TY  - JOUR
AU  - Mitić, Marko
AU  - Vuković, Najdan
AU  - Petrović, Milica
AU  - Miljković, Zoran
PY  - 2018
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/2880
AB  - Most of today's mobile robots operate in controlled environments prone to various unpredictable conditions. Programming or reprogramming of such systems is time-consuming and requires significant efforts by number of experts. One of the solutions to this problem is to enable the robot to learn from human teacher through demonstrations or observations. This paper presents novel approach that integrates Learning from Demonstrations methodology and chaotic bioinspired optimization algorithms for reproduction of desired motion trajectories. Demonstrations of the different trajectories to reproduce are gathered by human teacher while teleoperating the mobile robot in working environment. The learning (optimization) goal is to produce such sequence of mobile robot actuator commands that generate minimal error in the final robot pose. Four different chaotic methods are implemented, namely chaotic Bat Algorithm, chaotic Firefly Algorithm, chaotic Accelerated Particle Swarm Optimization and newly developed chaotic Grey Wolf Optimizer (CGWO). In order to determine the best map for CGWO, this algorithm is tested on ten benchmark problems using ten well-known chaotic maps. Simulations compare aforementioned algorithms in reproduction of two complex motion trajectories with different length and shape. Moreover, these tests include variation of population in swarm and demonstration examples. Real-world experiment on a nonholonomic mobile robot in indoor environment proves the applicability of the proposed approach.
PB  - Springer London Ltd, London
T2  - Neural Computing & Applications
T1  - Chaotic metaheuristic algorithms for learning and reproduction of robot motion trajectories
EP  - 1083
IS  - 4
SP  - 1065
VL  - 30
DO  - 10.1007/s00521-016-2717-6
ER  - 

@article{
author = "Mitić, Marko and Vuković, Najdan and Petrović, Milica and Miljković, Zoran",
year = "2018",
abstract = "Most of today's mobile robots operate in controlled environments prone to various unpredictable conditions. Programming or reprogramming of such systems is time-consuming and requires significant efforts by number of experts. One of the solutions to this problem is to enable the robot to learn from human teacher through demonstrations or observations. This paper presents novel approach that integrates Learning from Demonstrations methodology and chaotic bioinspired optimization algorithms for reproduction of desired motion trajectories. Demonstrations of the different trajectories to reproduce are gathered by human teacher while teleoperating the mobile robot in working environment. The learning (optimization) goal is to produce such sequence of mobile robot actuator commands that generate minimal error in the final robot pose. Four different chaotic methods are implemented, namely chaotic Bat Algorithm, chaotic Firefly Algorithm, chaotic Accelerated Particle Swarm Optimization and newly developed chaotic Grey Wolf Optimizer (CGWO). In order to determine the best map for CGWO, this algorithm is tested on ten benchmark problems using ten well-known chaotic maps. Simulations compare aforementioned algorithms in reproduction of two complex motion trajectories with different length and shape. Moreover, these tests include variation of population in swarm and demonstration examples. Real-world experiment on a nonholonomic mobile robot in indoor environment proves the applicability of the proposed approach.",
publisher = "Springer London Ltd, London",
journal = "Neural Computing & Applications",
title = "Chaotic metaheuristic algorithms for learning and reproduction of robot motion trajectories",
pages = "1083-1065",
number = "4",
volume = "30",
doi = "10.1007/s00521-016-2717-6"
}

Mitić, M., Vuković, N., Petrović, M.,& Miljković, Z.. (2018). Chaotic metaheuristic algorithms for learning and reproduction of robot motion trajectories. in Neural Computing & Applications
Springer London Ltd, London., 30(4), 1065-1083.
https://doi.org/10.1007/s00521-016-2717-6

Mitić M, Vuković N, Petrović M, Miljković Z. Chaotic metaheuristic algorithms for learning and reproduction of robot motion trajectories. in Neural Computing & Applications. 2018;30(4):1065-1083.
doi:10.1007/s00521-016-2717-6 .

Mitić, Marko, Vuković, Najdan, Petrović, Milica, Miljković, Zoran, "Chaotic metaheuristic algorithms for learning and reproduction of robot motion trajectories" in Neural Computing & Applications, 30, no. 4 (2018):1065-1083,
https://doi.org/10.1007/s00521-016-2717-6 . .

