TY  - CONF
AU  - Jokić, Aleksandar
AU  - Khazraei, Amir
AU  - Petrović, Milica
AU  - Jakovljević, Živana
AU  - Pajić, Miroslav
PY  - 2023
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/7676
AB  - Visual servoing represents a control strategy
capable of driving dynamical systems from the current to the
desired pose, when the only available information is the images
generated at both poses. In this work, we analyze vulnerability
of such systems and introduce two types of attacks to deceive
visual servoing controller within a wheeled mobile robotic
system. The attack goal is to alter the visual servoing procedure
in such a way that mobile robot achieves the pose defined by an
attacker instead of the desired one. Specifically, the attacks
exploit image transformations developed using a methodology
based on simulated annealing. The main difference between the
attacks is the considered threat model – i.e., how the attacker
has infiltrated the system. The first attack assumes the realtime camera feed has been compromised and thus, the images
from the current pose are modified (e.g., during the acquisition
or communication); for the second, only the desired destination
image is potentially altered. Finally, in 3D simulations and realworld experiments, we show the effectiveness of cyber-attacks.
C3  - 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
T1  - Cyber-Attacks on Wheeled Mobile Robotic Systems with Visual Servoing Control
EP  - 6348
SP  - 6342
DO  - 10.1109/IROS55552.2023.10341376
ER  - 

@conference{
author = "Jokić, Aleksandar and Khazraei, Amir and Petrović, Milica and Jakovljević, Živana and Pajić, Miroslav",
year = "2023",
abstract = "Visual servoing represents a control strategy
capable of driving dynamical systems from the current to the
desired pose, when the only available information is the images
generated at both poses. In this work, we analyze vulnerability
of such systems and introduce two types of attacks to deceive
visual servoing controller within a wheeled mobile robotic
system. The attack goal is to alter the visual servoing procedure
in such a way that mobile robot achieves the pose defined by an
attacker instead of the desired one. Specifically, the attacks
exploit image transformations developed using a methodology
based on simulated annealing. The main difference between the
attacks is the considered threat model – i.e., how the attacker
has infiltrated the system. The first attack assumes the realtime camera feed has been compromised and thus, the images
from the current pose are modified (e.g., during the acquisition
or communication); for the second, only the desired destination
image is potentially altered. Finally, in 3D simulations and realworld experiments, we show the effectiveness of cyber-attacks.",
journal = "2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)",
title = "Cyber-Attacks on Wheeled Mobile Robotic Systems with Visual Servoing Control",
pages = "6348-6342",
doi = "10.1109/IROS55552.2023.10341376"
}

Jokić, A., Khazraei, A., Petrović, M., Jakovljević, Ž.,& Pajić, M.. (2023). Cyber-Attacks on Wheeled Mobile Robotic Systems with Visual Servoing Control. in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 6342-6348.
https://doi.org/10.1109/IROS55552.2023.10341376

Jokić A, Khazraei A, Petrović M, Jakovljević Ž, Pajić M. Cyber-Attacks on Wheeled Mobile Robotic Systems with Visual Servoing Control. in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2023;:6342-6348.
doi:10.1109/IROS55552.2023.10341376 .

Jokić, Aleksandar, Khazraei, Amir, Petrović, Milica, Jakovljević, Živana, Pajić, Miroslav, "Cyber-Attacks on Wheeled Mobile Robotic Systems with Visual Servoing Control" in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2023):6342-6348,
https://doi.org/10.1109/IROS55552.2023.10341376 . .

TY  - CONF
AU  - Jokić, Aleksandar
AU  - Petrović, Milica
AU  - Miljković, Zoran
PY  - 2023
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/7004
AB  - In recent years, metaheuristic algorithms have become increasingly advantageous for solving many real-world optimization-based engineering tasks. Integrated process planning and scheduling of machine tools and mobile robots utilized for transportation tasks in a manufacturing environment represents one such task. Since the number of solutions increases exponentially with the addition of either parts, machines, or robots, this task belongs to a group of NP-hard problems. Therefore, for its successful resolution, it is essential to use efficient algorithms that are able to explore vast solution space and provide optimal solutions. In this paper, we propose an algorithm for solving integrated scheduling of machine tools and mobile robots based on a novel arithmetic metaheuristic optimization. The arithmetic optimization algorithm belongs to a group of stochastic population-based algorithms inspired by arithmetic mathematical operations. The main advantage of the proposed algorithm is in a well-suited balance between exploration and exploitation phases that are appropriate for extremely hard multi-objective optimization. A multi-objective metric is utilized to evaluate obtained Pareto front solutions in terms of the exploration capabilities in the solution space. The proposed algorithm is compared with two other state-of-the-art metaheuristic algorithms. The experimental evaluation is carried out on 20 benchmark problems, and the results show the advantages of the proposed algorithm.
C3  - 39th International Conference on Production Engineering of Serbia (ICPES 2023)
T1  - The Arithmetic Optimization Algorithm for  Multi-Objective Mobile Robot Scheduling
EP  - 15
SP  - 9
UR  - https://hdl.handle.net/21.15107/rcub_machinery_7004
ER  - 

@conference{
author = "Jokić, Aleksandar and Petrović, Milica and Miljković, Zoran",
year = "2023",
abstract = "In recent years, metaheuristic algorithms have become increasingly advantageous for solving many real-world optimization-based engineering tasks. Integrated process planning and scheduling of machine tools and mobile robots utilized for transportation tasks in a manufacturing environment represents one such task. Since the number of solutions increases exponentially with the addition of either parts, machines, or robots, this task belongs to a group of NP-hard problems. Therefore, for its successful resolution, it is essential to use efficient algorithms that are able to explore vast solution space and provide optimal solutions. In this paper, we propose an algorithm for solving integrated scheduling of machine tools and mobile robots based on a novel arithmetic metaheuristic optimization. The arithmetic optimization algorithm belongs to a group of stochastic population-based algorithms inspired by arithmetic mathematical operations. The main advantage of the proposed algorithm is in a well-suited balance between exploration and exploitation phases that are appropriate for extremely hard multi-objective optimization. A multi-objective metric is utilized to evaluate obtained Pareto front solutions in terms of the exploration capabilities in the solution space. The proposed algorithm is compared with two other state-of-the-art metaheuristic algorithms. The experimental evaluation is carried out on 20 benchmark problems, and the results show the advantages of the proposed algorithm.",
journal = "39th International Conference on Production Engineering of Serbia (ICPES 2023)",
title = "The Arithmetic Optimization Algorithm for  Multi-Objective Mobile Robot Scheduling",
pages = "15-9",
url = "https://hdl.handle.net/21.15107/rcub_machinery_7004"
}

Jokić, A., Petrović, M.,& Miljković, Z.. (2023). The Arithmetic Optimization Algorithm for  Multi-Objective Mobile Robot Scheduling. in 39th International Conference on Production Engineering of Serbia (ICPES 2023), 9-15.
https://hdl.handle.net/21.15107/rcub_machinery_7004

Jokić A, Petrović M, Miljković Z. The Arithmetic Optimization Algorithm for  Multi-Objective Mobile Robot Scheduling. in 39th International Conference on Production Engineering of Serbia (ICPES 2023). 2023;:9-15.
https://hdl.handle.net/21.15107/rcub_machinery_7004 .

