TY  - CONF
AU  - Mitić, Marko
AU  - Vuković, Najdan
AU  - Petrović, Milica
AU  - Petronijević, Jelena
AU  - Diryag, Ali
AU  - Miljković, Zoran
PY  - 2015
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4469
AB  - This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions.
PB  - Society for Information Systems and Computer Networks
C3  - Proceedings of the 5th International Conference on Information Society and Technology (ICIST 2015), Kopaonik 8-11. March 2015
T1  - Bioinspired metaheuristic algorithms for global optimization
EP  - 42
SP  - 38
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4469
ER  - 

@conference{
author = "Mitić, Marko and Vuković, Najdan and Petrović, Milica and Petronijević, Jelena and Diryag, Ali and Miljković, Zoran",
year = "2015",
abstract = "This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions.",
publisher = "Society for Information Systems and Computer Networks",
journal = "Proceedings of the 5th International Conference on Information Society and Technology (ICIST 2015), Kopaonik 8-11. March 2015",
title = "Bioinspired metaheuristic algorithms for global optimization",
pages = "42-38",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4469"
}

Mitić, M., Vuković, N., Petrović, M., Petronijević, J., Diryag, A.,& Miljković, Z.. (2015). Bioinspired metaheuristic algorithms for global optimization. in Proceedings of the 5th International Conference on Information Society and Technology (ICIST 2015), Kopaonik 8-11. March 2015
Society for Information Systems and Computer Networks., 38-42.
https://hdl.handle.net/21.15107/rcub_machinery_4469

Mitić M, Vuković N, Petrović M, Petronijević J, Diryag A, Miljković Z. Bioinspired metaheuristic algorithms for global optimization. in Proceedings of the 5th International Conference on Information Society and Technology (ICIST 2015), Kopaonik 8-11. March 2015. 2015;:38-42.
https://hdl.handle.net/21.15107/rcub_machinery_4469 .

Mitić, Marko, Vuković, Najdan, Petrović, Milica, Petronijević, Jelena, Diryag, Ali, Miljković, Zoran, "Bioinspired metaheuristic algorithms for global optimization" in Proceedings of the 5th International Conference on Information Society and Technology (ICIST 2015), Kopaonik 8-11. March 2015 (2015):38-42,
https://hdl.handle.net/21.15107/rcub_machinery_4469 .

TY  - CONF
AU  - Mitić, Marko
AU  - Vuković, Najdan
AU  - Diryag, Ali
AU  - Miljković, Zoran
PY  - 2014
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4632
AB  - The hybrid mobile robot control algorithm consists of two independent control loops. By developing two control phases, the transportation task is separated into two parts: movement from the initial position to a position at a great distance from the machine tool (global control) and movement from this position to the machine tool/intermediate point (local control). The original control system based on epipolar geometry as well as on learning of motion trajectories and visual commands was implemented on the Khepera II nonholonomic mobile robot (with additional equipment: KheGrip gripper and Prestigio PWC 2 WEB camera) by using metaheuristic technique in a laboratory model of the technological environment.
PB  - The Aristotle University of Thessaloniki
C3  - Proceedings of the 5th International Conference on Manufacturing Engineering (ICMEN 2014)
T1  - Learning Motion Trajectories and Visual Commands of a Nonholonomic Mobile Robot Using Metaheuristic Technique
EP  - 98
SP  - 89
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4632
ER  - 

@conference{
author = "Mitić, Marko and Vuković, Najdan and Diryag, Ali and Miljković, Zoran",
year = "2014",
abstract = "The hybrid mobile robot control algorithm consists of two independent control loops. By developing two control phases, the transportation task is separated into two parts: movement from the initial position to a position at a great distance from the machine tool (global control) and movement from this position to the machine tool/intermediate point (local control). The original control system based on epipolar geometry as well as on learning of motion trajectories and visual commands was implemented on the Khepera II nonholonomic mobile robot (with additional equipment: KheGrip gripper and Prestigio PWC 2 WEB camera) by using metaheuristic technique in a laboratory model of the technological environment.",
publisher = "The Aristotle University of Thessaloniki",
journal = "Proceedings of the 5th International Conference on Manufacturing Engineering (ICMEN 2014)",
title = "Learning Motion Trajectories and Visual Commands of a Nonholonomic Mobile Robot Using Metaheuristic Technique",
pages = "98-89",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4632"
}

