TY  - THES
AU  - Nedeljković, Dušan
PY  - 2023
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/7678
AB  - Integracija kibernetsko-fizičkih sistema (engl. Cyber-Physical Systems – CPS) u industrijski internet stvari predstavlja osnovu za prelazak sa centralizovanih na distribuirane sisteme upravljanja u okviru kojih se upravljački zadaci raspodeljuju na različite uređaje, a celokupan zadatak upravljanja realizuje kroz njihov sinhronizovani rad i stalnu razmenu informacija. Sveprisutna komunikacija između elemenata industrijskih sistema upravljanja (engl. Industrial Control Systems – ICS) kao i njihovo povezivanje na globalnu mrežu otvaraju prostor za različite kibernetske napade koji pored ekonomskih posledica i katastrofalnih oštećenja opreme mogu imati i negativne uticaje na životnu sredinu i bezbednost na radu. 
U fokusu ove doktorske disertacije je problem detekcije kibernetskih napada na komunikacione veze između CPS u okviru sistema za kontinualno upravljanje proizvodnim resursima. U radu je predložena metodologija za kreiranje sistema za detekciju napada koja je zasnovana na principima samonadgledanog učenja i kreiranju autoregresionih modela podataka koji se razmenjuju između uređaja u normalnim uslovima rada (bez napada) korišćenjem različitih tehnika mašinskog učenja. Metodologija vrši automatski izbor svih parametara algoritma za detekciju i uzima u obzir arhitekturu sistema upravljanja i mogućnost implementacije algoritma za detekciju napada na nekom od uređaja u okviru sistema. Može se primeniti kako za sisteme
iz kojih je moguće prikupiti dovoljnu količinu podataka, tako i za sisteme za koje je dostupnost podataka ograničena. 
Verifikacija razvijenih sistema za detekciju napada sprovedena je na javno dostupnim skupovima podataka i skupovima podataka dobijenim sa eksperimentalnih instalacija koje su razvijene u okviru disertacije. Izvršena je implementacija i eksperimentalna verifikacija sistema za detekciju napada generisanih korišćenjem razvijenih metoda na kreiranoj instalaciji čime su i u realnim uslovima potvrđene postavljene polazne hipoteze.
T2  - Univerzitet u Beogradu, Mašinski fakultet
T1  - Detekcija kibernetskih napada na sisteme za upravljanje proizvodnim resursima
T1  - Detection of cyber-attacks on systems for manufacturing equipment control
UR  - https://hdl.handle.net/21.15107/rcub_machinery_7678
ER  - 

@phdthesis{
author = "Nedeljković, Dušan",
year = "2023",
abstract = "Integracija kibernetsko-fizičkih sistema (engl. Cyber-Physical Systems – CPS) u industrijski internet stvari predstavlja osnovu za prelazak sa centralizovanih na distribuirane sisteme upravljanja u okviru kojih se upravljački zadaci raspodeljuju na različite uređaje, a celokupan zadatak upravljanja realizuje kroz njihov sinhronizovani rad i stalnu razmenu informacija. Sveprisutna komunikacija između elemenata industrijskih sistema upravljanja (engl. Industrial Control Systems – ICS) kao i njihovo povezivanje na globalnu mrežu otvaraju prostor za različite kibernetske napade koji pored ekonomskih posledica i katastrofalnih oštećenja opreme mogu imati i negativne uticaje na životnu sredinu i bezbednost na radu. 
U fokusu ove doktorske disertacije je problem detekcije kibernetskih napada na komunikacione veze između CPS u okviru sistema za kontinualno upravljanje proizvodnim resursima. U radu je predložena metodologija za kreiranje sistema za detekciju napada koja je zasnovana na principima samonadgledanog učenja i kreiranju autoregresionih modela podataka koji se razmenjuju između uređaja u normalnim uslovima rada (bez napada) korišćenjem različitih tehnika mašinskog učenja. Metodologija vrši automatski izbor svih parametara algoritma za detekciju i uzima u obzir arhitekturu sistema upravljanja i mogućnost implementacije algoritma za detekciju napada na nekom od uređaja u okviru sistema. Može se primeniti kako za sisteme
iz kojih je moguće prikupiti dovoljnu količinu podataka, tako i za sisteme za koje je dostupnost podataka ograničena. 
Verifikacija razvijenih sistema za detekciju napada sprovedena je na javno dostupnim skupovima podataka i skupovima podataka dobijenim sa eksperimentalnih instalacija koje su razvijene u okviru disertacije. Izvršena je implementacija i eksperimentalna verifikacija sistema za detekciju napada generisanih korišćenjem razvijenih metoda na kreiranoj instalaciji čime su i u realnim uslovima potvrđene postavljene polazne hipoteze.",
journal = "Univerzitet u Beogradu, Mašinski fakultet",
title = "Detekcija kibernetskih napada na sisteme za upravljanje proizvodnim resursima, Detection of cyber-attacks on systems for manufacturing equipment control",
url = "https://hdl.handle.net/21.15107/rcub_machinery_7678"
}

Nedeljković, D.. (2023). Detekcija kibernetskih napada na sisteme za upravljanje proizvodnim resursima. in Univerzitet u Beogradu, Mašinski fakultet.
https://hdl.handle.net/21.15107/rcub_machinery_7678

Nedeljković D. Detekcija kibernetskih napada na sisteme za upravljanje proizvodnim resursima. in Univerzitet u Beogradu, Mašinski fakultet. 2023;.
https://hdl.handle.net/21.15107/rcub_machinery_7678 .

Nedeljković, Dušan, "Detekcija kibernetskih napada na sisteme za upravljanje proizvodnim resursima" in Univerzitet u Beogradu, Mašinski fakultet (2023),
https://hdl.handle.net/21.15107/rcub_machinery_7678 .

TY  - JOUR
AU  - Slavković, Nikola
AU  - Živanović, Saša
AU  - Vorkapić, Nikola
AU  - Dimić, Zoran
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4621
AB  - This paper presents an approach for developing the programming and offline
simulation systems for low-cost industrial robots in the
MatLab/Simulink environment. The approach is presented in the example
of a virtual model of a 4-axis robot with hybrid kinematics intended for
manipulation tasks. The industrial robot with hybrid kinematics consists of
the well-known 5R planar parallel mechanism to which two serial axes
have been added. The programming system developed in a MatLab
environment involves generating G-code programs based on given pick
and place points. The virtual model included in the simulation system is
configured in the Simulink environment based on the CAD model of the
robot and its kinematic structure. The kinematic model and the inverse
kinematic problem have to be included in the virtual model to realize the
motion of the virtual robot. The system of programming and simulation has
been verified through several examples that include object manipulation to
perform various tasks.
PB  - Belgrade : Faculty of Mechanical Engineering
T2  - FME Transactions
T1  - Development of the Programming and Simulation System of 4-axis Robot with Hybrid Kinematic
EP  - 411
IS  - 3
SP  - 403
VL  - 50
DO  - 10.5937/fme2203403S
ER  - 

@article{
author = "Slavković, Nikola and Živanović, Saša and Vorkapić, Nikola and Dimić, Zoran",
year = "2022",
abstract = "This paper presents an approach for developing the programming and offline
simulation systems for low-cost industrial robots in the
MatLab/Simulink environment. The approach is presented in the example
of a virtual model of a 4-axis robot with hybrid kinematics intended for
manipulation tasks. The industrial robot with hybrid kinematics consists of
the well-known 5R planar parallel mechanism to which two serial axes
have been added. The programming system developed in a MatLab
environment involves generating G-code programs based on given pick
and place points. The virtual model included in the simulation system is
configured in the Simulink environment based on the CAD model of the
robot and its kinematic structure. The kinematic model and the inverse
kinematic problem have to be included in the virtual model to realize the
motion of the virtual robot. The system of programming and simulation has
been verified through several examples that include object manipulation to
perform various tasks.",
publisher = "Belgrade : Faculty of Mechanical Engineering",
journal = "FME Transactions",
title = "Development of the Programming and Simulation System of 4-axis Robot with Hybrid Kinematic",
pages = "411-403",
number = "3",
volume = "50",
doi = "10.5937/fme2203403S"
}

Slavković, N., Živanović, S., Vorkapić, N.,& Dimić, Z.. (2022). Development of the Programming and Simulation System of 4-axis Robot with Hybrid Kinematic. in FME Transactions
Belgrade : Faculty of Mechanical Engineering., 50(3), 403-411.
https://doi.org/10.5937/fme2203403S

Slavković N, Živanović S, Vorkapić N, Dimić Z. Development of the Programming and Simulation System of 4-axis Robot with Hybrid Kinematic. in FME Transactions. 2022;50(3):403-411.
doi:10.5937/fme2203403S .

Slavković, Nikola, Živanović, Saša, Vorkapić, Nikola, Dimić, Zoran, "Development of the Programming and Simulation System of 4-axis Robot with Hybrid Kinematic" in FME Transactions, 50, no. 3 (2022):403-411,
https://doi.org/10.5937/fme2203403S . .

TY  - JOUR
AU  - Nedeljković, Dušan
AU  - Jakovljević, Živana
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3705
AB  - Extensive communication between smart devices in contemporary Industrial Control Systems (ICS) opens up a vast area for different cyber-attacks and malicious threats. The negative effects of these attacks can not only disrupt or completely disable the system functioning, but also they can have serious safety related consequences. Therefore, cybersecurity in ICS becomes one of the most important issues. In this paper we propose a method for the design of algorithms for the detection of cyber-attacks on communication links between smart devices. The method belongs to the class of semi-supervised data driven approaches and it is based on Convolutional Neural Networks (CNN). Starting from a predefined range of network hyperparameters and data obtained from system operation without attacks, the proposed method autonomously selects suitable CNN architecture and thresholds for online intrusion detection. Following the characteristics of ICS, the proposed intrusion detection is host based, and in our research we consider the structure of ICS and the feasibility of the attack detection algorithm implementation on control system devices. The method is experimentally verified using two case studies. In the first case study that refers to the publicly available dataset obtained from Secure Water Treatment (SWaT) testbed, we present a comparative analysis of the developed method with alternative approaches. The second case study considers a custom developed electro-pneumatic positioning system; in this system we carry out the real-world implementation and validation of the intrusion detection algorithm developed using the proposed method.
PB  - Elsevier Advanced Technology, Oxford
T2  - Computers & Security
T1  - CNN based method for the development of cyber-attacks detection algorithms in industrial control systems
VL  - 114
DO  - 10.1016/j.cose.2021.102585
ER  - 

@article{
author = "Nedeljković, Dušan and Jakovljević, Živana",
year = "2022",
abstract = "Extensive communication between smart devices in contemporary Industrial Control Systems (ICS) opens up a vast area for different cyber-attacks and malicious threats. The negative effects of these attacks can not only disrupt or completely disable the system functioning, but also they can have serious safety related consequences. Therefore, cybersecurity in ICS becomes one of the most important issues. In this paper we propose a method for the design of algorithms for the detection of cyber-attacks on communication links between smart devices. The method belongs to the class of semi-supervised data driven approaches and it is based on Convolutional Neural Networks (CNN). Starting from a predefined range of network hyperparameters and data obtained from system operation without attacks, the proposed method autonomously selects suitable CNN architecture and thresholds for online intrusion detection. Following the characteristics of ICS, the proposed intrusion detection is host based, and in our research we consider the structure of ICS and the feasibility of the attack detection algorithm implementation on control system devices. The method is experimentally verified using two case studies. In the first case study that refers to the publicly available dataset obtained from Secure Water Treatment (SWaT) testbed, we present a comparative analysis of the developed method with alternative approaches. The second case study considers a custom developed electro-pneumatic positioning system; in this system we carry out the real-world implementation and validation of the intrusion detection algorithm developed using the proposed method.",
publisher = "Elsevier Advanced Technology, Oxford",
journal = "Computers & Security",
title = "CNN based method for the development of cyber-attacks detection algorithms in industrial control systems",
volume = "114",
doi = "10.1016/j.cose.2021.102585"
}

Nedeljković, D.,& Jakovljević, Ž.. (2022). CNN based method for the development of cyber-attacks detection algorithms in industrial control systems. in Computers & Security
Elsevier Advanced Technology, Oxford., 114.
https://doi.org/10.1016/j.cose.2021.102585

Nedeljković D, Jakovljević Ž. CNN based method for the development of cyber-attacks detection algorithms in industrial control systems. in Computers & Security. 2022;114.
doi:10.1016/j.cose.2021.102585 .