TY  - CONF
AU  - Petronijević, Jelena
AU  - Petrović, Milica
AU  - Vuković, Najdan
AU  - Mitić, Marko
AU  - Babić, Bojan
AU  - Miljković, Zoran
PY  - 2016
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4561
AB  - Projektovanje tehnoloških procesa predstavlja određivanje postupka proizvodnje uz zadovoljenje prethodno definisanih ciljeva i ograničenja. Terminiranjem proizvodnje se na osnovu proizvodnog plana i prethodno određenih tehnoloških postupaka dodeljuju optimalni proizvodni resursi za odgovarajući vremenski period. Uvođenjem koncepta masovne kastomizacije, već ranije ključne funkcije, projektovanje i terminiranje proizvodnje, sada imaju krucijalnu ulogu u tehnološkom sistemu zbog sve većih zahteva koje se pred ove funkcije postavljaju. Rad se bavi uvođenjem koncepta multiagentnih i holon tehnoloških sistema uz pregled stanja u oblasti projektovanja tehnoloških procesa i terminiranja proizvodnje. Radom je obuhvaćen tradicionalni, sledstveni, pristup projektovanju i terminiranju, ali i integrisan prilaz problematici.
AB  - Process planning can be defined as determination of manufacturing processes by achieving its goals and constraints. Scheduling process assigns optimal manufacturing resources over time based on production plan and previously determined process plans. With the mass customization concept, previously key functions in the production, process planning and scheduling, now become crucial for satisfaction of more demanding requirements. The paper introduces the concepts of multi-agent and holonic manufacturing systems and presents state of the process planning and scheduling area of research. It gives an overview on both, sequential and integrated, process planning and scheduling.
PB  - JUPITER Asocijacija, Univerzitet u Beogradu - Mašinski fakultet
C3  - 40. JUPITER Konferencija, 36. simpozijum „NU-ROBOTI-FTS“ : Zbornik radova, Beograd, maj 2016
T1  - Мултиагентни и холон технолошки системи у пројектовању технолошких процеса и терминирању производње
T1  - Multi-agent and Holonic Manufacturing Systems for Process Plannong and Scheduling
EP  - 3.68
SP  - 3.63
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4561
ER  - 

@conference{
author = "Petronijević, Jelena and Petrović, Milica and Vuković, Najdan and Mitić, Marko and Babić, Bojan and Miljković, Zoran",
year = "2016",
abstract = "Projektovanje tehnoloških procesa predstavlja određivanje postupka proizvodnje uz zadovoljenje prethodno definisanih ciljeva i ograničenja. Terminiranjem proizvodnje se na osnovu proizvodnog plana i prethodno određenih tehnoloških postupaka dodeljuju optimalni proizvodni resursi za odgovarajući vremenski period. Uvođenjem koncepta masovne kastomizacije, već ranije ključne funkcije, projektovanje i terminiranje proizvodnje, sada imaju krucijalnu ulogu u tehnološkom sistemu zbog sve većih zahteva koje se pred ove funkcije postavljaju. Rad se bavi uvođenjem koncepta multiagentnih i holon tehnoloških sistema uz pregled stanja u oblasti projektovanja tehnoloških procesa i terminiranja proizvodnje. Radom je obuhvaćen tradicionalni, sledstveni, pristup projektovanju i terminiranju, ali i integrisan prilaz problematici., Process planning can be defined as determination of manufacturing processes by achieving its goals and constraints. Scheduling process assigns optimal manufacturing resources over time based on production plan and previously determined process plans. With the mass customization concept, previously key functions in the production, process planning and scheduling, now become crucial for satisfaction of more demanding requirements. The paper introduces the concepts of multi-agent and holonic manufacturing systems and presents state of the process planning and scheduling area of research. It gives an overview on both, sequential and integrated, process planning and scheduling.",
publisher = "JUPITER Asocijacija, Univerzitet u Beogradu - Mašinski fakultet",
journal = "40. JUPITER Konferencija, 36. simpozijum „NU-ROBOTI-FTS“ : Zbornik radova, Beograd, maj 2016",
title = "Мултиагентни и холон технолошки системи у пројектовању технолошких процеса и терминирању производње, Multi-agent and Holonic Manufacturing Systems for Process Plannong and Scheduling",
pages = "3.68-3.63",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4561"
}

Petronijević, J., Petrović, M., Vuković, N., Mitić, M., Babić, B.,& Miljković, Z.. (2016). Мултиагентни и холон технолошки системи у пројектовању технолошких процеса и терминирању производње. in 40. JUPITER Konferencija, 36. simpozijum „NU-ROBOTI-FTS“ : Zbornik radova, Beograd, maj 2016
JUPITER Asocijacija, Univerzitet u Beogradu - Mašinski fakultet., 3.63-3.68.
https://hdl.handle.net/21.15107/rcub_machinery_4561

Petronijević J, Petrović M, Vuković N, Mitić M, Babić B, Miljković Z. Мултиагентни и холон технолошки системи у пројектовању технолошких процеса и терминирању производње. in 40. JUPITER Konferencija, 36. simpozijum „NU-ROBOTI-FTS“ : Zbornik radova, Beograd, maj 2016. 2016;:3.63-3.68.
https://hdl.handle.net/21.15107/rcub_machinery_4561 .