Jokić, Aleksandar, Petrović, Milica, Miljković, Zoran, "The Arithmetic Optimization Algorithm for  Multi-Objective Mobile Robot Scheduling" in 39th International Conference on Production Engineering of Serbia (ICPES 2023) (2023):9-15,
https://hdl.handle.net/21.15107/rcub_machinery_7004 .

TY  - CHAP
AU  - Petrović, Milica
AU  - Miljković, Zoran
AU  - Jokić, Aleksandar
PY  - 2023
UR  - https://www.riotu-lab.org/airasbook/
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/6660
AB  - During the last decade, Convolutional Neural Networks (CNNs) have been recognized as one of the most promising machine learning methods that are being utilized for deep learning of autonomous robotic systems. Faced with everlasting uncertainties while working in unstructured and dynamical real-world environments, robotic systems need to be able to recognize different environmental scenarios and make adequate decisions based on machine learning of the current environment’s state representation. One of the main challenges in the development of machine learning models based on CNN is in the selection of appropriate model structure and parameters that can achieve adequate accuracy of environment representation. In order to address this challenge, the book chapter provides a comprehensive analysis of the accuracy and efficiency of CNN models for autonomous robotic applications. Particularly, different CNN models (i.e., structures and parameters) are trained, validated, and tested on real-world image data gathered by a mobile robot’s stereo vision system. The best performing CNN models based on two criteria – the number of frames per second and mean intersection over union are implemented on the real-world wheeled mobile robot RAICO (Robot with Artificial Intelligence based COgnition), which is developed in the Laboratory for robotics and artificial intelligence (ROBOTICS & AI) and tested for obstacle avoidance tasks. The achieved experimental results show that the proposed machine learning strategy based on CNN provides high accuracy of mobile robot’s current environment state estimation. This book addresses many applications of artificial intelligence in robotics, namely AI using visual and motional input. Robotic technology has made significant contributions to daily living, industrial uses, and medicinal applications. Machine learning, in particular, is critical for intelligent robots or unmanned/autonomous systems such as UAVs, UGVs, UUVs, cooperative robots, and so on. Humans are distinguished from animals by capacities such as receiving visual information, adjusting to uncertain circumstances, and making decisions to take action in a complex system. Significant progress has been made in robotics toward human-like intelligence; yet, there are still numerous unresolved issues. Deep learning, reinforcement learning, real-time learning, swarm intelligence, and other developing approaches such as tiny-ML have been developed in recent decades and used in robotics. Artificial intelligence is being integrated into robots in order to develop advanced robotics capable of performing multiple tasks and learning new things with a better perception of the environment, allowing robots to perform critical tasks with human-like vision to detect or recognize various objects. Intelligent robots have been successfully constructed using machine learning and deep learning AI technology. Robotics performance is improving as higher quality, and more precise machine learning processes are used to train computer vision models to recognize different things and carry out operations correctly with the desired outcome.
PB  - Springer Cham, SWITZERLAND
T2  - Chapter accepted for printing in the monograph book: "Artificial intelligence for Robotics and Autonomous Systems Applications", 1st ed. 2023, Edited by Prof. Ahmad Taher Azar and Prof. Anis Koubaa, Series Title: "Studies in Computational Intelligence", printed by Springer Cham
T1  - Efficient Machine Learning of  Mobile Robotic Systems based on Convolutional Neural Networks
VL  - 1093
UR  - https://hdl.handle.net/21.15107/rcub_machinery_6660
ER  - 

@inbook{
author = "Petrović, Milica and Miljković, Zoran and Jokić, Aleksandar",
year = "2023",
abstract = "During the last decade, Convolutional Neural Networks (CNNs) have been recognized as one of the most promising machine learning methods that are being utilized for deep learning of autonomous robotic systems. Faced with everlasting uncertainties while working in unstructured and dynamical real-world environments, robotic systems need to be able to recognize different environmental scenarios and make adequate decisions based on machine learning of the current environment’s state representation. One of the main challenges in the development of machine learning models based on CNN is in the selection of appropriate model structure and parameters that can achieve adequate accuracy of environment representation. In order to address this challenge, the book chapter provides a comprehensive analysis of the accuracy and efficiency of CNN models for autonomous robotic applications. Particularly, different CNN models (i.e., structures and parameters) are trained, validated, and tested on real-world image data gathered by a mobile robot’s stereo vision system. The best performing CNN models based on two criteria – the number of frames per second and mean intersection over union are implemented on the real-world wheeled mobile robot RAICO (Robot with Artificial Intelligence based COgnition), which is developed in the Laboratory for robotics and artificial intelligence (ROBOTICS & AI) and tested for obstacle avoidance tasks. The achieved experimental results show that the proposed machine learning strategy based on CNN provides high accuracy of mobile robot’s current environment state estimation. This book addresses many applications of artificial intelligence in robotics, namely AI using visual and motional input. Robotic technology has made significant contributions to daily living, industrial uses, and medicinal applications. Machine learning, in particular, is critical for intelligent robots or unmanned/autonomous systems such as UAVs, UGVs, UUVs, cooperative robots, and so on. Humans are distinguished from animals by capacities such as receiving visual information, adjusting to uncertain circumstances, and making decisions to take action in a complex system. Significant progress has been made in robotics toward human-like intelligence; yet, there are still numerous unresolved issues. Deep learning, reinforcement learning, real-time learning, swarm intelligence, and other developing approaches such as tiny-ML have been developed in recent decades and used in robotics. Artificial intelligence is being integrated into robots in order to develop advanced robotics capable of performing multiple tasks and learning new things with a better perception of the environment, allowing robots to perform critical tasks with human-like vision to detect or recognize various objects. Intelligent robots have been successfully constructed using machine learning and deep learning AI technology. Robotics performance is improving as higher quality, and more precise machine learning processes are used to train computer vision models to recognize different things and carry out operations correctly with the desired outcome.",
publisher = "Springer Cham, SWITZERLAND",
journal = "Chapter accepted for printing in the monograph book: "Artificial intelligence for Robotics and Autonomous Systems Applications", 1st ed. 2023, Edited by Prof. Ahmad Taher Azar and Prof. Anis Koubaa, Series Title: "Studies in Computational Intelligence", printed by Springer Cham",
booktitle = "Efficient Machine Learning of  Mobile Robotic Systems based on Convolutional Neural Networks",
volume = "1093",
url = "https://hdl.handle.net/21.15107/rcub_machinery_6660"
}