Nedeljković, Dušan, Jakovljević, Živana, "CNN based method for the development of cyber-attacks detection algorithms in industrial control systems" in Computers & Security, 114 (2022),
https://doi.org/10.1016/j.cose.2021.102585 . .

TY  - JOUR
AU  - Marković, Veljko
AU  - Jakovljević, Živana
AU  - Budak, Igor
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/91
AB  - 3D scanning devices are traditionally employed in reverse engineering tasks that can be carried out semi-automatically, with user assistance. However, their application in manufacturing process control requires automatic point cloud segmentation and extraction of geometric primitives. In this paper, we propose a method for automatic recognition of planes and cylinders (most frequently encountered geometric primitives in mechanical engineering) from unstructured point clouds. The method is based on the scatter of data during least squares fitting of second order surfaces. It consists of three phases and the first phase represents automatic point cloud segmentation. The second phase deals with merging of over-segmented regions and surfaces parameters estimation, whereas the final phase provides extraction of recognized geometric primitives. The method is experimentally verified using three real-world case studies, and its performances are compared with two state of the art recognition algorithms. The results have shown that the proposed method outperforms alternative approaches in terms of appropriately recognized planes and cylinders without surface type confusion, as well as when the recognition of non-existent primitives is considered. In addition, the method determines surfaces parameters with high accuracy.
PB  - Springer, New York
T2  - Visual Computer
T1  - Automatic recognition of cylinders and planes from unstructured point clouds
EP  - 4352
SP  - 4329
VL  - 38
DO  - 10.1007/s00371-021-02299-9
ER  - 

@article{
author = "Marković, Veljko and Jakovljević, Živana and Budak, Igor",
year = "2022",
abstract = "3D scanning devices are traditionally employed in reverse engineering tasks that can be carried out semi-automatically, with user assistance. However, their application in manufacturing process control requires automatic point cloud segmentation and extraction of geometric primitives. In this paper, we propose a method for automatic recognition of planes and cylinders (most frequently encountered geometric primitives in mechanical engineering) from unstructured point clouds. The method is based on the scatter of data during least squares fitting of second order surfaces. It consists of three phases and the first phase represents automatic point cloud segmentation. The second phase deals with merging of over-segmented regions and surfaces parameters estimation, whereas the final phase provides extraction of recognized geometric primitives. The method is experimentally verified using three real-world case studies, and its performances are compared with two state of the art recognition algorithms. The results have shown that the proposed method outperforms alternative approaches in terms of appropriately recognized planes and cylinders without surface type confusion, as well as when the recognition of non-existent primitives is considered. In addition, the method determines surfaces parameters with high accuracy.",
publisher = "Springer, New York",
journal = "Visual Computer",
title = "Automatic recognition of cylinders and planes from unstructured point clouds",
pages = "4352-4329",
volume = "38",
doi = "10.1007/s00371-021-02299-9"
}

Marković, V., Jakovljević, Ž.,& Budak, I.. (2022). Automatic recognition of cylinders and planes from unstructured point clouds. in Visual Computer
Springer, New York., 38, 4329-4352.
https://doi.org/10.1007/s00371-021-02299-9

Marković V, Jakovljević Ž, Budak I. Automatic recognition of cylinders and planes from unstructured point clouds. in Visual Computer. 2022;38:4329-4352.
doi:10.1007/s00371-021-02299-9 .

Marković, Veljko, Jakovljević, Živana, Budak, Igor, "Automatic recognition of cylinders and planes from unstructured point clouds" in Visual Computer, 38 (2022):4329-4352,
https://doi.org/10.1007/s00371-021-02299-9 . .

TY  - JOUR
AU  - Vesović, Mitra
AU  - Jovanović, Radiša
AU  - Trišović, Nataša
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3770
AB  - The aim of this study is to investigate nonlinear DC motor behavior and to control velocity as output variable. The linear model is designed, but as it is experimentally verified that it does not describe the system well enough it is replaced by the nonlinear one. System's model has been obtained taking into account Coulomb and viscous friction in the firmly nonlinear environment. In the frame of the paper the dynamical analysis of the nonlinear feedback linearizing control is carried out. Furthermore, a metaheuristic optimization algorithm is set up for finding the coefficient of the proportional-integral feedback linearizing controller. For this purpose Gray wolf optimization technique is used. Moreover, after the introduction of the control law, analysis of the pole placement and stability of the system is establish. Optimized nonlinear control signal has been applied to the real object with simulated white noise and step signal as disturbances. Finally, for several desired output signals, responses with and without disruption are compared to illustrate the approach proposed in the paper. Experimental results obtained on the given system are provided and they verify nonlinear control robustness.
PB  - Sage Publications Ltd, London
T2  - Advances in Mechanical Engineering
T1  - Control of a DC motor using feedback linearization and gray wolf optimization algorithm
IS  - 3
VL  - 14
DO  - 10.1177/16878132221085324
ER  - 

@article{
author = "Vesović, Mitra and Jovanović, Radiša and Trišović, Nataša",
year = "2022",
abstract = "The aim of this study is to investigate nonlinear DC motor behavior and to control velocity as output variable. The linear model is designed, but as it is experimentally verified that it does not describe the system well enough it is replaced by the nonlinear one. System's model has been obtained taking into account Coulomb and viscous friction in the firmly nonlinear environment. In the frame of the paper the dynamical analysis of the nonlinear feedback linearizing control is carried out. Furthermore, a metaheuristic optimization algorithm is set up for finding the coefficient of the proportional-integral feedback linearizing controller. For this purpose Gray wolf optimization technique is used. Moreover, after the introduction of the control law, analysis of the pole placement and stability of the system is establish. Optimized nonlinear control signal has been applied to the real object with simulated white noise and step signal as disturbances. Finally, for several desired output signals, responses with and without disruption are compared to illustrate the approach proposed in the paper. Experimental results obtained on the given system are provided and they verify nonlinear control robustness.",
publisher = "Sage Publications Ltd, London",
journal = "Advances in Mechanical Engineering",
title = "Control of a DC motor using feedback linearization and gray wolf optimization algorithm",
number = "3",
volume = "14",
doi = "10.1177/16878132221085324"
}

Vesović, M., Jovanović, R.,& Trišović, N.. (2022). Control of a DC motor using feedback linearization and gray wolf optimization algorithm. in Advances in Mechanical Engineering
Sage Publications Ltd, London., 14(3).
https://doi.org/10.1177/16878132221085324

Vesović M, Jovanović R, Trišović N. Control of a DC motor using feedback linearization and gray wolf optimization algorithm. in Advances in Mechanical Engineering. 2022;14(3).
doi:10.1177/16878132221085324 .

Vesović, Mitra, Jovanović, Radiša, Trišović, Nataša, "Control of a DC motor using feedback linearization and gray wolf optimization algorithm" in Advances in Mechanical Engineering, 14, no. 3 (2022),
https://doi.org/10.1177/16878132221085324 . .

TY  - JOUR
AU  - Jokić, Aleksandar
AU  - Petrović, Milica
AU  - Miljković, Zoran
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3757
AB  - In the interest of developing an intelligent manufacturing environment with an agile, efficient, and optimally utilized transportation system, mobile robots need to achieve a certain level of autonomy as they play an important role in carrying out transportation tasks. Bearing this in mind, in the paper we propose a novel stereo visual servoing method for nonholonomic mobile robot control based on semantic segmentation. Semantic segmentation provides a rich body of information required for an adequate decision-making process in a clustered, dynamic, and ever-changing manufacturing environment. The innovative idea behind the new visual servoing system is to utilize semantic information of the scene for visual servoing, as well as for other mobile robot tasks, such as obstacle avoidance, scene understanding, and simultaneous localization and mapping. Semantic segmentation is carried out by exploiting fully convolutional neural networks. The new visual servoing algorithm utilizes an intensity-based image registration procedure, which results in the image transformation matrix. The transformation matrix encompasses the relations of images taken at the current and desired pose, and that information is directly used for visual servoing. The developed algorithm is deployed on our own developed wheeled differential drive mobile robot RAICO (Robot with Artificial Intelligence based COgnition). The experimental evaluation is carried out in the 3D simulation environment and in the laboratory model of the real manufacturing environment. The experimental results show that the accuracy of the proposed approach is improved when compared to the state-of-the-art approaches while being robust to the partial occlusions of the scene and illumination changes.
PB  - Pergamon-Elsevier Science Ltd, Oxford
T2  - Expert Systems With Applications
T1  - Semantic segmentation based stereo visual servoing of nonholonomic mobile robot in intelligent manufacturing environment
SP  - 116203
VL  - 190
DO  - 10.1016/j.eswa.2021.116203
ER  - 

@article{
author = "Jokić, Aleksandar and Petrović, Milica and Miljković, Zoran",
year = "2022",
abstract = "In the interest of developing an intelligent manufacturing environment with an agile, efficient, and optimally utilized transportation system, mobile robots need to achieve a certain level of autonomy as they play an important role in carrying out transportation tasks. Bearing this in mind, in the paper we propose a novel stereo visual servoing method for nonholonomic mobile robot control based on semantic segmentation. Semantic segmentation provides a rich body of information required for an adequate decision-making process in a clustered, dynamic, and ever-changing manufacturing environment. The innovative idea behind the new visual servoing system is to utilize semantic information of the scene for visual servoing, as well as for other mobile robot tasks, such as obstacle avoidance, scene understanding, and simultaneous localization and mapping. Semantic segmentation is carried out by exploiting fully convolutional neural networks. The new visual servoing algorithm utilizes an intensity-based image registration procedure, which results in the image transformation matrix. The transformation matrix encompasses the relations of images taken at the current and desired pose, and that information is directly used for visual servoing. The developed algorithm is deployed on our own developed wheeled differential drive mobile robot RAICO (Robot with Artificial Intelligence based COgnition). The experimental evaluation is carried out in the 3D simulation environment and in the laboratory model of the real manufacturing environment. The experimental results show that the accuracy of the proposed approach is improved when compared to the state-of-the-art approaches while being robust to the partial occlusions of the scene and illumination changes.",
publisher = "Pergamon-Elsevier Science Ltd, Oxford",
journal = "Expert Systems With Applications",
title = "Semantic segmentation based stereo visual servoing of nonholonomic mobile robot in intelligent manufacturing environment",
pages = "116203",
volume = "190",
doi = "10.1016/j.eswa.2021.116203"
}

Jokić, A., Petrović, M.,& Miljković, Z.. (2022). Semantic segmentation based stereo visual servoing of nonholonomic mobile robot in intelligent manufacturing environment. in Expert Systems With Applications
Pergamon-Elsevier Science Ltd, Oxford., 190, 116203.
https://doi.org/10.1016/j.eswa.2021.116203

Jokić A, Petrović M, Miljković Z. Semantic segmentation based stereo visual servoing of nonholonomic mobile robot in intelligent manufacturing environment. in Expert Systems With Applications. 2022;190:116203.
doi:10.1016/j.eswa.2021.116203 .

Jokić, Aleksandar, Petrović, Milica, Miljković, Zoran, "Semantic segmentation based stereo visual servoing of nonholonomic mobile robot in intelligent manufacturing environment" in Expert Systems With Applications, 190 (2022):116203,
https://doi.org/10.1016/j.eswa.2021.116203 . .