Petronijević, Jelena, Petrović, Milica, Vuković, Najdan, Mitić, Marko, Babić, Bojan, Miljković, Zoran, "Мултиагентни и холон технолошки системи у пројектовању технолошких процеса и терминирању производње" in 40. JUPITER Konferencija, 36. simpozijum „NU-ROBOTI-FTS“ : Zbornik radova, Beograd, maj 2016 (2016):3.63-3.68,
https://hdl.handle.net/21.15107/rcub_machinery_4561 .

TY  - JOUR
AU  - Petronijević, J
AU  - Petrović, Milica
AU  - Vuković, Najdan
AU  - Mitić, Marko
AU  - Babić, Bojan
AU  - Miljković, Zoran
PY  - 2016
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3965
AB  - Market growth and mass customization cause a need for a change in traditional manufacturing. Decentralized decision making and integration of process planning is necessary in order to become concurrent in the market. The paper presents decentralized decision making methodology using multi-agent systems. The model is used for integrated process planning and scheduling based on the minimum processing time under dynamic change of the environment. Two types of disturbance are used to represent the change: part arrival and machine breakdown. The proposed model comprises part agent, job agent, machine agent and optimization agent. Comparative analysis is conducted using simulation in AnyLogic software in order to verify the proposed approach.
T2  - Applied Mechanics and Materials
T1  - Integrated process planning and scheduling using multi-agent methodology
SP  - 192-198
VL  - 834
DO  - 10.4028/www.scientific.net/AMM.834.193
ER  - 

@article{
author = "Petronijević, J and Petrović, Milica and Vuković, Najdan and Mitić, Marko and Babić, Bojan and Miljković, Zoran",
year = "2016",
abstract = "Market growth and mass customization cause a need for a change in traditional manufacturing. Decentralized decision making and integration of process planning is necessary in order to become concurrent in the market. The paper presents decentralized decision making methodology using multi-agent systems. The model is used for integrated process planning and scheduling based on the minimum processing time under dynamic change of the environment. Two types of disturbance are used to represent the change: part arrival and machine breakdown. The proposed model comprises part agent, job agent, machine agent and optimization agent. Comparative analysis is conducted using simulation in AnyLogic software in order to verify the proposed approach.",
journal = "Applied Mechanics and Materials",
title = "Integrated process planning and scheduling using multi-agent methodology",
pages = "192-198",
volume = "834",
doi = "10.4028/www.scientific.net/AMM.834.193"
}

Petronijević, J., Petrović, M., Vuković, N., Mitić, M., Babić, B.,& Miljković, Z.. (2016). Integrated process planning and scheduling using multi-agent methodology. in Applied Mechanics and Materials, 834, 192-198.
https://doi.org/10.4028/www.scientific.net/AMM.834.193

Petronijević J, Petrović M, Vuković N, Mitić M, Babić B, Miljković Z. Integrated process planning and scheduling using multi-agent methodology. in Applied Mechanics and Materials. 2016;834:192-198.
doi:10.4028/www.scientific.net/AMM.834.193 .

Petronijević, J, Petrović, Milica, Vuković, Najdan, Mitić, Marko, Babić, Bojan, Miljković, Zoran, "Integrated process planning and scheduling using multi-agent methodology" in Applied Mechanics and Materials, 834 (2016):192-198,
https://doi.org/10.4028/www.scientific.net/AMM.834.193 . .