Petrović, M., Miljković, Z.,& Jokić, A.. (2023). Efficient Machine Learning of  Mobile Robotic Systems based on Convolutional Neural Networks. in Chapter accepted for printing in the monograph book: "Artificial intelligence for Robotics and Autonomous Systems Applications", 1st ed. 2023, Edited by Prof. Ahmad Taher Azar and Prof. Anis Koubaa, Series Title: "Studies in Computational Intelligence", printed by Springer Cham
Springer Cham, SWITZERLAND., 1093.
https://hdl.handle.net/21.15107/rcub_machinery_6660

Petrović M, Miljković Z, Jokić A. Efficient Machine Learning of  Mobile Robotic Systems based on Convolutional Neural Networks. in Chapter accepted for printing in the monograph book: "Artificial intelligence for Robotics and Autonomous Systems Applications", 1st ed. 2023, Edited by Prof. Ahmad Taher Azar and Prof. Anis Koubaa, Series Title: "Studies in Computational Intelligence", printed by Springer Cham. 2023;1093.
https://hdl.handle.net/21.15107/rcub_machinery_6660 .

Petrović, Milica, Miljković, Zoran, Jokić, Aleksandar, "Efficient Machine Learning of  Mobile Robotic Systems based on Convolutional Neural Networks" in Chapter accepted for printing in the monograph book: "Artificial intelligence for Robotics and Autonomous Systems Applications", 1st ed. 2023, Edited by Prof. Ahmad Taher Azar and Prof. Anis Koubaa, Series Title: "Studies in Computational Intelligence", printed by Springer Cham, 1093 (2023),
https://hdl.handle.net/21.15107/rcub_machinery_6660 .

TY  - CONF
AU  - Jevtić, Đorđe
AU  - Miljković, Zoran
AU  - Petrović, Milica
AU  - Jokić, Aleksandar
PY  - 2023
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/6896
AB  - One of the significant aspects for enabling the intelligent behavior to the Unmanned Aerial Vehicles (UAVs) is by providing an algorithm for navigation through the dynamic and unseen environment. Therefore, to be autonomous, they need sensors to perceive their surroundings and utilize gathered information to decide which action to take. Having that in mind, in this paper, the authors designed the system for obstacle avoidance and also investigate the elements of the Markov decision process and their influence on each other. The flying mobile robot used within the considered problem is quadrotor type and has an integrated Lidar sensor which is utilized to
detect obstacles. The sequential decision-making model based on Q-learning is trained within the MATLAB Simulink environment. The simulation results demonstrate that the UAV can navigate through the environment in most algorithm runs without colliding with surrounding obstacles.
PB  - ETRAN Society, The Society for Electronics, Telecommunications, Computing, Automatics and Nuclear engineering supported by IEEE
C3  - Proceedings of the 10th International Conference on Electrical, Electronics and Computing Engineering (IcETRAN 2023)
T1  - Reinforcement Learning-based Collision Avoidance for UAV
EP  - 6
IS  - 5496
SP  - 1
UR  - https://hdl.handle.net/21.15107/rcub_machinery_6896
ER  - 

@conference{
author = "Jevtić, Đorđe and Miljković, Zoran and Petrović, Milica and Jokić, Aleksandar",
year = "2023",
abstract = "One of the significant aspects for enabling the intelligent behavior to the Unmanned Aerial Vehicles (UAVs) is by providing an algorithm for navigation through the dynamic and unseen environment. Therefore, to be autonomous, they need sensors to perceive their surroundings and utilize gathered information to decide which action to take. Having that in mind, in this paper, the authors designed the system for obstacle avoidance and also investigate the elements of the Markov decision process and their influence on each other. The flying mobile robot used within the considered problem is quadrotor type and has an integrated Lidar sensor which is utilized to
detect obstacles. The sequential decision-making model based on Q-learning is trained within the MATLAB Simulink environment. The simulation results demonstrate that the UAV can navigate through the environment in most algorithm runs without colliding with surrounding obstacles.",
publisher = "ETRAN Society, The Society for Electronics, Telecommunications, Computing, Automatics and Nuclear engineering supported by IEEE",
journal = "Proceedings of the 10th International Conference on Electrical, Electronics and Computing Engineering (IcETRAN 2023)",
title = "Reinforcement Learning-based Collision Avoidance for UAV",
pages = "6-1",
number = "5496",
url = "https://hdl.handle.net/21.15107/rcub_machinery_6896"
}

Jevtić, Đ., Miljković, Z., Petrović, M.,& Jokić, A.. (2023). Reinforcement Learning-based Collision Avoidance for UAV. in Proceedings of the 10th International Conference on Electrical, Electronics and Computing Engineering (IcETRAN 2023)
ETRAN Society, The Society for Electronics, Telecommunications, Computing, Automatics and Nuclear engineering supported by IEEE.(5496), 1-6.
https://hdl.handle.net/21.15107/rcub_machinery_6896

Jevtić Đ, Miljković Z, Petrović M, Jokić A. Reinforcement Learning-based Collision Avoidance for UAV. in Proceedings of the 10th International Conference on Electrical, Electronics and Computing Engineering (IcETRAN 2023). 2023;(5496):1-6.
https://hdl.handle.net/21.15107/rcub_machinery_6896 .

Jevtić, Đorđe, Miljković, Zoran, Petrović, Milica, Jokić, Aleksandar, "Reinforcement Learning-based Collision Avoidance for UAV" in Proceedings of the 10th International Conference on Electrical, Electronics and Computing Engineering (IcETRAN 2023), no. 5496 (2023):1-6,
https://hdl.handle.net/21.15107/rcub_machinery_6896 .

TY  - CHAP
AU  - Jokić, Aleksandar
AU  - Petrović, Milica
AU  - Miljković, Zoran
PY  - 2023
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/6861
AB  - Integrated process planning and scheduling of manufacturing entities
represents one of the most impactful processes for optimizing production systems. The result of integrated process planning and scheduling is visually presented in the form of the Gantt chart that summarizes the timing and order of all
manufacturing operations. Moreover, a sequence of actions that needs to be performed by a transportation system to ensure the smooth execution of all manufacturing processes is also incorporated within the Gantt chart. Therefore, in this
paper, we develop a framework for mobile robot action planning based on highlevel action, e.g., transporting the part from one machine to another. The idea is
to automatically plan a sequence of robot-performable actions that, if performed
accurately, results in achieving a high-level goal. The framework is developed in
the standard Robot Operative System 2 (ROS2) middleware. The system, domain, and essential entities are modeled by using Planning Domain Definition
Language (PDDL). The mobile robot actions are modeled by Behavior trees
within the PlanSys2 framework.
PB  - Springer, Cham, Switzerland
T2  - New Trends in Medical and Service Robotics - MESROB 2023. Series: Mechanisms and Machine Science
T1  - The Framework for Mobile Robot Task Planning Based on the Optimal Manufacturing Schedule
EP  - 325
SP  - 317
VL  - 133
DO  - 10.1007/978-3-031-32446-8_34
ER  - 