TY  - JOUR
AU  - Petrović, Milica
AU  - Jokić, Aleksandar
AU  - Miljković, Zoran
AU  - Kulesza, Zbigniew
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3946
AB  - During the last decades, intelligent mobile robots have been recognized as one of the most promising
and emerging solutions used for fulfilling material transport demands in intelligent manufacturing
systems. One of the most significant characteristics of those demands is their multi-objectivity, where
identified objectives might usually conflict. Therefore, obtaining the optimally scheduled robotic-
based material transport system that is simultaneously facing several conflicting objectives is a highly
challenging task. To address such a challenge, this paper proposes a novel multi-objective Grey Wolf
Optimizer (MOGWO) methodology to efficiently schedule material transport systems based on an
intelligent single mobile robot. The proposed optimization methodology includes the comprehensive
analysis and the mathematical formulation of 13 novel fitness functions combined to form a Pareto
front of the multi-objective optimization problem and a novel strategy for optimal exploration of multi-
objective search space. Moreover, four metrics, i.e., Generational Distance (GD), Inverted Generational
Distance (IGD), Spacing (SP), and Maximum Spread (MS), are employed to quantitively evaluate and
compare the effectiveness of the proposed enhanced MOGWO algorithm with three state-of-the-
art metaheuristic methods (MOGA, MOAOA, and MOPSO) on 25 benchmark problems. The results
achieved through two experimental scenarios indicate that the enhanced MOGWO algorithm outper-
forms other algorithms in terms of convergence, coverage, and the robust optimal Pareto solution.
Finally, transportation paths based on obtained scheduling plans are experimentally corroborated
by the mobile robot RAICO (Robot with Artificial Intelligence based Cognition) within a physical
model of the intelligent manufacturing environment. The achieved experimental results successfully
demonstrate the efficiency of the proposed methodology for optimal multi-objective scheduling of
material transport tasks based on a single mobile robotic system.
PB  - Elsevier
T2  - Applied Soft Computing
T1  - Multi-objective scheduling of a single mobile robot based on the grey wolf optimization algorithm
SP  - 109784
VL  - 131
DO  - 10.1016/j.asoc.2022.109784
ER  - 

@article{
author = "Petrović, Milica and Jokić, Aleksandar and Miljković, Zoran and Kulesza, Zbigniew",
year = "2022",
abstract = "During the last decades, intelligent mobile robots have been recognized as one of the most promising
and emerging solutions used for fulfilling material transport demands in intelligent manufacturing
systems. One of the most significant characteristics of those demands is their multi-objectivity, where
identified objectives might usually conflict. Therefore, obtaining the optimally scheduled robotic-
based material transport system that is simultaneously facing several conflicting objectives is a highly
challenging task. To address such a challenge, this paper proposes a novel multi-objective Grey Wolf
Optimizer (MOGWO) methodology to efficiently schedule material transport systems based on an
intelligent single mobile robot. The proposed optimization methodology includes the comprehensive
analysis and the mathematical formulation of 13 novel fitness functions combined to form a Pareto
front of the multi-objective optimization problem and a novel strategy for optimal exploration of multi-
objective search space. Moreover, four metrics, i.e., Generational Distance (GD), Inverted Generational
Distance (IGD), Spacing (SP), and Maximum Spread (MS), are employed to quantitively evaluate and
compare the effectiveness of the proposed enhanced MOGWO algorithm with three state-of-the-
art metaheuristic methods (MOGA, MOAOA, and MOPSO) on 25 benchmark problems. The results
achieved through two experimental scenarios indicate that the enhanced MOGWO algorithm outper-
forms other algorithms in terms of convergence, coverage, and the robust optimal Pareto solution.
Finally, transportation paths based on obtained scheduling plans are experimentally corroborated
by the mobile robot RAICO (Robot with Artificial Intelligence based Cognition) within a physical
model of the intelligent manufacturing environment. The achieved experimental results successfully
demonstrate the efficiency of the proposed methodology for optimal multi-objective scheduling of
material transport tasks based on a single mobile robotic system.",
publisher = "Elsevier",
journal = "Applied Soft Computing",
title = "Multi-objective scheduling of a single mobile robot based on the grey wolf optimization algorithm",
pages = "109784",
volume = "131",
doi = "10.1016/j.asoc.2022.109784"
}

Petrović, M., Jokić, A., Miljković, Z.,& Kulesza, Z.. (2022). Multi-objective scheduling of a single mobile robot based on the grey wolf optimization algorithm. in Applied Soft Computing
Elsevier., 131, 109784.
https://doi.org/10.1016/j.asoc.2022.109784

Petrović M, Jokić A, Miljković Z, Kulesza Z. Multi-objective scheduling of a single mobile robot based on the grey wolf optimization algorithm. in Applied Soft Computing. 2022;131:109784.
doi:10.1016/j.asoc.2022.109784 .

Petrović, Milica, Jokić, Aleksandar, Miljković, Zoran, Kulesza, Zbigniew, "Multi-objective scheduling of a single mobile robot based on the grey wolf optimization algorithm" in Applied Soft Computing, 131 (2022):109784,
https://doi.org/10.1016/j.asoc.2022.109784 . .

TY  - CONF
AU  - Petrović, Milica
AU  - Jokić, Aleksandar
AU  - Miljković, Zoran
AU  - Kulesza, Zbigniew
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3968
AB  - The contemporary manufacturing systems face a challenging and uncertain future due to frequent customer demands for customized products. A promising direction that can enable manufacturing systems to fulfill the market requirements is the adaptation of a reconfigurable manufacturing system paradigm. Physical reconfigurability can be achieved by developing systems that can satisfy conflicting production priorities such as minimal production time and maximal profit. Having that in mind, in this paper, the authors present a comprehensive analysis of population-based multi-objective optimization algorithms utilized for scheduling manufacturing entities. The output of multi-objective optimization is a set of Pareto optimal solutions in the form of production scheduling plans with transportation constraints. Three state-of-the-art population-based algorithms i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA), are employed for optimization, while the experimental results show the effectiveness and superiority of the WOA algorithm.
C3  - Proceedings of the 26th International Conference on Methods and Models in Automation and Robotics (MMAR 2022)
T1  - Multi-Objective Population-based Optimization Algorithms for Scheduling of Manufacturing Entities
SP  - 403-407
DO  - 10.1109/MMAR55195.2022.9874301
ER  - 

@conference{
author = "Petrović, Milica and Jokić, Aleksandar and Miljković, Zoran and Kulesza, Zbigniew",
year = "2022",
abstract = "The contemporary manufacturing systems face a challenging and uncertain future due to frequent customer demands for customized products. A promising direction that can enable manufacturing systems to fulfill the market requirements is the adaptation of a reconfigurable manufacturing system paradigm. Physical reconfigurability can be achieved by developing systems that can satisfy conflicting production priorities such as minimal production time and maximal profit. Having that in mind, in this paper, the authors present a comprehensive analysis of population-based multi-objective optimization algorithms utilized for scheduling manufacturing entities. The output of multi-objective optimization is a set of Pareto optimal solutions in the form of production scheduling plans with transportation constraints. Three state-of-the-art population-based algorithms i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA), are employed for optimization, while the experimental results show the effectiveness and superiority of the WOA algorithm.",
journal = "Proceedings of the 26th International Conference on Methods and Models in Automation and Robotics (MMAR 2022)",
title = "Multi-Objective Population-based Optimization Algorithms for Scheduling of Manufacturing Entities",
pages = "403-407",
doi = "10.1109/MMAR55195.2022.9874301"
}

Petrović, M., Jokić, A., Miljković, Z.,& Kulesza, Z.. (2022). Multi-Objective Population-based Optimization Algorithms for Scheduling of Manufacturing Entities. in Proceedings of the 26th International Conference on Methods and Models in Automation and Robotics (MMAR 2022), 403-407.
https://doi.org/10.1109/MMAR55195.2022.9874301

Petrović M, Jokić A, Miljković Z, Kulesza Z. Multi-Objective Population-based Optimization Algorithms for Scheduling of Manufacturing Entities. in Proceedings of the 26th International Conference on Methods and Models in Automation and Robotics (MMAR 2022). 2022;:403-407.
doi:10.1109/MMAR55195.2022.9874301 .

Petrović, Milica, Jokić, Aleksandar, Miljković, Zoran, Kulesza, Zbigniew, "Multi-Objective Population-based Optimization Algorithms for Scheduling of Manufacturing Entities" in Proceedings of the 26th International Conference on Methods and Models in Automation and Robotics (MMAR 2022) (2022):403-407,
https://doi.org/10.1109/MMAR55195.2022.9874301 . .

TY  - CONF
AU  - Vesović, Mitra
AU  - Jovanović, Radiša
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4546
AB  - Several studies dealing with position control of the DC motor have reported 
issues concerning friction force. This article demonstrates a nonlinear control 
and optimization strategy for position control of a series servo motor. Once it is 
empirically verified that the linear model does not adequately reflect the system, 
the model is upgraded from linear to nonlinear. In the course of the research, 
the nonlinear feedback linearizing the controller's behavior is examined. A grey 
wolf metaheuristic optimization algorithm is used to find the coefficients of the 
controller's gains. In this way, modern methods are applied to take a fresh look 
at the existing problem.  Furthermore, performance for various targeted output 
signals is compared to show the approach proposed in the study. Also, a compara-
tive analysis with whale optimization algorithm is performed. The experimental 
results acquired on the stated system are shown, and they validate the usage of the 
nonlinear control, demonstrating the effectiveness of using optimum feedback 
linearization in electrical machines.
PB  - Belgrade :  Singidunum University
C3  - Sinteza 2022-International Scientific Conference on Information Technology and Data Related Research
T1  - GREY WOLF OPTIMIZATION FOR POSITION CONTROL OF A DIRECT CURRENT MOTOR DRIVEN  BY FEEDBACK LINEARIZATION METHOD
EP  - 43
SP  - THEORETICAL COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE SESSION pp. 36
DO  - 10.15308/Sinteza-2022-36-43
ER  - 

@conference{
author = "Vesović, Mitra and Jovanović, Radiša",
year = "2022",
abstract = "Several studies dealing with position control of the DC motor have reported 
issues concerning friction force. This article demonstrates a nonlinear control 
and optimization strategy for position control of a series servo motor. Once it is 
empirically verified that the linear model does not adequately reflect the system, 
the model is upgraded from linear to nonlinear. In the course of the research, 
the nonlinear feedback linearizing the controller's behavior is examined. A grey 
wolf metaheuristic optimization algorithm is used to find the coefficients of the 
controller's gains. In this way, modern methods are applied to take a fresh look 
at the existing problem.  Furthermore, performance for various targeted output 
signals is compared to show the approach proposed in the study. Also, a compara-
tive analysis with whale optimization algorithm is performed. The experimental 
results acquired on the stated system are shown, and they validate the usage of the 
nonlinear control, demonstrating the effectiveness of using optimum feedback 
linearization in electrical machines.",
publisher = "Belgrade :  Singidunum University",
journal = "Sinteza 2022-International Scientific Conference on Information Technology and Data Related Research",
title = "GREY WOLF OPTIMIZATION FOR POSITION CONTROL OF A DIRECT CURRENT MOTOR DRIVEN  BY FEEDBACK LINEARIZATION METHOD",
pages = "43-THEORETICAL COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE SESSION pp. 36",
doi = "10.15308/Sinteza-2022-36-43"
}

Vesović, M.,& Jovanović, R.. (2022). GREY WOLF OPTIMIZATION FOR POSITION CONTROL OF A DIRECT CURRENT MOTOR DRIVEN  BY FEEDBACK LINEARIZATION METHOD. in Sinteza 2022-International Scientific Conference on Information Technology and Data Related Research
Belgrade :  Singidunum University., THEORETICAL COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE SESSION pp. 36-43.
https://doi.org/10.15308/Sinteza-2022-36-43

Vesović M, Jovanović R. GREY WOLF OPTIMIZATION FOR POSITION CONTROL OF A DIRECT CURRENT MOTOR DRIVEN  BY FEEDBACK LINEARIZATION METHOD. in Sinteza 2022-International Scientific Conference on Information Technology and Data Related Research. 2022;:THEORETICAL COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE SESSION pp. 36-43.
doi:10.15308/Sinteza-2022-36-43 .

Vesović, Mitra, Jovanović, Radiša, "GREY WOLF OPTIMIZATION FOR POSITION CONTROL OF A DIRECT CURRENT MOTOR DRIVEN  BY FEEDBACK LINEARIZATION METHOD" in Sinteza 2022-International Scientific Conference on Information Technology and Data Related Research (2022):THEORETICAL COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE SESSION pp. 36-43,
https://doi.org/10.15308/Sinteza-2022-36-43 . .