TY  - JOUR
AU  - Petrović, Milica
AU  - Vuković, Najdan
AU  - Mitić, Marko
AU  - Miljković, Zoran
PY  - 2016
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/2462
AB  - Process planning and scheduling are two of the most important manufacturing functions traditionally performed separately and sequentially. These functions being complementary and interrelated, their integration is essential for the optimal utilization of manufacturing resources. Such integration is also significant for improving the performance of the modern manufacturing system. A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes integrated process planning and scheduling (IPPS) problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. Therefore, an optimal solution for the problem is searched in a vast search space. In order to explore the search space comprehensively and avoid being trapped into local optima, this paper focuses on using the method based on the particle swarm optimization algorithm and chaos theory (cPSO). The initial solutions for the IPPS problem are presented in the form of the particles of cPSO algorithm. The particle encoding/decoding scheme is also proposed in this paper. Flexible process and scheduling plans are presented using AND/OR network and five flexibility types: machine, tool, tool access direction (TAD), process, and sequence flexibility. Optimal process plans are obtained by multi objective optimization of production time and production cost. On the other hand, optimal scheduling plans are generated based on three objective functions: makespan, balanced level of machine utilization, and mean flow time. The proposed cPSO algorithm is implemented in Matlab environment and verified extensively using five experimental studies. The experimental results show that the proposed algorithm outperforms genetic algorithm (GA), simulated annealing (SA) based approach, and hybrid algorithm. Moreover, the scheduling plans obtained by the proposed methodology are additionally tested by Khepera II mobile robot using a laboratory model of manufacturing environment.
PB  - Pergamon-Elsevier Science Ltd, Oxford
T2  - Expert Systems With Applications
T1  - Integration of process planning and scheduling using chaotic particle swarm optimization algorithm
EP  - 588
SP  - 569
VL  - 64
DO  - 10.1016/j.eswa.2016.08.019
ER  - 

@article{
author = "Petrović, Milica and Vuković, Najdan and Mitić, Marko and Miljković, Zoran",
year = "2016",
abstract = "Process planning and scheduling are two of the most important manufacturing functions traditionally performed separately and sequentially. These functions being complementary and interrelated, their integration is essential for the optimal utilization of manufacturing resources. Such integration is also significant for improving the performance of the modern manufacturing system. A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes integrated process planning and scheduling (IPPS) problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. Therefore, an optimal solution for the problem is searched in a vast search space. In order to explore the search space comprehensively and avoid being trapped into local optima, this paper focuses on using the method based on the particle swarm optimization algorithm and chaos theory (cPSO). The initial solutions for the IPPS problem are presented in the form of the particles of cPSO algorithm. The particle encoding/decoding scheme is also proposed in this paper. Flexible process and scheduling plans are presented using AND/OR network and five flexibility types: machine, tool, tool access direction (TAD), process, and sequence flexibility. Optimal process plans are obtained by multi objective optimization of production time and production cost. On the other hand, optimal scheduling plans are generated based on three objective functions: makespan, balanced level of machine utilization, and mean flow time. The proposed cPSO algorithm is implemented in Matlab environment and verified extensively using five experimental studies. The experimental results show that the proposed algorithm outperforms genetic algorithm (GA), simulated annealing (SA) based approach, and hybrid algorithm. Moreover, the scheduling plans obtained by the proposed methodology are additionally tested by Khepera II mobile robot using a laboratory model of manufacturing environment.",
publisher = "Pergamon-Elsevier Science Ltd, Oxford",
journal = "Expert Systems With Applications",
title = "Integration of process planning and scheduling using chaotic particle swarm optimization algorithm",
pages = "588-569",
volume = "64",
doi = "10.1016/j.eswa.2016.08.019"
}

Petrović, M., Vuković, N., Mitić, M.,& Miljković, Z.. (2016). Integration of process planning and scheduling using chaotic particle swarm optimization algorithm. in Expert Systems With Applications
Pergamon-Elsevier Science Ltd, Oxford., 64, 569-588.
https://doi.org/10.1016/j.eswa.2016.08.019

Petrović M, Vuković N, Mitić M, Miljković Z. Integration of process planning and scheduling using chaotic particle swarm optimization algorithm. in Expert Systems With Applications. 2016;64:569-588.
doi:10.1016/j.eswa.2016.08.019 .

Petrović, Milica, Vuković, Najdan, Mitić, Marko, Miljković, Zoran, "Integration of process planning and scheduling using chaotic particle swarm optimization algorithm" in Expert Systems With Applications, 64 (2016):569-588,
https://doi.org/10.1016/j.eswa.2016.08.019 . .

TY  - JOUR
AU  - Petrović, Milica
AU  - Mitić, Marko
AU  - Vuković, Najdan
AU  - Miljković, Zoran
PY  - 2016
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/2475
AB  - A variety of manufacturing operations together with a variety of alternative manufacturing resources provide that most jobs in the modern manufacturing systems may have a large number of alternative process plans. For that reason, obtaining an optimal process plan according to all alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) as well as alternative operations has become a very important task in flexible process planning problem research. In this paper, we present and evaluate a new algorithm for optimization of flexible process plans based on utilization of particle swarm optimization (PSO) algorithm and chaos theory. The main idea is to prevent the convergence of PSO in early stages of optimization process by implementing ten different chaotic maps which enlarge search space and provide its diversity. The flexible process plans are represented by using AND/OR network, and machine flexibility, tool flexibility, tool access direction (TAD) flexibility, process flexibility and sequence flexibility are considered. Further, mathematical models for minimization of production time and total production cost are derived. The newly developed algorithm is extensively experimentally verified by using four experimental studies, which show that the developed method outperforms genetic algorithm (GA), simulated annealing (SA), hybrid GA-SA and generic PSO based approach.
PB  - Springer London Ltd, London
T2  - International Journal of Advanced Manufacturing Technology
T1  - Chaotic particle swarm optimization algorithm for flexible process planning
EP  - 2555
IS  - 9-12
SP  - 2535
VL  - 85
DO  - 10.1007/s00170-015-7991-4
ER  - 