Mitić, M., Vuković, N., Diryag, A.,& Miljković, Z.. (2014). Learning Motion Trajectories and Visual Commands of a Nonholonomic Mobile Robot Using Metaheuristic Technique. in Proceedings of the 5th International Conference on Manufacturing Engineering (ICMEN 2014)
The Aristotle University of Thessaloniki., 89-98.
https://hdl.handle.net/21.15107/rcub_machinery_4632

Mitić M, Vuković N, Diryag A, Miljković Z. Learning Motion Trajectories and Visual Commands of a Nonholonomic Mobile Robot Using Metaheuristic Technique. in Proceedings of the 5th International Conference on Manufacturing Engineering (ICMEN 2014). 2014;:89-98.
https://hdl.handle.net/21.15107/rcub_machinery_4632 .

Mitić, Marko, Vuković, Najdan, Diryag, Ali, Miljković, Zoran, "Learning Motion Trajectories and Visual Commands of a Nonholonomic Mobile Robot Using Metaheuristic Technique" in Proceedings of the 5th International Conference on Manufacturing Engineering (ICMEN 2014) (2014):89-98,
https://hdl.handle.net/21.15107/rcub_machinery_4632 .

TY  - JOUR
AU  - Diryag, Ali
AU  - Mitić, Marko
AU  - Miljković, Zoran
PY  - 2014
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/1875
AB  - It is known that the supervision and learning of robotic executions is not a trivial problem. Nowadays, robots must be able to tolerate and predict internal failures in order to successfully continue performing their tasks. This study presents a novel approach for prediction of robot execution failures based on neural networks. Real data consisting of robot forces and torques recorded immediately after the system failure are used for the neural network training. The multilayer feedforward neural networks are employed in order to find optimal solution for the failure prediction problem. In total, 7 learning algorithms and 24 neural architectures are implemented in two environments - Matlab and specially designed software titled BPnet. The results show that the neural networks can successfully be applied for the problem in hand with prediction rate of 95.4545%, despite having the erroneous or otherwise incomplete sensor measurements invoked in the dataset. Additionally, the real-world experiments are conducted on a mobile robot for obstacle detection and trajectory tracking problems in order to prove the robustness of the proposed prediction approach. In over 96% for the detection problem and 99% for the tracking experiments, neural network successfully predicted the failed information, which evidences the usefulness and the applicability of the developed intelligent method.
PB  - Sage Publications Ltd, London
T2  - Proceedings of The Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Scie
T1  - Neural networks for prediction of robot failures
EP  - 1458
IS  - 8
SP  - 1444
VL  - 228
DO  - 10.1177/0954406213507704
ER  - 

@article{
author = "Diryag, Ali and Mitić, Marko and Miljković, Zoran",
year = "2014",
abstract = "It is known that the supervision and learning of robotic executions is not a trivial problem. Nowadays, robots must be able to tolerate and predict internal failures in order to successfully continue performing their tasks. This study presents a novel approach for prediction of robot execution failures based on neural networks. Real data consisting of robot forces and torques recorded immediately after the system failure are used for the neural network training. The multilayer feedforward neural networks are employed in order to find optimal solution for the failure prediction problem. In total, 7 learning algorithms and 24 neural architectures are implemented in two environments - Matlab and specially designed software titled BPnet. The results show that the neural networks can successfully be applied for the problem in hand with prediction rate of 95.4545%, despite having the erroneous or otherwise incomplete sensor measurements invoked in the dataset. Additionally, the real-world experiments are conducted on a mobile robot for obstacle detection and trajectory tracking problems in order to prove the robustness of the proposed prediction approach. In over 96% for the detection problem and 99% for the tracking experiments, neural network successfully predicted the failed information, which evidences the usefulness and the applicability of the developed intelligent method.",
publisher = "Sage Publications Ltd, London",
journal = "Proceedings of The Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Scie",
title = "Neural networks for prediction of robot failures",
pages = "1458-1444",
number = "8",
volume = "228",
doi = "10.1177/0954406213507704"
}

Diryag, A., Mitić, M.,& Miljković, Z.. (2014). Neural networks for prediction of robot failures. in Proceedings of The Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Scie
Sage Publications Ltd, London., 228(8), 1444-1458.
https://doi.org/10.1177/0954406213507704

Diryag A, Mitić M, Miljković Z. Neural networks for prediction of robot failures. in Proceedings of The Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Scie. 2014;228(8):1444-1458.
doi:10.1177/0954406213507704 .