TY  - CONF
AU  - Jovanović, Radiša
AU  - Vesović, Mitra
AU  - Perišić, Natalija
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4501
AB  - In this paper a novel metaheuristic method, artificial gorilla troops optimizer, is used in order to optimize classical proportional-integral controller for liquid level system, that has wide application in many industries. In optimization process nonlinear model of the system is used. Obtained results are provided. It is shown that optimized controller represents superior solution compared to classical controller.
PB  - University of Belgrade Faculty of Mechanical Engineering
C3  - 8th International Conference of Industrial Engineering SIE 2022 : Proceedings, 29th-30th September, 2022, Belgrade, Serbia
T1  - PI controller optimization by artificial gorilla troops for liquid level control
EP  - 93
SP  - 90
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4501
ER  - 

@conference{
author = "Jovanović, Radiša and Vesović, Mitra and Perišić, Natalija",
year = "2022",
abstract = "In this paper a novel metaheuristic method, artificial gorilla troops optimizer, is used in order to optimize classical proportional-integral controller for liquid level system, that has wide application in many industries. In optimization process nonlinear model of the system is used. Obtained results are provided. It is shown that optimized controller represents superior solution compared to classical controller.",
publisher = "University of Belgrade Faculty of Mechanical Engineering",
journal = "8th International Conference of Industrial Engineering SIE 2022 : Proceedings, 29th-30th September, 2022, Belgrade, Serbia",
title = "PI controller optimization by artificial gorilla troops for liquid level control",
pages = "93-90",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4501"
}

Jovanović, R., Vesović, M.,& Perišić, N.. (2022). PI controller optimization by artificial gorilla troops for liquid level control. in 8th International Conference of Industrial Engineering SIE 2022 : Proceedings, 29th-30th September, 2022, Belgrade, Serbia
University of Belgrade Faculty of Mechanical Engineering., 90-93.
https://hdl.handle.net/21.15107/rcub_machinery_4501

Jovanović R, Vesović M, Perišić N. PI controller optimization by artificial gorilla troops for liquid level control. in 8th International Conference of Industrial Engineering SIE 2022 : Proceedings, 29th-30th September, 2022, Belgrade, Serbia. 2022;:90-93.
https://hdl.handle.net/21.15107/rcub_machinery_4501 .

Jovanović, Radiša, Vesović, Mitra, Perišić, Natalija, "PI controller optimization by artificial gorilla troops for liquid level control" in 8th International Conference of Industrial Engineering SIE 2022 : Proceedings, 29th-30th September, 2022, Belgrade, Serbia (2022):90-93,
https://hdl.handle.net/21.15107/rcub_machinery_4501 .

TY  - JOUR
AU  - Jovanović, Radiša
AU  - Bugarić, Uglješa
AU  - Vesović, Mitra 
AU  - Perišić, Natalija
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4516
AB  - This paper presents the proportional–derivative fuzzy controller for
trajectory tracking of the gripping mechanism with two degrees of
freedom. Aiming to achieve movement of the gripping mechanism without
sudden starting and stopping, a polynomial velocity profile is utilized. The
African vultures optimization, as one of the latest metaheuristic
algorithms, is used to obtain the optimal input/output scaling gains of the
proposed fuzzy controller according to the selected fitness function. The
results obtained by this algorithm are compared with the other three new
and popular metaheuristic algorithms: the whale optimization, the ant lion
optimization and the sine cosine algorithm. Moreover, a simulation study
was done for the defined initial position and for the scenario where there is
a certain deviation because the gripping mechanism is not at its original
initial position. Finally, the robustness of the controller is tested for the
case when the masses of the segments increase three times. The results
revealed that the suggested controller was capable of dealing with
nonlinearities of the gripping mechanism, initial position and parameter
changes. The movement of the gripping mechanism is smooth and follows
the defined trajectory.
PB  - University of Belgrade - Faculty of Mechanical Engineering
T2  - FME Transactions
T1  - Fuzzy Controller Optimized by the African Vultures Algorithm for Trajectory Tracking of a Two–Link Gripping Mechanism
EP  - 501
IS  - 3
SP  - 491
VL  - 50
DO  - 10.5937/fme2203491J
ER  - 

@article{
author = "Jovanović, Radiša and Bugarić, Uglješa and Vesović, Mitra  and Perišić, Natalija",
year = "2022",
abstract = "This paper presents the proportional–derivative fuzzy controller for
trajectory tracking of the gripping mechanism with two degrees of
freedom. Aiming to achieve movement of the gripping mechanism without
sudden starting and stopping, a polynomial velocity profile is utilized. The
African vultures optimization, as one of the latest metaheuristic
algorithms, is used to obtain the optimal input/output scaling gains of the
proposed fuzzy controller according to the selected fitness function. The
results obtained by this algorithm are compared with the other three new
and popular metaheuristic algorithms: the whale optimization, the ant lion
optimization and the sine cosine algorithm. Moreover, a simulation study
was done for the defined initial position and for the scenario where there is
a certain deviation because the gripping mechanism is not at its original
initial position. Finally, the robustness of the controller is tested for the
case when the masses of the segments increase three times. The results
revealed that the suggested controller was capable of dealing with
nonlinearities of the gripping mechanism, initial position and parameter
changes. The movement of the gripping mechanism is smooth and follows
the defined trajectory.",
publisher = "University of Belgrade - Faculty of Mechanical Engineering",
journal = "FME Transactions",
title = "Fuzzy Controller Optimized by the African Vultures Algorithm for Trajectory Tracking of a Two–Link Gripping Mechanism",
pages = "501-491",
number = "3",
volume = "50",
doi = "10.5937/fme2203491J"
}

Jovanović, R., Bugarić, U., Vesović, M.,& Perišić, N.. (2022). Fuzzy Controller Optimized by the African Vultures Algorithm for Trajectory Tracking of a Two–Link Gripping Mechanism. in FME Transactions
University of Belgrade - Faculty of Mechanical Engineering., 50(3), 491-501.
https://doi.org/10.5937/fme2203491J

Jovanović R, Bugarić U, Vesović M, Perišić N. Fuzzy Controller Optimized by the African Vultures Algorithm for Trajectory Tracking of a Two–Link Gripping Mechanism. in FME Transactions. 2022;50(3):491-501.
doi:10.5937/fme2203491J .

Jovanović, Radiša, Bugarić, Uglješa, Vesović, Mitra , Perišić, Natalija, "Fuzzy Controller Optimized by the African Vultures Algorithm for Trajectory Tracking of a Two–Link Gripping Mechanism" in FME Transactions, 50, no. 3 (2022):491-501,
https://doi.org/10.5937/fme2203491J . .

TY  - CONF
AU  - Perišić, Natalija
AU  - Jovanović, Radiša
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4498
AB  - In this paper artificial neural network is proposed as a method to classify defective and non-defective casting products in order to improve quality inspection process. Three different models of convolutional neural networks are trained and tested on dataset of submersible pump impeller images which has uneven number of image samples in each class. In order to inspect if slightly imbalanced classes have impact on result, two experiments are done. All of the models are ImageNet pre-trained networks, InceptionV3, Xception and MobileNetV2, where transfer learning method is applied with fine-tuning. Stochastic gradient descent algorithm is implemented for optimization. Obtained results of all models are presented and comparison is made.
PB  - Belgrade: University of Belgrade Faculty of Mechanical Engineering
C3  - 8th International Conference of Industrial Engineering SIE 2022 : Proceedings, 29th-30th September, 2022, Belgrade, Serbia
T1  - Application of deep learning in quality inspection of casting products
EP  - 151
SP  - 148
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4498
ER  - 

@conference{
author = "Perišić, Natalija and Jovanović, Radiša",
year = "2022",
abstract = "In this paper artificial neural network is proposed as a method to classify defective and non-defective casting products in order to improve quality inspection process. Three different models of convolutional neural networks are trained and tested on dataset of submersible pump impeller images which has uneven number of image samples in each class. In order to inspect if slightly imbalanced classes have impact on result, two experiments are done. All of the models are ImageNet pre-trained networks, InceptionV3, Xception and MobileNetV2, where transfer learning method is applied with fine-tuning. Stochastic gradient descent algorithm is implemented for optimization. Obtained results of all models are presented and comparison is made.",
publisher = "Belgrade: University of Belgrade Faculty of Mechanical Engineering",
journal = "8th International Conference of Industrial Engineering SIE 2022 : Proceedings, 29th-30th September, 2022, Belgrade, Serbia",
title = "Application of deep learning in quality inspection of casting products",
pages = "151-148",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4498"
}

Perišić, N.,& Jovanović, R.. (2022). Application of deep learning in quality inspection of casting products. in 8th International Conference of Industrial Engineering SIE 2022 : Proceedings, 29th-30th September, 2022, Belgrade, Serbia
Belgrade: University of Belgrade Faculty of Mechanical Engineering., 148-151.
https://hdl.handle.net/21.15107/rcub_machinery_4498

Perišić N, Jovanović R. Application of deep learning in quality inspection of casting products. in 8th International Conference of Industrial Engineering SIE 2022 : Proceedings, 29th-30th September, 2022, Belgrade, Serbia. 2022;:148-151.
https://hdl.handle.net/21.15107/rcub_machinery_4498 .

Perišić, Natalija, Jovanović, Radiša, "Application of deep learning in quality inspection of casting products" in 8th International Conference of Industrial Engineering SIE 2022 : Proceedings, 29th-30th September, 2022, Belgrade, Serbia (2022):148-151,
https://hdl.handle.net/21.15107/rcub_machinery_4498 .

TY  - CONF
AU  - Perišić, Natalija
AU  - Jovanović, Radiša
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4494
AB  - This paper deals with the problem of classifying images of real and fake faces as it
is impossible to distinguish them with the bare eye. Two different convolutional
neural networks architecture models are applied. The first one is pre-trained
VGG16 model, where transfer learning method is applied on our dataset. The
architecture of the second model is based on VGG16 and represents its smaller
and lighter version. Techniques such as learning rate decay, dropout and batch
normalization was applied in training process. Comparison of obtained results
of both models is made.
PB  - Belgrade: Singidunum University
C3  - Sinteza 2022 - International Scientific Conference on Information Technology and Data Related Research
T1  - Convolutional Neural Networks for Real and Fake Face Classification
EP  - 35
SP  - 29
DO  - 10.15308/Sinteza-2022-29-35
ER  - 

@conference{
author = "Perišić, Natalija and Jovanović, Radiša",
year = "2022",
abstract = "This paper deals with the problem of classifying images of real and fake faces as it
is impossible to distinguish them with the bare eye. Two different convolutional
neural networks architecture models are applied. The first one is pre-trained
VGG16 model, where transfer learning method is applied on our dataset. The
architecture of the second model is based on VGG16 and represents its smaller
and lighter version. Techniques such as learning rate decay, dropout and batch
normalization was applied in training process. Comparison of obtained results
of both models is made.",
publisher = "Belgrade: Singidunum University",
journal = "Sinteza 2022 - International Scientific Conference on Information Technology and Data Related Research",
title = "Convolutional Neural Networks for Real and Fake Face Classification",
pages = "35-29",
doi = "10.15308/Sinteza-2022-29-35"
}

Perišić, N.,& Jovanović, R.. (2022). Convolutional Neural Networks for Real and Fake Face Classification. in Sinteza 2022 - International Scientific Conference on Information Technology and Data Related Research
Belgrade: Singidunum University., 29-35.
https://doi.org/10.15308/Sinteza-2022-29-35

Perišić N, Jovanović R. Convolutional Neural Networks for Real and Fake Face Classification. in Sinteza 2022 - International Scientific Conference on Information Technology and Data Related Research. 2022;:29-35.
doi:10.15308/Sinteza-2022-29-35 .

Perišić, Natalija, Jovanović, Radiša, "Convolutional Neural Networks for Real and Fake Face Classification" in Sinteza 2022 - International Scientific Conference on Information Technology and Data Related Research (2022):29-35,
https://doi.org/10.15308/Sinteza-2022-29-35 . .