@inbook{
author = "Jokić, Aleksandar and Petrović, Milica and Miljković, Zoran",
year = "2023",
abstract = "Integrated process planning and scheduling of manufacturing entities
represents one of the most impactful processes for optimizing production systems. The result of integrated process planning and scheduling is visually presented in the form of the Gantt chart that summarizes the timing and order of all
manufacturing operations. Moreover, a sequence of actions that needs to be performed by a transportation system to ensure the smooth execution of all manufacturing processes is also incorporated within the Gantt chart. Therefore, in this
paper, we develop a framework for mobile robot action planning based on highlevel action, e.g., transporting the part from one machine to another. The idea is
to automatically plan a sequence of robot-performable actions that, if performed
accurately, results in achieving a high-level goal. The framework is developed in
the standard Robot Operative System 2 (ROS2) middleware. The system, domain, and essential entities are modeled by using Planning Domain Definition
Language (PDDL). The mobile robot actions are modeled by Behavior trees
within the PlanSys2 framework.",
publisher = "Springer, Cham, Switzerland",
journal = "New Trends in Medical and Service Robotics - MESROB 2023. Series: Mechanisms and Machine Science",
booktitle = "The Framework for Mobile Robot Task Planning Based on the Optimal Manufacturing Schedule",
pages = "325-317",
volume = "133",
doi = "10.1007/978-3-031-32446-8_34"
}

Jokić, A., Petrović, M.,& Miljković, Z.. (2023). The Framework for Mobile Robot Task Planning Based on the Optimal Manufacturing Schedule. in New Trends in Medical and Service Robotics - MESROB 2023. Series: Mechanisms and Machine Science
Springer, Cham, Switzerland., 133, 317-325.
https://doi.org/10.1007/978-3-031-32446-8_34

Jokić A, Petrović M, Miljković Z. The Framework for Mobile Robot Task Planning Based on the Optimal Manufacturing Schedule. in New Trends in Medical and Service Robotics - MESROB 2023. Series: Mechanisms and Machine Science. 2023;133:317-325.
doi:10.1007/978-3-031-32446-8_34 .

Jokić, Aleksandar, Petrović, Milica, Miljković, Zoran, "The Framework for Mobile Robot Task Planning Based on the Optimal Manufacturing Schedule" in New Trends in Medical and Service Robotics - MESROB 2023. Series: Mechanisms and Machine Science, 133 (2023):317-325,
https://doi.org/10.1007/978-3-031-32446-8_34 . .

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  - 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  - CHAP
AU  - Petrović, Milica
AU  - Jokić, Aleksandar
AU  - Kulesza, Z.
AU  - Miljković, Zoran
PY  - 2021
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3661
AB  - In the last two decades, the development of state -of-the-art artificial intelligence (AI) models has significantly increased the utilization of commercial and task-specific robots in the service domain. The additional level of intelligence introduced by AI models has enabled service robots to coexist within different human environments and collaborate with end-users. One of the most promising AI techniques, Deep Learning (DL), can provide service robots with a wide range of abilities, such as detecting human pose and emotions, understanding natural languages, as well as scene understanding. Achieved abilities can enable mobile service robots to execute specific tasks in real and stochastic environments. Having that in mind, in this chapter, we provide an in-depth analysis of the tasks that are best-suited for DL within the service robots domain. Moreover, the study of the state-of-the-art DL models for object detection, semantic segmentation, and human pose estimation is carried out. In the end, the authors presented a thorough examination of the training process and analysis of the results for one of the most promising convolutional neural network models (DeepLabv3+) used for semantic segmentation.
PB  - Nova Science Publishers, Inc.
T2  - Service Robots: Advances in Research and Applications
T1  - Deep learning of mobile service robots
EP  - 97
SP  - 77
UR  - https://hdl.handle.net/21.15107/rcub_machinery_3661
ER  - 

@inbook{
author = "Petrović, Milica and Jokić, Aleksandar and Kulesza, Z. and Miljković, Zoran",
year = "2021",
abstract = "In the last two decades, the development of state -of-the-art artificial intelligence (AI) models has significantly increased the utilization of commercial and task-specific robots in the service domain. The additional level of intelligence introduced by AI models has enabled service robots to coexist within different human environments and collaborate with end-users. One of the most promising AI techniques, Deep Learning (DL), can provide service robots with a wide range of abilities, such as detecting human pose and emotions, understanding natural languages, as well as scene understanding. Achieved abilities can enable mobile service robots to execute specific tasks in real and stochastic environments. Having that in mind, in this chapter, we provide an in-depth analysis of the tasks that are best-suited for DL within the service robots domain. Moreover, the study of the state-of-the-art DL models for object detection, semantic segmentation, and human pose estimation is carried out. In the end, the authors presented a thorough examination of the training process and analysis of the results for one of the most promising convolutional neural network models (DeepLabv3+) used for semantic segmentation.",
publisher = "Nova Science Publishers, Inc.",
journal = "Service Robots: Advances in Research and Applications",
booktitle = "Deep learning of mobile service robots",
pages = "97-77",
url = "https://hdl.handle.net/21.15107/rcub_machinery_3661"
}

Petrović, M., Jokić, A., Kulesza, Z.,& Miljković, Z.. (2021). Deep learning of mobile service robots. in Service Robots: Advances in Research and Applications
Nova Science Publishers, Inc.., 77-97.
https://hdl.handle.net/21.15107/rcub_machinery_3661

Petrović M, Jokić A, Kulesza Z, Miljković Z. Deep learning of mobile service robots. in Service Robots: Advances in Research and Applications. 2021;:77-97.
https://hdl.handle.net/21.15107/rcub_machinery_3661 .

Petrović, Milica, Jokić, Aleksandar, Kulesza, Z., Miljković, Zoran, "Deep learning of mobile service robots" in Service Robots: Advances in Research and Applications (2021):77-97,
https://hdl.handle.net/21.15107/rcub_machinery_3661 .