Diryag, Ali, Mitić, Marko, Miljković, Zoran, "Neural networks for prediction of robot failures" in Proceedings of The Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Scie, 228, no. 8 (2014):1444-1458,
https://doi.org/10.1177/0954406213507704 . .

TY  - CONF
AU  - Mitić, Marko
AU  - Miljković, Zoran
AU  - Vuković, Najdan
AU  - Babić, Bojan
AU  - Diryag, Ali
PY  - 2013
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4473
AB  - In recent years, the industrial robotic systems are designed with abilities to adapt and to learn in a structured or unstructured environment. They are able to predict and to react to the undesirable and uncontrollable disturbances which frequently interfere in mission accomplishment. In order to prevent system failure and/or unwanted robot behaviour, various techniques have been addressed. In this study, a novel approach based on the neural networks (NNs) is employed for prediction of robot execution failures. The training and testing dataset used in the experiment consists of forces and torques memorized immediately after the real robot failed in assignment execution. Two types of networks are utilized in order to find best prediction method - recurrent NNs and feedforward NNs. Moreover, we investigated 24 neural architectures implemented in Matlab software package. The experimental results confirm that this approach can be successfully applied to the failures prediction problem, and that the NNs outperform other artificial intelligence techniques in this domain. To further validate a novel method, real world experiments are conducted on a Khepera II mobile robot in an indoor structured environment. The obtained results for trajectory tracking problem proved usefulness and the applicability of the proposed solution.
PB  - Kraljevo : Faculty of Mechanical and Civil Engineering
C3  - Proceedings of the 35th International Conference on Production Engineering (ICPE-S 2013), 25 – 28 September 2013 Kraljevo-Kopaonik
T1  - Prediction of Robot Execution Failures Using Neural Networks
EP  - 338
SP  - 335
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4473
ER  - 

@conference{
author = "Mitić, Marko and Miljković, Zoran and Vuković, Najdan and Babić, Bojan and Diryag, Ali",
year = "2013",
abstract = "In recent years, the industrial robotic systems are designed with abilities to adapt and to learn in a structured or unstructured environment. They are able to predict and to react to the undesirable and uncontrollable disturbances which frequently interfere in mission accomplishment. In order to prevent system failure and/or unwanted robot behaviour, various techniques have been addressed. In this study, a novel approach based on the neural networks (NNs) is employed for prediction of robot execution failures. The training and testing dataset used in the experiment consists of forces and torques memorized immediately after the real robot failed in assignment execution. Two types of networks are utilized in order to find best prediction method - recurrent NNs and feedforward NNs. Moreover, we investigated 24 neural architectures implemented in Matlab software package. The experimental results confirm that this approach can be successfully applied to the failures prediction problem, and that the NNs outperform other artificial intelligence techniques in this domain. To further validate a novel method, real world experiments are conducted on a Khepera II mobile robot in an indoor structured environment. The obtained results for trajectory tracking problem proved usefulness and the applicability of the proposed solution.",
publisher = "Kraljevo : Faculty of Mechanical and Civil Engineering",
journal = "Proceedings of the 35th International Conference on Production Engineering (ICPE-S 2013), 25 – 28 September 2013 Kraljevo-Kopaonik",
title = "Prediction of Robot Execution Failures Using Neural Networks",
pages = "338-335",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4473"
}

Mitić, M., Miljković, Z., Vuković, N., Babić, B.,& Diryag, A.. (2013). Prediction of Robot Execution Failures Using Neural Networks. in Proceedings of the 35th International Conference on Production Engineering (ICPE-S 2013), 25 – 28 September 2013 Kraljevo-Kopaonik
Kraljevo : Faculty of Mechanical and Civil Engineering., 335-338.
https://hdl.handle.net/21.15107/rcub_machinery_4473

Mitić M, Miljković Z, Vuković N, Babić B, Diryag A. Prediction of Robot Execution Failures Using Neural Networks. in Proceedings of the 35th International Conference on Production Engineering (ICPE-S 2013), 25 – 28 September 2013 Kraljevo-Kopaonik. 2013;:335-338.
https://hdl.handle.net/21.15107/rcub_machinery_4473 .