TY  - CONF
AU  - Slavković, Nikola
AU  - Živanović, Saša
AU  - Dimić, Zoran
AU  - Vorkapić, Nikola
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4622
AB  - The development of integrated computing
environments provides opportunities for the development of
virtual production. Virtual simulation is crucial when the robot
performs tasks that include some manufacturing processes.
Virtual robots are used for program verification before sending
it to the real robot and enable collision checking between robot
segments themselves and the robot and its environment. Virtual
models of industrial robots could be configured in different
environments and ways. This paper presents the method for
configuring virtual robots as an integral part of the control
system. The virtual robot's configuration is realized under the
LinuxCNC software environment and relies on OpenGL and
several interface classes written in Python programming
language. Developing a robot kinematic model to implement a
virtual robot integrated with an open-architecture control system
is necessary. Models of robot segments were imported in ASCII
STL format and connected according to the robot kinematic
model, and then the virtual robot was integrated within the
LinuxCNC control system. The method for configuring a virtual
robot as well as its kinematic model is presented in the example
of the BiSCARA robot. Verifying the robot control system,
virtual model, and kinematic model has been performed through
several examples of drawing contours on the configured virtual
robot.
PB  - Novi Pazar : State University
C3  - 9TH International Conference on Electrical, Electronic and Computing Engineering IcETRAN 2022, 6-9 June 2022, Novi Pazar, Serbia
T1  - Method for Configuring Virtual Robot as an Integral Part of the Control System
EP  - 644
SP  - 639
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4622
ER  - 

@conference{
author = "Slavković, Nikola and Živanović, Saša and Dimić, Zoran and Vorkapić, Nikola",
year = "2022",
abstract = "The development of integrated computing
environments provides opportunities for the development of
virtual production. Virtual simulation is crucial when the robot
performs tasks that include some manufacturing processes.
Virtual robots are used for program verification before sending
it to the real robot and enable collision checking between robot
segments themselves and the robot and its environment. Virtual
models of industrial robots could be configured in different
environments and ways. This paper presents the method for
configuring virtual robots as an integral part of the control
system. The virtual robot's configuration is realized under the
LinuxCNC software environment and relies on OpenGL and
several interface classes written in Python programming
language. Developing a robot kinematic model to implement a
virtual robot integrated with an open-architecture control system
is necessary. Models of robot segments were imported in ASCII
STL format and connected according to the robot kinematic
model, and then the virtual robot was integrated within the
LinuxCNC control system. The method for configuring a virtual
robot as well as its kinematic model is presented in the example
of the BiSCARA robot. Verifying the robot control system,
virtual model, and kinematic model has been performed through
several examples of drawing contours on the configured virtual
robot.",
publisher = "Novi Pazar : State University",
journal = "9TH International Conference on Electrical, Electronic and Computing Engineering IcETRAN 2022, 6-9 June 2022, Novi Pazar, Serbia",
title = "Method for Configuring Virtual Robot as an Integral Part of the Control System",
pages = "644-639",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4622"
}

Slavković, N., Živanović, S., Dimić, Z.,& Vorkapić, N.. (2022). Method for Configuring Virtual Robot as an Integral Part of the Control System. in 9TH International Conference on Electrical, Electronic and Computing Engineering IcETRAN 2022, 6-9 June 2022, Novi Pazar, Serbia
Novi Pazar : State University., 639-644.
https://hdl.handle.net/21.15107/rcub_machinery_4622

Slavković N, Živanović S, Dimić Z, Vorkapić N. Method for Configuring Virtual Robot as an Integral Part of the Control System. in 9TH International Conference on Electrical, Electronic and Computing Engineering IcETRAN 2022, 6-9 June 2022, Novi Pazar, Serbia. 2022;:639-644.
https://hdl.handle.net/21.15107/rcub_machinery_4622 .

Slavković, Nikola, Živanović, Saša, Dimić, Zoran, Vorkapić, Nikola, "Method for Configuring Virtual Robot as an Integral Part of the Control System" in 9TH International Conference on Electrical, Electronic and Computing Engineering IcETRAN 2022, 6-9 June 2022, Novi Pazar, Serbia (2022):639-644,
https://hdl.handle.net/21.15107/rcub_machinery_4622 .

TY  - JOUR
AU  - Vesović, Mitra
AU  - Jovanović, Radiša
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4547
AB  - Adaptivni neuro fazi sistemi zaključivanja (eng. Adaptive Neural Fuzzy Inference Systems) ANFIS imaju sve veću tendenciju upotrebe u naučnim istraživanjima i praktičnim primenama. Digitalizacija proizvodnje i pojava Industrije 4.0 omogućila je razvoj ovog trenda, pre svega, zbog sposobnosti prilagođavanja zadatku integrisanjem veštačkih neuronskih mreža i fazi logike, čime se potencijalno mogu iskoristiti prednosti obe tehnike u jedinstvenim okvirima. Ovaj pristup olakšao je procese modelovanja, analize podataka, klasifikacije i upravljanja. Pogodnost ANFIS sistema, u odnosu na konvencionalne metode, se ogleda u mogućnosti predviđanja izlaza na osnovu skupa ulaza i baze pravila. Takođe, ovi sistemi su pogodni za korišćenje u upravljanju, jer pružaju mogućnost za podešavanje parametara upravljačkog sistema. U ovom radu je predstavljena struktura ANFIS sistema i dat je detaljan prikaz dosadašnjih dostignuća, kroz komparativnu analizu, pri čemu su istaknute neke moguće sfere interdisciplinarne primene. Razmatrane su mogućnosti za varijacije, poboljšanja i inovacije algoritma, kao i smanjenja složenosti same arhitekture mreže. Prikazani su predlozi za neke nove, još neiskorišćene kombinacije sa metaheurističkim metodama optimizacije. Konačno, date su bitne smernice o tome kada i gde je korisno primeniti ANFIS sisteme.
PB  - Beograd : Savez inženjera i tehničara Srbije
T2  - Tehnika
T1  - Adaptivni neuro fazi sistemi u identifikaciji, modelovanju i upravljanju - pregled stanja u oblasti istraživanja
EP  - 446
IS  - 4
SP  - 439
VL  - 77
DO  - 10.5937/tehnika2204439V
ER  - 

@article{
author = "Vesović, Mitra and Jovanović, Radiša",
year = "2022",
abstract = "Adaptivni neuro fazi sistemi zaključivanja (eng. Adaptive Neural Fuzzy Inference Systems) ANFIS imaju sve veću tendenciju upotrebe u naučnim istraživanjima i praktičnim primenama. Digitalizacija proizvodnje i pojava Industrije 4.0 omogućila je razvoj ovog trenda, pre svega, zbog sposobnosti prilagođavanja zadatku integrisanjem veštačkih neuronskih mreža i fazi logike, čime se potencijalno mogu iskoristiti prednosti obe tehnike u jedinstvenim okvirima. Ovaj pristup olakšao je procese modelovanja, analize podataka, klasifikacije i upravljanja. Pogodnost ANFIS sistema, u odnosu na konvencionalne metode, se ogleda u mogućnosti predviđanja izlaza na osnovu skupa ulaza i baze pravila. Takođe, ovi sistemi su pogodni za korišćenje u upravljanju, jer pružaju mogućnost za podešavanje parametara upravljačkog sistema. U ovom radu je predstavljena struktura ANFIS sistema i dat je detaljan prikaz dosadašnjih dostignuća, kroz komparativnu analizu, pri čemu su istaknute neke moguće sfere interdisciplinarne primene. Razmatrane su mogućnosti za varijacije, poboljšanja i inovacije algoritma, kao i smanjenja složenosti same arhitekture mreže. Prikazani su predlozi za neke nove, još neiskorišćene kombinacije sa metaheurističkim metodama optimizacije. Konačno, date su bitne smernice o tome kada i gde je korisno primeniti ANFIS sisteme.",
publisher = "Beograd : Savez inženjera i tehničara Srbije",
journal = "Tehnika",
title = "Adaptivni neuro fazi sistemi u identifikaciji, modelovanju i upravljanju - pregled stanja u oblasti istraživanja",
pages = "446-439",
number = "4",
volume = "77",
doi = "10.5937/tehnika2204439V"
}

Vesović, M.,& Jovanović, R.. (2022). Adaptivni neuro fazi sistemi u identifikaciji, modelovanju i upravljanju - pregled stanja u oblasti istraživanja. in Tehnika
Beograd : Savez inženjera i tehničara Srbije., 77(4), 439-446.
https://doi.org/10.5937/tehnika2204439V

Vesović M, Jovanović R. Adaptivni neuro fazi sistemi u identifikaciji, modelovanju i upravljanju - pregled stanja u oblasti istraživanja. in Tehnika. 2022;77(4):439-446.
doi:10.5937/tehnika2204439V .

Vesović, Mitra, Jovanović, Radiša, "Adaptivni neuro fazi sistemi u identifikaciji, modelovanju i upravljanju - pregled stanja u oblasti istraživanja" in Tehnika, 77, no. 4 (2022):439-446,
https://doi.org/10.5937/tehnika2204439V . .

TY  - CONF
AU  - Jakovljević, Živana
AU  - Nedeljković, Dušan
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/5278
AB  - U okviru ovog rada navode se rezultati istraživanja sprovedenih u okviru projekta MISSION4.0 pod nazivom Optimizacioni algoritmi za upravljanje i terminiranje kibernetsko fizičkih sistema u okviru Industrije 4.0 zasnovani na dubokom mašinskom učenju i inteligenciji roja, finansiranog od strane Fonda za nauku Republike Srbije u periodu od 2020-2022. godine. Prikazani rezultati odnose se na oblast sajber bezbednosti u kontinualnim sistemima upravljanja što predstavlja jedan od radnih paketa projekta MISSION4.0. U skladu sa tim, pravci istraživanja odnosili su se na razvoj algoritama za detekciju napada u industrijskim sistemima upravljanja sa centralizovanom i distribuiranom arhitekturom, kao i na primenu otvorene platforme za komunikaciju, u cilju bezbedne razmene podataka između uređaja različitih proizvođača. Pored toga, dobijeni rezultati i njihova integracija u predavanja i laboratorijske vežbe poslužili su kao osnova za edukaciju inženjera u oblastima kibernetsko fizičkih sistema, industrijskog interneta stvari i sajber bezbednosti.
PB  - University of Belgrade - Faculty of Mechanical Engineering
C3  - 43. JUPITER konferencija, 36. simpozijum „CIM u strategiji tehnološkog razvoja industrije prerade metala“, Zbornik radova / 43rd JUPITER Conference, Proceedings, Beograd, oktobar 2022
T1  - Sajber bezbednost u kontinualnim sistemima upravljanja – pregled rezultata u okviru projekta mission4.0
T1  - Cyber security in continuous-time controlled systems – overview of the results within the project of mission4.0
EP  - 1.16
SP  - 1.7
UR  - https://hdl.handle.net/21.15107/rcub_machinery_5278
ER  - 

@conference{
author = "Jakovljević, Živana and Nedeljković, Dušan",
year = "2022",
abstract = "U okviru ovog rada navode se rezultati istraživanja sprovedenih u okviru projekta MISSION4.0 pod nazivom Optimizacioni algoritmi za upravljanje i terminiranje kibernetsko fizičkih sistema u okviru Industrije 4.0 zasnovani na dubokom mašinskom učenju i inteligenciji roja, finansiranog od strane Fonda za nauku Republike Srbije u periodu od 2020-2022. godine. Prikazani rezultati odnose se na oblast sajber bezbednosti u kontinualnim sistemima upravljanja što predstavlja jedan od radnih paketa projekta MISSION4.0. U skladu sa tim, pravci istraživanja odnosili su se na razvoj algoritama za detekciju napada u industrijskim sistemima upravljanja sa centralizovanom i distribuiranom arhitekturom, kao i na primenu otvorene platforme za komunikaciju, u cilju bezbedne razmene podataka između uređaja različitih proizvođača. Pored toga, dobijeni rezultati i njihova integracija u predavanja i laboratorijske vežbe poslužili su kao osnova za edukaciju inženjera u oblastima kibernetsko fizičkih sistema, industrijskog interneta stvari i sajber bezbednosti.",
publisher = "University of Belgrade - Faculty of Mechanical Engineering",
journal = "43. JUPITER konferencija, 36. simpozijum „CIM u strategiji tehnološkog razvoja industrije prerade metala“, Zbornik radova / 43rd JUPITER Conference, Proceedings, Beograd, oktobar 2022",
title = "Sajber bezbednost u kontinualnim sistemima upravljanja – pregled rezultata u okviru projekta mission4.0, Cyber security in continuous-time controlled systems – overview of the results within the project of mission4.0",
pages = "1.16-1.7",
url = "https://hdl.handle.net/21.15107/rcub_machinery_5278"
}

Jakovljević, Ž.,& Nedeljković, D.. (2022). Sajber bezbednost u kontinualnim sistemima upravljanja – pregled rezultata u okviru projekta mission4.0. in 43. JUPITER konferencija, 36. simpozijum „CIM u strategiji tehnološkog razvoja industrije prerade metala“, Zbornik radova / 43rd JUPITER Conference, Proceedings, Beograd, oktobar 2022
University of Belgrade - Faculty of Mechanical Engineering., 1.7-1.16.
https://hdl.handle.net/21.15107/rcub_machinery_5278

Jakovljević Ž, Nedeljković D. Sajber bezbednost u kontinualnim sistemima upravljanja – pregled rezultata u okviru projekta mission4.0. in 43. JUPITER konferencija, 36. simpozijum „CIM u strategiji tehnološkog razvoja industrije prerade metala“, Zbornik radova / 43rd JUPITER Conference, Proceedings, Beograd, oktobar 2022. 2022;:1.7-1.16.
https://hdl.handle.net/21.15107/rcub_machinery_5278 .