TY  - CHAP
AU  - Miljković, Zoran
AU  - Jokić, Aleksandar
AU  - Petrović, Milica
PY  - 2021
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3646
AB  - Since the emergence of deep learning as a dominant technique for numerous tasks in the computer vision domain, the robotics community has strived to utilize its potential. Deep learning represents a framework capable of learning the most complex models necessary to carry out various robotic tasks. We propose to integrate deep learning and one of the fundamental robotic algorithms—visual servoing. Fully convolutional neural networks are used for semantic segmentation, which represents the process of labeling every pixel within the image. The obtained information from labeled (categorical) images can be crucial for mobile robot control in dynamic environments. To adequately utilize semantic segmentation for mobile robot control, the segmented images acquired at the desired and the current pose need to be registered (aligned). Since the accuracy of visual servoing depends on the accuracy of the image registration process, we propose to increase the accuracy of mobile robot positioning by analyzing three different optimization algorithms devoted to the registration of categorical images. The standard gradient descent algorithm is compared to the OnePlusOneEvolutionary algorithm, and simulated annealing. Moreover, different cost functions such as Mattes mutual information, global accuracy, and mean intersection over union are also investigated. All the algorithms are tested on our own wheeled mobile robot RAICO (Robot with Artificial Intelligence based COgnition) developed within the Laboratory for robotics and artificial intelligence. The results indicate that the algorithm with a larger exploration to exploitation ratio provides better results. Moreover, the cost function with the steepest convex domain is more advantageous.
PB  - Springer Science and Business Media Deutschland GmbH
T2  - Studies in Computational Intelligence
T1  - Image Registration Algorithm for Deep Learning-Based Stereo Visual Control of Mobile Robots
EP  - 479
SP  - 447
VL  - 984
DO  - 10.1007/978-3-030-77939-9_13
ER  - 

@inbook{
author = "Miljković, Zoran and Jokić, Aleksandar and Petrović, Milica",
year = "2021",
abstract = "Since the emergence of deep learning as a dominant technique for numerous tasks in the computer vision domain, the robotics community has strived to utilize its potential. Deep learning represents a framework capable of learning the most complex models necessary to carry out various robotic tasks. We propose to integrate deep learning and one of the fundamental robotic algorithms—visual servoing. Fully convolutional neural networks are used for semantic segmentation, which represents the process of labeling every pixel within the image. The obtained information from labeled (categorical) images can be crucial for mobile robot control in dynamic environments. To adequately utilize semantic segmentation for mobile robot control, the segmented images acquired at the desired and the current pose need to be registered (aligned). Since the accuracy of visual servoing depends on the accuracy of the image registration process, we propose to increase the accuracy of mobile robot positioning by analyzing three different optimization algorithms devoted to the registration of categorical images. The standard gradient descent algorithm is compared to the OnePlusOneEvolutionary algorithm, and simulated annealing. Moreover, different cost functions such as Mattes mutual information, global accuracy, and mean intersection over union are also investigated. All the algorithms are tested on our own wheeled mobile robot RAICO (Robot with Artificial Intelligence based COgnition) developed within the Laboratory for robotics and artificial intelligence. The results indicate that the algorithm with a larger exploration to exploitation ratio provides better results. Moreover, the cost function with the steepest convex domain is more advantageous.",
publisher = "Springer Science and Business Media Deutschland GmbH",
journal = "Studies in Computational Intelligence",
booktitle = "Image Registration Algorithm for Deep Learning-Based Stereo Visual Control of Mobile Robots",
pages = "479-447",
volume = "984",
doi = "10.1007/978-3-030-77939-9_13"
}

Miljković, Z., Jokić, A.,& Petrović, M.. (2021). Image Registration Algorithm for Deep Learning-Based Stereo Visual Control of Mobile Robots. in Studies in Computational Intelligence
Springer Science and Business Media Deutschland GmbH., 984, 447-479.
https://doi.org/10.1007/978-3-030-77939-9_13

Miljković Z, Jokić A, Petrović M. Image Registration Algorithm for Deep Learning-Based Stereo Visual Control of Mobile Robots. in Studies in Computational Intelligence. 2021;984:447-479.
doi:10.1007/978-3-030-77939-9_13 .

Miljković, Zoran, Jokić, Aleksandar, Petrović, Milica, "Image Registration Algorithm for Deep Learning-Based Stereo Visual Control of Mobile Robots" in Studies in Computational Intelligence, 984 (2021):447-479,
https://doi.org/10.1007/978-3-030-77939-9_13 . .

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

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

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

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

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

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

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

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

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

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

TY  - CONF
AU  - Jokić, Aleksandar
AU  - Petrović, Milica
AU  - Kulesza, Z.
AU  - Miljković, Zoran
PY  - 2021
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3649
AB  - The recent development of faster and more accurate deep learning models has enabled researchers to utilize the potential of deep learning in robotics. Convolutional neural networks used for the process of semantic segmentation are being applied to improve the traditional robotic tasks by adding an additional level of intelligence, through the execution of context-aware tasks. Having that in mind, visual servoing can now be performed in a completely new manner, by exploiting only semantic and geometric knowledge about the environment. To carry out visual servoing, the mathematical model of the error between the images generated at the current and the desired mobile robot pose (i.e. position and orientation) in the image space needs to be adequately defined. In this paper, we propose the novel mathematical model for the weighted fitness function evaluation, which is utilized for the image registration process within the visual servoing framework. By weighting the classes by their importance in the desired image, the convergence domain of the initial error in the visual servoing process can be greatly extended. The experimental evaluation is carried out on the mobile robot RAICO (Robot with Artificial Intelligence based COgnition), where it is shown that weighted fitness function enables more robust intelligent visual servoing systems with a lower possibility of failure, easier real-world implementation, and feasible object driven navigation.
PB  - Springer Science and Business Media Deutschland GmbH
C3  - Lecture Notes in Networks and Systems
T1  - Visual Deep Learning-Based Mobile Robot Control: A Novel Weighted Fitness Function-Based Image Registration Model
EP  - 752
SP  - 744
VL  - 233
DO  - 10.1007/978-3-030-75275-0_82
ER  - 

@conference{
author = "Jokić, Aleksandar and Petrović, Milica and Kulesza, Z. and Miljković, Zoran",
year = "2021",
abstract = "The recent development of faster and more accurate deep learning models has enabled researchers to utilize the potential of deep learning in robotics. Convolutional neural networks used for the process of semantic segmentation are being applied to improve the traditional robotic tasks by adding an additional level of intelligence, through the execution of context-aware tasks. Having that in mind, visual servoing can now be performed in a completely new manner, by exploiting only semantic and geometric knowledge about the environment. To carry out visual servoing, the mathematical model of the error between the images generated at the current and the desired mobile robot pose (i.e. position and orientation) in the image space needs to be adequately defined. In this paper, we propose the novel mathematical model for the weighted fitness function evaluation, which is utilized for the image registration process within the visual servoing framework. By weighting the classes by their importance in the desired image, the convergence domain of the initial error in the visual servoing process can be greatly extended. The experimental evaluation is carried out on the mobile robot RAICO (Robot with Artificial Intelligence based COgnition), where it is shown that weighted fitness function enables more robust intelligent visual servoing systems with a lower possibility of failure, easier real-world implementation, and feasible object driven navigation.",
publisher = "Springer Science and Business Media Deutschland GmbH",
journal = "Lecture Notes in Networks and Systems",
title = "Visual Deep Learning-Based Mobile Robot Control: A Novel Weighted Fitness Function-Based Image Registration Model",
pages = "752-744",
volume = "233",
doi = "10.1007/978-3-030-75275-0_82"
}

Jokić, A., Petrović, M., Kulesza, Z.,& Miljković, Z.. (2021). Visual Deep Learning-Based Mobile Robot Control: A Novel Weighted Fitness Function-Based Image Registration Model. in Lecture Notes in Networks and Systems
Springer Science and Business Media Deutschland GmbH., 233, 744-752.
https://doi.org/10.1007/978-3-030-75275-0_82

Jokić A, Petrović M, Kulesza Z, Miljković Z. Visual Deep Learning-Based Mobile Robot Control: A Novel Weighted Fitness Function-Based Image Registration Model. in Lecture Notes in Networks and Systems. 2021;233:744-752.
doi:10.1007/978-3-030-75275-0_82 .