Mitić, Marko, Miljković, Zoran, Vuković, Najdan, Babić, Bojan, Diryag, Ali, "Prediction of Robot Execution Failures Using Neural Networks" in Proceedings of the 35th International Conference on Production Engineering (ICPE-S 2013), 25 – 28 September 2013 Kraljevo-Kopaonik (2013):335-338,
https://hdl.handle.net/21.15107/rcub_machinery_4473 .

TY  - CONF
AU  - Miljković, Zoran
AU  - Mitić, Marko
AU  - Babić, Bojan
AU  - Diryag, Ali
PY  - 2011
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4949
AB  - In this paper neural network representation for the Q-learning algorithm of a mobile robot is presented. The property of adaptability in modern intelligent manufacturing environments is the significant advantage comparing with the traditional designed facilities. Presented approach characterizes its efficiency and simplicity considering the learning process of the intelligent agent - a mobile robot. Experience gathered from the external sensors in an obstacle avoidance task presents the input of the neural network, enabling the mobile robot to learn the value of the selected actions as the output of the neural network, gradually improving its behaviour. If the more learning epochs are conducted mobile robot could became autonomous, which can 
be crucial advantage for the 21st century manufacturing systems.
PB  - The Aristotle University of Thessaloniki
C3  - Proceedings of the 4th International Conference on Manufacturing Engineering (ICMEN 2011)
T1  - Q-Learning Algorithm for a Mobile Robot Obstacle Avoidance in an Unknown Environment Based on Artificial Neural Networks
EP  - 440
SP  - 431
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4949
ER  - 

@conference{
author = "Miljković, Zoran and Mitić, Marko and Babić, Bojan and Diryag, Ali",
year = "2011",
abstract = "In this paper neural network representation for the Q-learning algorithm of a mobile robot is presented. The property of adaptability in modern intelligent manufacturing environments is the significant advantage comparing with the traditional designed facilities. Presented approach characterizes its efficiency and simplicity considering the learning process of the intelligent agent - a mobile robot. Experience gathered from the external sensors in an obstacle avoidance task presents the input of the neural network, enabling the mobile robot to learn the value of the selected actions as the output of the neural network, gradually improving its behaviour. If the more learning epochs are conducted mobile robot could became autonomous, which can 
be crucial advantage for the 21st century manufacturing systems.",
publisher = "The Aristotle University of Thessaloniki",
journal = "Proceedings of the 4th International Conference on Manufacturing Engineering (ICMEN 2011)",
title = "Q-Learning Algorithm for a Mobile Robot Obstacle Avoidance in an Unknown Environment Based on Artificial Neural Networks",
pages = "440-431",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4949"
}

Miljković, Z., Mitić, M., Babić, B.,& Diryag, A.. (2011). Q-Learning Algorithm for a Mobile Robot Obstacle Avoidance in an Unknown Environment Based on Artificial Neural Networks. in Proceedings of the 4th International Conference on Manufacturing Engineering (ICMEN 2011)
The Aristotle University of Thessaloniki., 431-440.
https://hdl.handle.net/21.15107/rcub_machinery_4949

Miljković Z, Mitić M, Babić B, Diryag A. Q-Learning Algorithm for a Mobile Robot Obstacle Avoidance in an Unknown Environment Based on Artificial Neural Networks. in Proceedings of the 4th International Conference on Manufacturing Engineering (ICMEN 2011). 2011;:431-440.
https://hdl.handle.net/21.15107/rcub_machinery_4949 .

Miljković, Zoran, Mitić, Marko, Babić, Bojan, Diryag, Ali, "Q-Learning Algorithm for a Mobile Robot Obstacle Avoidance in an Unknown Environment Based on Artificial Neural Networks" in Proceedings of the 4th International Conference on Manufacturing Engineering (ICMEN 2011) (2011):431-440,
https://hdl.handle.net/21.15107/rcub_machinery_4949 .