Jakovljević, Živana, Nedeljković, Dušan, "Sajber bezbednost u kontinualnim sistemima upravljanja – pregled rezultata u okviru projekta mission4.0" in 43. JUPITER konferencija, 36. simpozijum „CIM u strategiji tehnološkog razvoja industrije prerade metala“, Zbornik radova / 43rd JUPITER Conference, Proceedings, Beograd, oktobar 2022 (2022):1.7-1.16,
https://hdl.handle.net/21.15107/rcub_machinery_5278 .

TY  - GEN
AU  - Nedeljković, Dušan
AU  - Jakovljević, Živana
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/5658
AB  - Tehničko rešenje (nova metoda - M85) pripada oblasti proizvodnog mašinstva i direktno se odnosi na jedan od domena istraživanja u okviru projekta „Deep Machine Learning and Swarm Intelligence-based Optimization Algorithms for Control and Scheduling of Cyber-Physical Systems in Industry 4.0“ (akronim - MISSION4.0, evidencioni broj 6523109), koji je finansiran od strane Fonda za nauku Republike Srbije – domen razvoja sistema za detekciju napada u cilju zaštite kibernetsko fizičkih sistema u okviru postrojenja Industrije 4.0. Shodno tome, metodom se rešava problem detekcije sajber napada na bazi signala dobijenih sa senzora raspoređenih unutar postrojenja. Sistem detekcije zasnovan je na modelu koji je dobijen dubokim mašinskim učenjem (DL - engl. Deep Learning). Konkretno, za modeliranje ponašanja sistema korišćeni su različiti tipovi rekurentnih neuronskih mreža (RNN - engl. Recurrent Neural Networks), kao i konvolucione neuronske mreže (CNN - engl. Convolutional Neural Networks). Predloženi algoritam je eksperimentalno verifikovan na dva javno dostupna skupa podataka: 1) SWaT (Secure Water Treatment) skup podataka i 2) skup podataka dobijen sa elektropneumatskog sistema za pozicioniranje koji je razvijen u okviru Laboratorije za automatizaciju proizvodnih procesa Mašinskog fakulteta Univerziteta u Beogradu.
T2  - Tehničko rešenje je prihvaćeno od strane Matičnog naučnog odbora za mašinstvo i industrijski softver, 2022.
T1  - Algoritam za detekciju sajber napada kod energetski ograničenih kibernetsko fizičkih sistema baziran na dubokom mašinskom učenju
UR  - https://hdl.handle.net/21.15107/rcub_machinery_5658
ER  - 

@misc{
author = "Nedeljković, Dušan and Jakovljević, Živana",
year = "2022",
abstract = "Tehničko rešenje (nova metoda - M85) pripada oblasti proizvodnog mašinstva i direktno se odnosi na jedan od domena istraživanja u okviru projekta „Deep Machine Learning and Swarm Intelligence-based Optimization Algorithms for Control and Scheduling of Cyber-Physical Systems in Industry 4.0“ (akronim - MISSION4.0, evidencioni broj 6523109), koji je finansiran od strane Fonda za nauku Republike Srbije – domen razvoja sistema za detekciju napada u cilju zaštite kibernetsko fizičkih sistema u okviru postrojenja Industrije 4.0. Shodno tome, metodom se rešava problem detekcije sajber napada na bazi signala dobijenih sa senzora raspoređenih unutar postrojenja. Sistem detekcije zasnovan je na modelu koji je dobijen dubokim mašinskim učenjem (DL - engl. Deep Learning). Konkretno, za modeliranje ponašanja sistema korišćeni su različiti tipovi rekurentnih neuronskih mreža (RNN - engl. Recurrent Neural Networks), kao i konvolucione neuronske mreže (CNN - engl. Convolutional Neural Networks). Predloženi algoritam je eksperimentalno verifikovan na dva javno dostupna skupa podataka: 1) SWaT (Secure Water Treatment) skup podataka i 2) skup podataka dobijen sa elektropneumatskog sistema za pozicioniranje koji je razvijen u okviru Laboratorije za automatizaciju proizvodnih procesa Mašinskog fakulteta Univerziteta u Beogradu.",
journal = "Tehničko rešenje je prihvaćeno od strane Matičnog naučnog odbora za mašinstvo i industrijski softver, 2022.",
title = "Algoritam za detekciju sajber napada kod energetski ograničenih kibernetsko fizičkih sistema baziran na dubokom mašinskom učenju",
url = "https://hdl.handle.net/21.15107/rcub_machinery_5658"
}

Nedeljković, D.,& Jakovljević, Ž.. (2022). Algoritam za detekciju sajber napada kod energetski ograničenih kibernetsko fizičkih sistema baziran na dubokom mašinskom učenju. in Tehničko rešenje je prihvaćeno od strane Matičnog naučnog odbora za mašinstvo i industrijski softver, 2022..
https://hdl.handle.net/21.15107/rcub_machinery_5658

Nedeljković D, Jakovljević Ž. Algoritam za detekciju sajber napada kod energetski ograničenih kibernetsko fizičkih sistema baziran na dubokom mašinskom učenju. in Tehničko rešenje je prihvaćeno od strane Matičnog naučnog odbora za mašinstvo i industrijski softver, 2022.. 2022;.
https://hdl.handle.net/21.15107/rcub_machinery_5658 .

Nedeljković, Dušan, Jakovljević, Živana, "Algoritam za detekciju sajber napada kod energetski ograničenih kibernetsko fizičkih sistema baziran na dubokom mašinskom učenju" in Tehničko rešenje je prihvaćeno od strane Matičnog naučnog odbora za mašinstvo i industrijski softver, 2022. (2022),
https://hdl.handle.net/21.15107/rcub_machinery_5658 .

TY  - CONF
AU  - Nedeljković, Dušan
AU  - Jakovljević, Živana
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/5277
AB  - The advent of the Industry 4.0 paradigm that relies on the concepts of Cyber-Physical Systems (CPS) and the Industrial Internet of Things (IIoT) leads to the transition from centralized to distributed control. In this approach, interconnected smart devices (sensors, actuators, etc.) as the key enablers achieve system control through coordinated work. Introduction of IIoT leads to ubiquitous communication between smart devices, thus opening up a vast area for potential malicious threats and attacks which can cause serious consequences, take to system dysfunction or even endanger human lives. Therefore, security mechanisms have to be developed to provide timely detection of different cyber-attacks and to keep the system safe and protected. Since industrial processes are often very complex and their analytical model is very difficult to determine, deep learning based methods for cyber-security mechanisms development are imposed as a technique of choice. Successful employment of data-driven solutions, particularly based on deep learning approaches usually requires a big amount of data. However, due to various limitations in the acquisition of data from the real process, its availability is still a major challenge. For instance, the Industry 4.0 factory implies frequent reconfiguration which reduces the time intervals available for experimental procedures such as data acquisition. One of the ways to deal with this issue is called data augmentation. In this paper, we apply data augmentation in the design of cyber-attack detection methods in Industrial Control Systems (ICS). In particular, we explore the possibilities for utilization of Generative Adversarial Networks (GAN) to generate the necessary amount of data for deep learning based modeling sing a relatively small number of available samples on input.
PB  - ETRAN Society, Belgrade, Academic Mind, Belgrade
C3  - 9th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN 2022), Proceedings, Novi Pazar, June 2022, ROI1.4
T1  - Gan-based data augmentation in the design of Cyber-attack detection methods
SP  - ROI1.4
UR  - https://hdl.handle.net/21.15107/rcub_machinery_5277
ER  - 

@conference{
author = "Nedeljković, Dušan and Jakovljević, Živana",
year = "2022",
abstract = "The advent of the Industry 4.0 paradigm that relies on the concepts of Cyber-Physical Systems (CPS) and the Industrial Internet of Things (IIoT) leads to the transition from centralized to distributed control. In this approach, interconnected smart devices (sensors, actuators, etc.) as the key enablers achieve system control through coordinated work. Introduction of IIoT leads to ubiquitous communication between smart devices, thus opening up a vast area for potential malicious threats and attacks which can cause serious consequences, take to system dysfunction or even endanger human lives. Therefore, security mechanisms have to be developed to provide timely detection of different cyber-attacks and to keep the system safe and protected. Since industrial processes are often very complex and their analytical model is very difficult to determine, deep learning based methods for cyber-security mechanisms development are imposed as a technique of choice. Successful employment of data-driven solutions, particularly based on deep learning approaches usually requires a big amount of data. However, due to various limitations in the acquisition of data from the real process, its availability is still a major challenge. For instance, the Industry 4.0 factory implies frequent reconfiguration which reduces the time intervals available for experimental procedures such as data acquisition. One of the ways to deal with this issue is called data augmentation. In this paper, we apply data augmentation in the design of cyber-attack detection methods in Industrial Control Systems (ICS). In particular, we explore the possibilities for utilization of Generative Adversarial Networks (GAN) to generate the necessary amount of data for deep learning based modeling sing a relatively small number of available samples on input.",
publisher = "ETRAN Society, Belgrade, Academic Mind, Belgrade",
journal = "9th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN 2022), Proceedings, Novi Pazar, June 2022, ROI1.4",
title = "Gan-based data augmentation in the design of Cyber-attack detection methods",
pages = "ROI1.4",
url = "https://hdl.handle.net/21.15107/rcub_machinery_5277"
}

Nedeljković, D.,& Jakovljević, Ž.. (2022). Gan-based data augmentation in the design of Cyber-attack detection methods. in 9th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN 2022), Proceedings, Novi Pazar, June 2022, ROI1.4
ETRAN Society, Belgrade, Academic Mind, Belgrade., ROI1.4.
https://hdl.handle.net/21.15107/rcub_machinery_5277

Nedeljković D, Jakovljević Ž. Gan-based data augmentation in the design of Cyber-attack detection methods. in 9th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN 2022), Proceedings, Novi Pazar, June 2022, ROI1.4. 2022;:ROI1.4.
https://hdl.handle.net/21.15107/rcub_machinery_5277 .

Nedeljković, Dušan, Jakovljević, Živana, "Gan-based data augmentation in the design of Cyber-attack detection methods" in 9th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN 2022), Proceedings, Novi Pazar, June 2022, ROI1.4 (2022):ROI1.4,
https://hdl.handle.net/21.15107/rcub_machinery_5277 .