Jokić, Aleksandar, Petrović, Milica, Kulesza, Z., Miljković, Zoran, "Visual Deep Learning-Based Mobile Robot Control: A Novel Weighted Fitness Function-Based Image Registration Model" in Lecture Notes in Networks and Systems, 233 (2021):744-752,
https://doi.org/10.1007/978-3-030-75275-0_82 . .

TY  - CONF
AU  - Đokić, L.
AU  - Jokić, Aleksandar
AU  - Petrović, Milica
AU  - Miljković, Zoran
PY  - 2021
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3640
AB  - Modern intelligent manufacturing systems are dynamic environments with the ability to respond and adapt to various internal and external changes that can occur during the manufacturing process. By default, they imply efficient, reliable, and flexible material handling and transportation system, which can be effectively realized by using intelligent mobile robots. In order to achieve locomotion of an intelligent mobile robot that will minimize the usage of space within the manufacturing environment, we propose the development of a new holonomic mobile robot DOMINO (Deep learning-based Omnidirectional Mobile robot with INtelligentcOntrol). 3D model of holonomic mobile robot prototype is developed in CAD software package SolidWorks, and designed parts are produced with additive manufacturing technology. Single board computer Raspberry Pi 4 and microcontroller board Arduino Mega 2560 are used for motion control of the holonomic mobile robot, while control actions are determined by the defined kinematic model of the omnidirectional wheeled mobile robot. The experimental verification shows that the holonomic mobile robot is capable of following a predetermined path while successfully avoiding obstacles within a laboratory model of a manufacturing environment.
PB  - Springer Science and Business Media Deutschland GmbH
C3  - Lecture Notes in Networks and Systems
T1  - Design and Development of a Holonomic Mobile Robot for Material Handling and Transportation Tasks
EP  - 716
SP  - 709
VL  - 233
DO  - 10.1007/978-3-030-75275-0_78
ER  - 

@conference{
author = "Đokić, L. and Jokić, Aleksandar and Petrović, Milica and Miljković, Zoran",
year = "2021",
abstract = "Modern intelligent manufacturing systems are dynamic environments with the ability to respond and adapt to various internal and external changes that can occur during the manufacturing process. By default, they imply efficient, reliable, and flexible material handling and transportation system, which can be effectively realized by using intelligent mobile robots. In order to achieve locomotion of an intelligent mobile robot that will minimize the usage of space within the manufacturing environment, we propose the development of a new holonomic mobile robot DOMINO (Deep learning-based Omnidirectional Mobile robot with INtelligentcOntrol). 3D model of holonomic mobile robot prototype is developed in CAD software package SolidWorks, and designed parts are produced with additive manufacturing technology. Single board computer Raspberry Pi 4 and microcontroller board Arduino Mega 2560 are used for motion control of the holonomic mobile robot, while control actions are determined by the defined kinematic model of the omnidirectional wheeled mobile robot. The experimental verification shows that the holonomic mobile robot is capable of following a predetermined path while successfully avoiding obstacles within a laboratory model of a manufacturing environment.",
publisher = "Springer Science and Business Media Deutschland GmbH",
journal = "Lecture Notes in Networks and Systems",
title = "Design and Development of a Holonomic Mobile Robot for Material Handling and Transportation Tasks",
pages = "716-709",
volume = "233",
doi = "10.1007/978-3-030-75275-0_78"
}

Đokić, L., Jokić, A., Petrović, M.,& Miljković, Z.. (2021). Design and Development of a Holonomic Mobile Robot for Material Handling and Transportation Tasks. in Lecture Notes in Networks and Systems
Springer Science and Business Media Deutschland GmbH., 233, 709-716.
https://doi.org/10.1007/978-3-030-75275-0_78

Đokić L, Jokić A, Petrović M, Miljković Z. Design and Development of a Holonomic Mobile Robot for Material Handling and Transportation Tasks. in Lecture Notes in Networks and Systems. 2021;233:709-716.
doi:10.1007/978-3-030-75275-0_78 .

Đokić, L., Jokić, Aleksandar, Petrović, Milica, Miljković, Zoran, "Design and Development of a Holonomic Mobile Robot for Material Handling and Transportation Tasks" in Lecture Notes in Networks and Systems, 233 (2021):709-716,
https://doi.org/10.1007/978-3-030-75275-0_78 . .

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

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

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

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

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

TY  - CONF
AU  - Petrović, Milica
AU  - Mystkowski, A.
AU  - Jokić, Aleksandar
AU  - Dokić, L.
AU  - Miljković, Zoran
PY  - 2020
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3446
AB  - For the implementation of stereo image-based visual servoing algorithm in the eye-in-hand robotics applications, one of the main concerns is the accurate point feature detection and matching algorithm. Since the visual servoing is carried out in the textureless environment, the feature detection process is even more challenging. To fulfill the requirement of a robust and reliable point feature detection process, in this paper we present the novel deep learning-based algorithm. The approach based on convolutional neural networks and algorithm for detection of manufacturing entities is proposed and detected regions of interest are utilized for the improvement of the point feature detection algorithm. The proposed algorithm is experimentally evaluated in real-world settings by using wheeled nonholonomic mobile robot RAICO equipped with stereo vision system. The experimental results show the improvement of 58% in the accuracy of matched point features in the images obtained during the visual servoing process. Moreover, with the implementation of the proposed deep learning-based approach, the number of successful experimental runs has increased by 80%.
PB  - Institute of Electrical and Electronics Engineers Inc.
C3  - 15th International Conference Mechatronic Systems and Materials, MSM 2020
T1  - Deep learning-based algorithm for mobile robot control in textureless environment
DO  - 10.1109/MSM49833.2020.9201666
ER  - 

