle.net/21.15107/rcub_machinery_4237

Đokić L, Jokić A, Petrović M, Miljković Z. Stereo vision-based algorithm for control of nonholonomic mobile robot. in Proceedings of selected papers and abstracts of the The Third International Students Scientific Conference "Multidisciplinary Approach to Contemporary Research - Cultural and Industrial Heritage", Belgrade, 21-22.12. 2019. 2019;:69-82.
https://hdl.handle.net/21.15107/rcub_machinery_4237 .

Đokić, Lazar, Jokić, Aleksandar, Petrović, Milica, Miljković, Zoran, "Stereo vision-based algorithm for control of nonholonomic mobile robot" in Proceedings of selected papers and abstracts of the The Third International Students Scientific Conference "Multidisciplinary Approach to Contemporary Research - Cultural and Industrial Heritage", Belgrade, 21-22.12. 2019 (2019):69-82,
https://hdl.handle.net/21.15107/rcub_machinery_4237 .