TY  - CONF
AU  - Jakovljević, Živana
AU  - Nedeljković, Dušan
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3801
AB  - Implementation of Industry 4.0 concept in manufacturing environment requires the education of a new generation of engineers capable to address all the challenges that this industrial (r)evolution brings about. Cyber Physical Systems (CPS) represent technological basis of Industry 4.0 and the education of engineers in this highly interdisciplinary area is a paramount for successful implementation of Industry 4.0. In this paper we analyze expected levels of CPS implementation in manufacturing environment, opportunities that they offer with respect to manufacturing customization and high product variety, as well as the changes, challenges and threats that these systems introduce. Based on this analysis, the paper presents the most important topics that should be covered in manufacturing engineers’ education to provide them with necessary skills and competences, making them capable to effectively design and implement CPS based solutions and Industry 4.0 concept at factory shop-floor.
PB  - Springer Science and Business Media B.V.
C3  - Mechanisms and Machine Science
T1  - Cyber Physical Systems in Manufacturing Engineers Education
EP  - 743
SP  - 735
VL  - 109
DO  - 10.1007/978-3-030-88465-9_75
ER  - 

@conference{
author = "Jakovljević, Živana and Nedeljković, Dušan",
year = "2022",
abstract = "Implementation of Industry 4.0 concept in manufacturing environment requires the education of a new generation of engineers capable to address all the challenges that this industrial (r)evolution brings about. Cyber Physical Systems (CPS) represent technological basis of Industry 4.0 and the education of engineers in this highly interdisciplinary area is a paramount for successful implementation of Industry 4.0. In this paper we analyze expected levels of CPS implementation in manufacturing environment, opportunities that they offer with respect to manufacturing customization and high product variety, as well as the changes, challenges and threats that these systems introduce. Based on this analysis, the paper presents the most important topics that should be covered in manufacturing engineers’ education to provide them with necessary skills and competences, making them capable to effectively design and implement CPS based solutions and Industry 4.0 concept at factory shop-floor.",
publisher = "Springer Science and Business Media B.V.",
journal = "Mechanisms and Machine Science",
title = "Cyber Physical Systems in Manufacturing Engineers Education",
pages = "743-735",
volume = "109",
doi = "10.1007/978-3-030-88465-9_75"
}

Jakovljević, Ž.,& Nedeljković, D.. (2022). Cyber Physical Systems in Manufacturing Engineers Education. in Mechanisms and Machine Science
Springer Science and Business Media B.V.., 109, 735-743.
https://doi.org/10.1007/978-3-030-88465-9_75

Jakovljević Ž, Nedeljković D. Cyber Physical Systems in Manufacturing Engineers Education. in Mechanisms and Machine Science. 2022;109:735-743.
doi:10.1007/978-3-030-88465-9_75 .

Jakovljević, Živana, Nedeljković, Dušan, "Cyber Physical Systems in Manufacturing Engineers Education" in Mechanisms and Machine Science, 109 (2022):735-743,
https://doi.org/10.1007/978-3-030-88465-9_75 . .

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  - GEN
AU  - Petrović, Milica
AU  - Jokić, Aleksandar
AU  - Babić, Bojan
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4355
AB  - Техничко решење (нова метода - М85) припада области производног машинства и директно се односи на један од домена истраживања у оквиру пројекта „Deep Machine Learning and Swarm Intelligence-based Optimization Algorithms for Control and Scheduling of Cyber-Physical Systems in Industry 4.0“ (акроним - MISSION4.0, евиденциони број 6523109), који је финансиран од стране Фонда за науку Републике Србије – домен развоја система за доношење одлука мобилних робота на бази дубоког машинског учења и вишекритеријумске оптимизације технолошког система Индустрије 4.0.
T2  - Техничко решење је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер
T1  - Intelligent Mobile Robot Multi-objective Decision-making System based on Metaheuristic Optimization and Deep Machine Learning
T1  - Вишекритеријумско одлучивање интелигентног мобилног робота на бази метода метахеуристичке оптимизације и дубоког машинског учења
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4355
ER  - 

@misc{
author = "Petrović, Milica and Jokić, Aleksandar and Babić, Bojan",
year = "2022",
abstract = "Техничко решење (нова метода - М85) припада области производног машинства и директно се односи на један од домена истраживања у оквиру пројекта „Deep Machine Learning and Swarm Intelligence-based Optimization Algorithms for Control and Scheduling of Cyber-Physical Systems in Industry 4.0“ (акроним - MISSION4.0, евиденциони број 6523109), који је финансиран од стране Фонда за науку Републике Србије – домен развоја система за доношење одлука мобилних робота на бази дубоког машинског учења и вишекритеријумске оптимизације технолошког система Индустрије 4.0.",
journal = "Техничко решење је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер",
title = "Intelligent Mobile Robot Multi-objective Decision-making System based on Metaheuristic Optimization and Deep Machine Learning, Вишекритеријумско одлучивање интелигентног мобилног робота на бази метода метахеуристичке оптимизације и дубоког машинског учења",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4355"
}

Petrović, M., Jokić, A.,& Babić, B.. (2022). Intelligent Mobile Robot Multi-objective Decision-making System based on Metaheuristic Optimization and Deep Machine Learning. in Техничко решење је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер.
https://hdl.handle.net/21.15107/rcub_machinery_4355

Petrović M, Jokić A, Babić B. Intelligent Mobile Robot Multi-objective Decision-making System based on Metaheuristic Optimization and Deep Machine Learning. in Техничко решење је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер. 2022;.
https://hdl.handle.net/21.15107/rcub_machinery_4355 .

Petrović, Milica, Jokić, Aleksandar, Babić, Bojan, "Intelligent Mobile Robot Multi-objective Decision-making System based on Metaheuristic Optimization and Deep Machine Learning" in Техничко решење је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер (2022),
https://hdl.handle.net/21.15107/rcub_machinery_4355 .

TY  - CONF
AU  - Jokić, Aleksandar
AU  - Petrović, Milica
AU  - Miljković, Zoran
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4230
AB  - 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).
C3  - Proceedings / 9th International Conference on Electrical, Electronics and Computing Engineering (IcETRAN 2022) Novi Pazar, Serbia, 6-9, June, 2022
T1  - Mobile robot decision-making system based on deep machine learning
EP  - 638
SP  - 635
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4230
ER  - 

@conference{
author = "Jokić, Aleksandar and Petrović, Milica and Miljković, Zoran",
year = "2022",
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).",
journal = "Proceedings / 9th International Conference on Electrical, Electronics and Computing Engineering (IcETRAN 2022) Novi Pazar, Serbia, 6-9, June, 2022",
title = "Mobile robot decision-making system based on deep machine learning",
pages = "638-635",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4230"
}

Jokić, A., Petrović, M.,& Miljković, Z.. (2022). Mobile robot decision-making system based on deep machine learning. in Proceedings / 9th International Conference on Electrical, Electronics and Computing Engineering (IcETRAN 2022) Novi Pazar, Serbia, 6-9, June, 2022, 635-638.
https://hdl.handle.net/21.15107/rcub_machinery_4230

Jokić A, Petrović M, Miljković Z. Mobile robot decision-making system based on deep machine learning. in Proceedings / 9th International Conference on Electrical, Electronics and Computing Engineering (IcETRAN 2022) Novi Pazar, Serbia, 6-9, June, 2022. 2022;:635-638.
https://hdl.handle.net/21.15107/rcub_machinery_4230 .

Jokić, Aleksandar, Petrović, Milica, Miljković, Zoran, "Mobile robot decision-making system based on deep machine learning" in Proceedings / 9th International Conference on Electrical, Electronics and Computing Engineering (IcETRAN 2022) Novi Pazar, Serbia, 6-9, June, 2022 (2022):635-638,
https://hdl.handle.net/21.15107/rcub_machinery_4230 .

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  - CONF
AU  - Jokić, Aleksandar
AU  - Petrović, Milica
AU  - Miljković, Zoran
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3815
AB  - The recent advances in deep learning models have enabled the robotics community to utilize their potential. The mobile robot domain on which deep learning has the most influence is scene understanding. Scene understanding enables mobile robots to exist and execute their tasks through processes such as object detection, semantic segmentation, or instance segmentation. A perception system that can recognize and locate objects in the scene is of the highest importance for achieving autonomous behavior of robotic systems. Having that in mind, we develop the mobile robot perception system based on deep learning. More precisely, we utilize an accurate and fast Convolution Neural Network (CNN) model to enable a mobile robot to detect objects in its scene in a real-time manner. The integration of two CNN models (SSD and MobileNet) is performed and implemented on mobile robot RAICO (Robot with Artificial Intelligence based COgnition). The experimental results show that the proposed perception system enables a high degree of object recognition with satisfying inference speed, even with limited processing power provided by Nvidia Jetson Nano integrated within RACIO.
PB  - Springer Science and Business Media Deutschland GmbH
C3  - Lecture Notes in Networks and Systems
T1  - Real-Time Mobile Robot Perception Based on Deep Learning Detection Model
EP  - 677
SP  - 670
VL  - 472
DO  - 10.1007/978-3-031-05230-9_80
ER  - 

@conference{
author = "Jokić, Aleksandar and Petrović, Milica and Miljković, Zoran",
year = "2022",
abstract = "The recent advances in deep learning models have enabled the robotics community to utilize their potential. The mobile robot domain on which deep learning has the most influence is scene understanding. Scene understanding enables mobile robots to exist and execute their tasks through processes such as object detection, semantic segmentation, or instance segmentation. A perception system that can recognize and locate objects in the scene is of the highest importance for achieving autonomous behavior of robotic systems. Having that in mind, we develop the mobile robot perception system based on deep learning. More precisely, we utilize an accurate and fast Convolution Neural Network (CNN) model to enable a mobile robot to detect objects in its scene in a real-time manner. The integration of two CNN models (SSD and MobileNet) is performed and implemented on mobile robot RAICO (Robot with Artificial Intelligence based COgnition). The experimental results show that the proposed perception system enables a high degree of object recognition with satisfying inference speed, even with limited processing power provided by Nvidia Jetson Nano integrated within RACIO.",
publisher = "Springer Science and Business Media Deutschland GmbH",
journal = "Lecture Notes in Networks and Systems",
title = "Real-Time Mobile Robot Perception Based on Deep Learning Detection Model",
pages = "677-670",
volume = "472",
doi = "10.1007/978-3-031-05230-9_80"
}

Jokić, A., Petrović, M.,& Miljković, Z.. (2022). Real-Time Mobile Robot Perception Based on Deep Learning Detection Model. in Lecture Notes in Networks and Systems
Springer Science and Business Media Deutschland GmbH., 472, 670-677.
https://doi.org/10.1007/978-3-031-05230-9_80

Jokić A, Petrović M, Miljković Z. Real-Time Mobile Robot Perception Based on Deep Learning Detection Model. in Lecture Notes in Networks and Systems. 2022;472:670-677.
doi:10.1007/978-3-031-05230-9_80 .

Jokić, Aleksandar, Petrović, Milica, Miljković, Zoran, "Real-Time Mobile Robot Perception Based on Deep Learning Detection Model" in Lecture Notes in Networks and Systems, 472 (2022):670-677,
https://doi.org/10.1007/978-3-031-05230-9_80 . .