@conference{
author = "Petrović, Milica and Mystkowski, A. and Jokić, Aleksandar and Dokić, L. and Miljković, Zoran",
year = "2020",
abstract = "For the implementation of stereo image-based visual servoing algorithm in the eye-in-hand robotics applications, one of the main concerns is the accurate point feature detection and matching algorithm. Since the visual servoing is carried out in the textureless environment, the feature detection process is even more challenging. To fulfill the requirement of a robust and reliable point feature detection process, in this paper we present the novel deep learning-based algorithm. The approach based on convolutional neural networks and algorithm for detection of manufacturing entities is proposed and detected regions of interest are utilized for the improvement of the point feature detection algorithm. The proposed algorithm is experimentally evaluated in real-world settings by using wheeled nonholonomic mobile robot RAICO equipped with stereo vision system. The experimental results show the improvement of 58% in the accuracy of matched point features in the images obtained during the visual servoing process. Moreover, with the implementation of the proposed deep learning-based approach, the number of successful experimental runs has increased by 80%.",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
journal = "15th International Conference Mechatronic Systems and Materials, MSM 2020",
title = "Deep learning-based algorithm for mobile robot control in textureless environment",
doi = "10.1109/MSM49833.2020.9201666"
}

Petrović, M., Mystkowski, A., Jokić, A., Dokić, L.,& Miljković, Z.. (2020). Deep learning-based algorithm for mobile robot control in textureless environment. in 15th International Conference Mechatronic Systems and Materials, MSM 2020
Institute of Electrical and Electronics Engineers Inc...
https://doi.org/10.1109/MSM49833.2020.9201666

Petrović M, Mystkowski A, Jokić A, Dokić L, Miljković Z. Deep learning-based algorithm for mobile robot control in textureless environment. in 15th International Conference Mechatronic Systems and Materials, MSM 2020. 2020;.
doi:10.1109/MSM49833.2020.9201666 .

Petrović, Milica, Mystkowski, A., Jokić, Aleksandar, Dokić, L., Miljković, Zoran, "Deep learning-based algorithm for mobile robot control in textureless environment" in 15th International Conference Mechatronic Systems and Materials, MSM 2020 (2020),
https://doi.org/10.1109/MSM49833.2020.9201666 . .

TY  - GEN
AU  - Jokić, Aleksandar
AU  - Petrović, Milica
AU  - Miljković, Zoran
AU  - Babić, Bojan
PY  - 2020
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4296
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 интелигентног управљачког система транспортних средстава (мобилних робота) у технолошком окружењу Индустрије 4.0. Сходно томе, методом се решава проблем перцепције мобилних робота на бази информација добијених од стерео визуелног система, пројектованог коришћењем две паралелно постављене индустријске камере BASLER daA1600-60uc и рачунарске платформе Nvidia Jetson Nano. Систем перцепције је базиран на интеграцији стерео визуелног система и метода дубоког машинског учења (енгл. Deep Learning - DL), односно конволуционих неуронских мрежа (енгл. Convolutional Neural Networks - CNN). Поменутом интеграцијом се остварује: (i) висока флексибилност хардверско-софтверске структуре, (ii) висока тачност препознавања објеката у окружењу у коме се мобилни робот налази, (iii) могућност детекције објеката у реалном времену. Предложени систем перцепције је експериментално верификован на интелигентном мобилном роботу RAICO (Robot with Artificial Intelligence based COgnition) развијеном у оквиру Лабораторије за индустријску роботику и вештачку интелигенцију Машинског факултета у Београду.
T2  - Техничко решење је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер
T1  - Стерео визуелни систем перцепције мобилног робота базиран на дубоком машинском учењу : Техничко решење (М85)
T1  - Mobile robot stereo visual perception system based on deep machine learning
EP  - 20
SP  - 1
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4296
ER  - 

@misc{
author = "Jokić, Aleksandar and Petrović, Milica and Miljković, Zoran and Babić, Bojan",
year = "2020",
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 интелигентног управљачког система транспортних средстава (мобилних робота) у технолошком окружењу Индустрије 4.0. Сходно томе, методом се решава проблем перцепције мобилних робота на бази информација добијених од стерео визуелног система, пројектованог коришћењем две паралелно постављене индустријске камере BASLER daA1600-60uc и рачунарске платформе Nvidia Jetson Nano. Систем перцепције је базиран на интеграцији стерео визуелног система и метода дубоког машинског учења (енгл. Deep Learning - DL), односно конволуционих неуронских мрежа (енгл. Convolutional Neural Networks - CNN). Поменутом интеграцијом се остварује: (i) висока флексибилност хардверско-софтверске структуре, (ii) висока тачност препознавања објеката у окружењу у коме се мобилни робот налази, (iii) могућност детекције објеката у реалном времену. Предложени систем перцепције је експериментално верификован на интелигентном мобилном роботу RAICO (Robot with Artificial Intelligence based COgnition) развијеном у оквиру Лабораторије за индустријску роботику и вештачку интелигенцију Машинског факултета у Београду.",
journal = "Техничко решење је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер",
title = "Стерео визуелни систем перцепције мобилног робота базиран на дубоком машинском учењу : Техничко решење (М85), Mobile robot stereo visual perception system based on deep machine learning",
pages = "20-1",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4296"
}

Jokić, A., Petrović, M., Miljković, Z.,& Babić, B.. (2020). Стерео визуелни систем перцепције мобилног робота базиран на дубоком машинском учењу : Техничко решење (М85). in Техничко решење је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер, 1-20.
https://hdl.handle.net/21.15107/rcub_machinery_4296

Jokić A, Petrović M, Miljković Z, Babić B. Стерео визуелни систем перцепције мобилног робота базиран на дубоком машинском учењу : Техничко решење (М85). in Техничко решење је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер. 2020;:1-20.
https://hdl.handle.net/21.15107/rcub_machinery_4296 .

Jokić, Aleksandar, Petrović, Milica, Miljković, Zoran, Babić, Bojan, "Стерео визуелни систем перцепције мобилног робота базиран на дубоком машинском учењу : Техничко решење (М85)" in Техничко решење је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер (2020):1-20,
https://hdl.handle.net/21.15107/rcub_machinery_4296 .

TY  - DATA
AU  - Jokić, Aleksandar
AU  - Petrović, Milica
AU  - Miljković, Zoran
PY  - 2020
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4294
AB  - This dataset includes images and labels used for semantic segmentation of the laboratory model of the manufacturing environment, at the University of Belgrade - Faculty of Mechanical Engineering. The dataset is gathered by using mobile robot RAICO (Robot with Artificial Intelligence based COgnition) and its stereo visual system made from two Basler acA1920-25uc cameras with Fujinon lens DF6HA-1B. The dataset includes close to 430 images with a resolution of 640x360. Images are acquired by both cameras at different mobile robot poses in the laboratory model of a manufacturing environment. Five classes are introduced in the dataset, machines 1 to 4, and a background class. The exact names of the classes are:

classNames = ["Machine_1", "Machine_2", "Machine_3", "Machine_4", "Background"];

while the labels of the classes (RGB values of label images) are:

labelIDs = [ ...
    000 000 255; ... % "Machine 1"
    000 255 255; ... % "Machine 2"
    255 255 000; ... % "Machine 3"
    255 000 000; ... % "Machine 4"
    255 255 255; ...   % "Background"
    ];

Image and label pairs are entitled 1 to 430, and e.g. label 5 corresponds to images 5.
T2  - Zenodo
T1  - Dataset for semantic segmentation of the laboratory model of manufacturing environment (Version 0.1.0)
DO  - 10.5281/zenodo.4138944
ER  - 

@misc{
author = "Jokić, Aleksandar and Petrović, Milica and Miljković, Zoran",
year = "2020",
abstract = "This dataset includes images and labels used for semantic segmentation of the laboratory model of the manufacturing environment, at the University of Belgrade - Faculty of Mechanical Engineering. The dataset is gathered by using mobile robot RAICO (Robot with Artificial Intelligence based COgnition) and its stereo visual system made from two Basler acA1920-25uc cameras with Fujinon lens DF6HA-1B. The dataset includes close to 430 images with a resolution of 640x360. Images are acquired by both cameras at different mobile robot poses in the laboratory model of a manufacturing environment. Five classes are introduced in the dataset, machines 1 to 4, and a background class. The exact names of the classes are:

classNames = ["Machine_1", "Machine_2", "Machine_3", "Machine_4", "Background"];

while the labels of the classes (RGB values of label images) are:

labelIDs = [ ...
    000 000 255; ... % "Machine 1"
    000 255 255; ... % "Machine 2"
    255 255 000; ... % "Machine 3"
    255 000 000; ... % "Machine 4"
    255 255 255; ...   % "Background"
    ];

Image and label pairs are entitled 1 to 430, and e.g. label 5 corresponds to images 5.",
journal = "Zenodo",
title = "Dataset for semantic segmentation of the laboratory model of manufacturing environment (Version 0.1.0)",
doi = "10.5281/zenodo.4138944"
}

Jokić, A., Petrović, M.,& Miljković, Z.. (2020). Dataset for semantic segmentation of the laboratory model of manufacturing environment (Version 0.1.0). in Zenodo.
https://doi.org/10.5281/zenodo.4138944

Jokić A, Petrović M, Miljković Z. Dataset for semantic segmentation of the laboratory model of manufacturing environment (Version 0.1.0). in Zenodo. 2020;.
doi:10.5281/zenodo.4138944 .

Jokić, Aleksandar, Petrović, Milica, Miljković, Zoran, "Dataset for semantic segmentation of the laboratory model of manufacturing environment (Version 0.1.0)" in Zenodo (2020),
https://doi.org/10.5281/zenodo.4138944 . .

TY  - JOUR
AU  - Petrović, Milica
AU  - Miljković, Zoran
AU  - Jokić, Aleksandar
PY  - 2019
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3015
AB  - One of the fundamental requirements for creating an intelligent manufacturing environment is to develop a reliable, efficient and optimally scheduled material transport system. Besides traditional material transport solutions based on conveyor belts, industrial trucks, or automated guided vehicles, nowadays intelligent mobile robots are becoming widely used to satisfy this requirement. In this paper, the authors analyze a single mobile robot scheduling problem in order to find an optimal way to transport raw materials, goods, and parts within an intelligent manufacturing system. The proposed methodology is based on biologically inspired Whale Optimization Algorithm (WOA) and is aimed to find the optimal solution of the nondeterministic polynomial-hard (NP-hard) scheduling problem. The authors propose a novel mathematical model for the problem and give a mathematical formulation for minimization of seven fitness functions (makespan, robot finishing time, transport time, balanced level of robot utilization, robot waiting time, job waiting time, as well as total robot and job waiting time). This newly developed methodology is extensively experimentally tested on 26 benchmark problems through three experimental studies and compared to five meta-heuristic algorithms including genetic algorithm (GA), simulated annealing (SA), generic and chaotic Particle Swarm Optimization algorithm (PSO and cPSO), and hybrid GA-SA algorithm. Furthermore, the data are analyzed by using the Friedman statistical test to prove that results are statistically significant. Finally, generated scheduling plans are tested by Khepera II mobile robot within a laboratory model of the manufacturing environment. The experimental results show that the proposed methodology provides very competitive results compared to the state-of-art optimization algorithms.
PB  - Elsevier, Amsterdam
T2  - Applied Soft Computing
T1  - A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm
SP  - 105520
VL  - 81
DO  - 10.1016/j.asoc.2019.105520
ER  - 

@article{
author = "Petrović, Milica and Miljković, Zoran and Jokić, Aleksandar",
year = "2019",
abstract = "One of the fundamental requirements for creating an intelligent manufacturing environment is to develop a reliable, efficient and optimally scheduled material transport system. Besides traditional material transport solutions based on conveyor belts, industrial trucks, or automated guided vehicles, nowadays intelligent mobile robots are becoming widely used to satisfy this requirement. In this paper, the authors analyze a single mobile robot scheduling problem in order to find an optimal way to transport raw materials, goods, and parts within an intelligent manufacturing system. The proposed methodology is based on biologically inspired Whale Optimization Algorithm (WOA) and is aimed to find the optimal solution of the nondeterministic polynomial-hard (NP-hard) scheduling problem. The authors propose a novel mathematical model for the problem and give a mathematical formulation for minimization of seven fitness functions (makespan, robot finishing time, transport time, balanced level of robot utilization, robot waiting time, job waiting time, as well as total robot and job waiting time). This newly developed methodology is extensively experimentally tested on 26 benchmark problems through three experimental studies and compared to five meta-heuristic algorithms including genetic algorithm (GA), simulated annealing (SA), generic and chaotic Particle Swarm Optimization algorithm (PSO and cPSO), and hybrid GA-SA algorithm. Furthermore, the data are analyzed by using the Friedman statistical test to prove that results are statistically significant. Finally, generated scheduling plans are tested by Khepera II mobile robot within a laboratory model of the manufacturing environment. The experimental results show that the proposed methodology provides very competitive results compared to the state-of-art optimization algorithms.",
publisher = "Elsevier, Amsterdam",
journal = "Applied Soft Computing",
title = "A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm",
pages = "105520",
volume = "81",
doi = "10.1016/j.asoc.2019.105520"
}

Petrović, M., Miljković, Z.,& Jokić, A.. (2019). A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm. in Applied Soft Computing
Elsevier, Amsterdam., 81, 105520.
https://doi.org/10.1016/j.asoc.2019.105520

Petrović M, Miljković Z, Jokić A. A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm. in Applied Soft Computing. 2019;81:105520.
doi:10.1016/j.asoc.2019.105520 .

Petrović, Milica, Miljković, Zoran, Jokić, Aleksandar, "A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm" in Applied Soft Computing, 81 (2019):105520,
https://doi.org/10.1016/j.asoc.2019.105520 . .

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

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

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

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

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