TY  - GEN
AU  - Miljković, Zoran
AU  - Jevtić, Đorđe
AU  - Svorcan, Jelena
PY  - 2021
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/6625
AB  - Roboti koji imaju mogućnost kretanja uz vertikalnu podlogu (engl. wall-climbing), a u koje spadaju i roboti namenjeni posebnim zadacima kao što je čišćenje gabaritnih staklenih površina eksterijera visokih zgrada (engl. glass and façade-cleaning), predstavljaju predmet brojnih istraživanja u prethodnih trideset godina. Interesovanja naučne i stručne zajednice u ovoj oblasti su posledica pre svega njihovog velikog potencijala za rešavanje mnogobrojnih izazova kao što su održavanje i inspekcija građevinskih konstrukcija, ispitivanja teško dostupnih ili veoma opasnih radnih mesta i dr. Iako je na ovom polju ostvaren vidan napredak poslednjih nekoliko godina, ova tehnologija i dalje poseduje određena ograničenja kao što su nemogućnost kontinualnog kretanja ovih robota po fizički odvojenim površinama visokih zgrada i nemogućnost kretanja po neravnim površinama. Male bespilotne letelice (MBL) predstavljaju aktuelni tip letelica poslednjih dvadeset godina, a koje mogu da se korisno upotrebe u širokom opsegu primena. Minijaturizacija i smanjenje troškova električnih komponenti doveli su do njihove komercijalizacije i masovne upotrebe u mnogim oblastima, kao što su gašenje požara, inspekcija, nadzor, farbanje, održavanje vetro-generatora i brodova. Najveću primenu u praksi pronašli su kvadkopteri, pre svega zbog činjenice da se pomoću četiri veličine, tj. upravljanjem brzinama obrta propelera, može ostvariti šest stepeni slobode pri kretanju. Prednosti ovih letelica su dobre manevarske sposobnosti i jednostavno upravljanje. Međutim, smanjenje njihovih dimenzija dovodi do smanjenja efikasnosti, kao i povećanja viskoznih efekata što je posledica malih Rejnoldsovih brojeva. Koncept predstavljen ovim predavanjem po pozivu podrazumeva sledeće prioritete: 1. Realizovati robotski sistem za čišćenje koji treba da omogući kvalitetno obavljanje ove namene. 2. Ostvariti bezbednu, pouzdanu i laku tranziciju. 3. Osmisliti sistem za prianjanje koji će omogućiti robotu potrebnu mobilnost u svim pravcima pri izrvšavanju zadatka na radnim površinama različitih oblika uz minimalni utrošak energije. 4. Obezbediti robotu mogućnost savladavanja prepreka malih dimenzija radi izbegavanja potrebe za čestom tranzicijom. 5. Ostvariti bezbednost u radu i u slučaju neplaniranog otkaza sistema za prianjanje. 6. Omogućiti bezbedno spuštanje robota na tlo usled otkaza nekog od motora namenjenih generisanju vučne sile. Zadatak koji se postavlja pred mobilni robot letelicu je učenje optimalne putanje od početnog položaja (predstavlja položaj robota u neposrednoj blizini objekta) do ciljnog položaja predstavlja položaj robota na radnoj površini. Određivanje ciljnog položaja se ostvaruje aktiviranjem kamere kao spoljašnjeg senzora koja uz pomoć metoda mašinskog gledanja omogućava mobilnom robotu letelici detektovanje staklenih površina, kao i izdvajanje karakterističnih objekata. Kako je u okviru navedenog problema nemoguće odrediti tačan matematički model kretanja mobilnog robota letelice, odnosno okruženja, uvodi se pojam mašinskog učenja ojačavanjem (engl. Reinforcement Learning). Dva osnovna činioca koncepta mašinskog učenja ojačavanjem predstavljaju inteligentni agent i okruženje. Inteligentni agent ima zadatak da istraživanjem okruženja pomoću eksternih senzora kao i eksploatacijom stečenog znanja generiše optimalno ponašanje u cilju izvršavanja postavljenog tehnološkog zadatka. Stanje sistema se može definisati kao skup komponenti nastalih kao rezultat akcija robota i/ili rezultat interakcije robot okruženje. Stanja mogu činiti sledeći elementi: položaj robota u okruženju u odnosu na izabrani referentni koordinatni sistem, njegova brzina kretanja, položaj i brzina kretanja pokretnih objekata u okruženju i sl. Nagradna oceana stanja (engl. Reward ) se definiše kao stepen uspešnosti odabrane akcije u prethodnom stanju sistema izražene u vidu numeričke vrednosti. Cilj agenta jeste da pronađe skup najpovoljnijih akcija u svim stanjima mobilnog robota letelice pri njenom kretanju od početnog do ciljnog položaja. Na osnovu informacija o trenutnom stanju sistema kao i trenutnom stepenu obučenosti, inteligentni agent treba da odabere akciju koja će ga dovesti u naredni položaj. Ovaj položaj predstavlja ulaz u upravljačku jedinicu za kontrolu položaja koja treba da odredi signale na ulazu u kontrolere rada električnih motora kako bi se generisale odgovarajuće vučne sile. Stanje sistema u razmatranom primeru predstavlja položaj mobilnog robota letelice u režimu lebdenja. Okruženje je sastavljeno od sfera jednakih prečnika, čiji su centri moguća stanja sistema. U konkretnom primeru razmatrani prostor je dimenzija 9000x6000x4000mm, i
PB  - Mašinski fakultet Univerziteta u Beogradu
T2  - 7. Kongres studenata tehnike - "TEHNOLOGIJE MODERNOG INŽENjERSTVA"
T1  - AUTONOMNOST KRETANjA MOBILNOG ROBOTA -LETELICE ZA RAD NA VISINAMA – SPECIFIČNOSTI KONFIGURACIJE, MODELIRANjE, FUNKCIONALNA APROKSIMACIJA I MAŠINSKO UČENjE OJAČAVANjEM
UR  - https://hdl.handle.net/21.15107/rcub_machinery_6625
ER  - 

@misc{
author = "Miljković, Zoran and Jevtić, Đorđe and Svorcan, Jelena",
year = "2021",
abstract = "Roboti koji imaju mogućnost kretanja uz vertikalnu podlogu (engl. wall-climbing), a u koje spadaju i roboti namenjeni posebnim zadacima kao što je čišćenje gabaritnih staklenih površina eksterijera visokih zgrada (engl. glass and façade-cleaning), predstavljaju predmet brojnih istraživanja u prethodnih trideset godina. Interesovanja naučne i stručne zajednice u ovoj oblasti su posledica pre svega njihovog velikog potencijala za rešavanje mnogobrojnih izazova kao što su održavanje i inspekcija građevinskih konstrukcija, ispitivanja teško dostupnih ili veoma opasnih radnih mesta i dr. Iako je na ovom polju ostvaren vidan napredak poslednjih nekoliko godina, ova tehnologija i dalje poseduje određena ograničenja kao što su nemogućnost kontinualnog kretanja ovih robota po fizički odvojenim površinama visokih zgrada i nemogućnost kretanja po neravnim površinama. Male bespilotne letelice (MBL) predstavljaju aktuelni tip letelica poslednjih dvadeset godina, a koje mogu da se korisno upotrebe u širokom opsegu primena. Minijaturizacija i smanjenje troškova električnih komponenti doveli su do njihove komercijalizacije i masovne upotrebe u mnogim oblastima, kao što su gašenje požara, inspekcija, nadzor, farbanje, održavanje vetro-generatora i brodova. Najveću primenu u praksi pronašli su kvadkopteri, pre svega zbog činjenice da se pomoću četiri veličine, tj. upravljanjem brzinama obrta propelera, može ostvariti šest stepeni slobode pri kretanju. Prednosti ovih letelica su dobre manevarske sposobnosti i jednostavno upravljanje. Međutim, smanjenje njihovih dimenzija dovodi do smanjenja efikasnosti, kao i povećanja viskoznih efekata što je posledica malih Rejnoldsovih brojeva. Koncept predstavljen ovim predavanjem po pozivu podrazumeva sledeće prioritete: 1. Realizovati robotski sistem za čišćenje koji treba da omogući kvalitetno obavljanje ove namene. 2. Ostvariti bezbednu, pouzdanu i laku tranziciju. 3. Osmisliti sistem za prianjanje koji će omogućiti robotu potrebnu mobilnost u svim pravcima pri izrvšavanju zadatka na radnim površinama različitih oblika uz minimalni utrošak energije. 4. Obezbediti robotu mogućnost savladavanja prepreka malih dimenzija radi izbegavanja potrebe za čestom tranzicijom. 5. Ostvariti bezbednost u radu i u slučaju neplaniranog otkaza sistema za prianjanje. 6. Omogućiti bezbedno spuštanje robota na tlo usled otkaza nekog od motora namenjenih generisanju vučne sile. Zadatak koji se postavlja pred mobilni robot letelicu je učenje optimalne putanje od početnog položaja (predstavlja položaj robota u neposrednoj blizini objekta) do ciljnog položaja predstavlja položaj robota na radnoj površini. Određivanje ciljnog položaja se ostvaruje aktiviranjem kamere kao spoljašnjeg senzora koja uz pomoć metoda mašinskog gledanja omogućava mobilnom robotu letelici detektovanje staklenih površina, kao i izdvajanje karakterističnih objekata. Kako je u okviru navedenog problema nemoguće odrediti tačan matematički model kretanja mobilnog robota letelice, odnosno okruženja, uvodi se pojam mašinskog učenja ojačavanjem (engl. Reinforcement Learning). Dva osnovna činioca koncepta mašinskog učenja ojačavanjem predstavljaju inteligentni agent i okruženje. Inteligentni agent ima zadatak da istraživanjem okruženja pomoću eksternih senzora kao i eksploatacijom stečenog znanja generiše optimalno ponašanje u cilju izvršavanja postavljenog tehnološkog zadatka. Stanje sistema se može definisati kao skup komponenti nastalih kao rezultat akcija robota i/ili rezultat interakcije robot okruženje. Stanja mogu činiti sledeći elementi: položaj robota u okruženju u odnosu na izabrani referentni koordinatni sistem, njegova brzina kretanja, položaj i brzina kretanja pokretnih objekata u okruženju i sl. Nagradna oceana stanja (engl. Reward ) se definiše kao stepen uspešnosti odabrane akcije u prethodnom stanju sistema izražene u vidu numeričke vrednosti. Cilj agenta jeste da pronađe skup najpovoljnijih akcija u svim stanjima mobilnog robota letelice pri njenom kretanju od početnog do ciljnog položaja. Na osnovu informacija o trenutnom stanju sistema kao i trenutnom stepenu obučenosti, inteligentni agent treba da odabere akciju koja će ga dovesti u naredni položaj. Ovaj položaj predstavlja ulaz u upravljačku jedinicu za kontrolu položaja koja treba da odredi signale na ulazu u kontrolere rada električnih motora kako bi se generisale odgovarajuće vučne sile. Stanje sistema u razmatranom primeru predstavlja položaj mobilnog robota letelice u režimu lebdenja. Okruženje je sastavljeno od sfera jednakih prečnika, čiji su centri moguća stanja sistema. U konkretnom primeru razmatrani prostor je dimenzija 9000x6000x4000mm, i",
publisher = "Mašinski fakultet Univerziteta u Beogradu",
journal = "7. Kongres studenata tehnike - "TEHNOLOGIJE MODERNOG INŽENjERSTVA"",
title = "AUTONOMNOST KRETANjA MOBILNOG ROBOTA -LETELICE ZA RAD NA VISINAMA – SPECIFIČNOSTI KONFIGURACIJE, MODELIRANjE, FUNKCIONALNA APROKSIMACIJA I MAŠINSKO UČENjE OJAČAVANjEM",
url = "https://hdl.handle.net/21.15107/rcub_machinery_6625"
}

Miljković, Z., Jevtić, Đ.,& Svorcan, J.. (2021). AUTONOMNOST KRETANjA MOBILNOG ROBOTA -LETELICE ZA RAD NA VISINAMA – SPECIFIČNOSTI KONFIGURACIJE, MODELIRANjE, FUNKCIONALNA APROKSIMACIJA I MAŠINSKO UČENjE OJAČAVANjEM. in 7. Kongres studenata tehnike - "TEHNOLOGIJE MODERNOG INŽENjERSTVA"
Mašinski fakultet Univerziteta u Beogradu..
https://hdl.handle.net/21.15107/rcub_machinery_6625

Miljković Z, Jevtić Đ, Svorcan J. AUTONOMNOST KRETANjA MOBILNOG ROBOTA -LETELICE ZA RAD NA VISINAMA – SPECIFIČNOSTI KONFIGURACIJE, MODELIRANjE, FUNKCIONALNA APROKSIMACIJA I MAŠINSKO UČENjE OJAČAVANjEM. in 7. Kongres studenata tehnike - "TEHNOLOGIJE MODERNOG INŽENjERSTVA". 2021;.
https://hdl.handle.net/21.15107/rcub_machinery_6625 .

Miljković, Zoran, Jevtić, Đorđe, Svorcan, Jelena, "AUTONOMNOST KRETANjA MOBILNOG ROBOTA -LETELICE ZA RAD NA VISINAMA – SPECIFIČNOSTI KONFIGURACIJE, MODELIRANjE, FUNKCIONALNA APROKSIMACIJA I MAŠINSKO UČENjE OJAČAVANjEM" in 7. Kongres studenata tehnike - "TEHNOLOGIJE MODERNOG INŽENjERSTVA" (2021),
https://hdl.handle.net/21.15107/rcub_machinery_6625 .