@article{
author = "Petrović, Milica and Mitić, Marko and Vuković, Najdan and Miljković, Zoran",
year = "2016",
abstract = "A variety of manufacturing operations together with a variety of alternative manufacturing resources provide that most jobs in the modern manufacturing systems may have a large number of alternative process plans. For that reason, obtaining an optimal process plan according to all alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) as well as alternative operations has become a very important task in flexible process planning problem research. In this paper, we present and evaluate a new algorithm for optimization of flexible process plans based on utilization of particle swarm optimization (PSO) algorithm and chaos theory. The main idea is to prevent the convergence of PSO in early stages of optimization process by implementing ten different chaotic maps which enlarge search space and provide its diversity. The flexible process plans are represented by using AND/OR network, and machine flexibility, tool flexibility, tool access direction (TAD) flexibility, process flexibility and sequence flexibility are considered. Further, mathematical models for minimization of production time and total production cost are derived. The newly developed algorithm is extensively experimentally verified by using four experimental studies, which show that the developed method outperforms genetic algorithm (GA), simulated annealing (SA), hybrid GA-SA and generic PSO based approach.",
publisher = "Springer London Ltd, London",
journal = "International Journal of Advanced Manufacturing Technology",
title = "Chaotic particle swarm optimization algorithm for flexible process planning",
pages = "2555-2535",
number = "9-12",
volume = "85",
doi = "10.1007/s00170-015-7991-4"
}

Petrović, M., Mitić, M., Vuković, N.,& Miljković, Z.. (2016). Chaotic particle swarm optimization algorithm for flexible process planning. in International Journal of Advanced Manufacturing Technology
Springer London Ltd, London., 85(9-12), 2535-2555.
https://doi.org/10.1007/s00170-015-7991-4

Petrović M, Mitić M, Vuković N, Miljković Z. Chaotic particle swarm optimization algorithm for flexible process planning. in International Journal of Advanced Manufacturing Technology. 2016;85(9-12):2535-2555.
doi:10.1007/s00170-015-7991-4 .

Petrović, Milica, Mitić, Marko, Vuković, Najdan, Miljković, Zoran, "Chaotic particle swarm optimization algorithm for flexible process planning" in International Journal of Advanced Manufacturing Technology, 85, no. 9-12 (2016):2535-2555,
https://doi.org/10.1007/s00170-015-7991-4 . .

TY  - JOUR
AU  - Miljković, Zoran
AU  - Vuković, Najdan
AU  - Mitić, Marko
PY  - 2016
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/2472
AB  - The extended Kalman filter (EKF) has become a popular solution for the simultaneous localization and mapping (SLAM). This paper presents the implementation of the EKF coupled with a feedforward neural network for the monocular SLAM. The neural extended Kalman filter (NEKF) is applied online to approximate an error between the motion model of the mobile robot and the real system performance. Inadequate modeling of the robot motion can jeopardize the quality of estimation. The paper shows integration of EKF with feedforward neural network and simulation analysis of its consistency and implementation of the NEKF with a mobile robot, laboratory experimental environment, and a simple USB camera. The simulation and experimental results show that integration of neural network into EKF prediction-correction cycle results in improved consistency and accuracy.
PB  - Sage Publications Ltd, London
T2  - Proceedings of The Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Scie
T1  - Neural extended Kalman filter for monocular SLAM in indoor environment
EP  - 866
IS  - 5
SP  - 856
VL  - 230
DO  - 10.1177/0954406215586589
ER  - 

@article{
author = "Miljković, Zoran and Vuković, Najdan and Mitić, Marko",
year = "2016",
abstract = "The extended Kalman filter (EKF) has become a popular solution for the simultaneous localization and mapping (SLAM). This paper presents the implementation of the EKF coupled with a feedforward neural network for the monocular SLAM. The neural extended Kalman filter (NEKF) is applied online to approximate an error between the motion model of the mobile robot and the real system performance. Inadequate modeling of the robot motion can jeopardize the quality of estimation. The paper shows integration of EKF with feedforward neural network and simulation analysis of its consistency and implementation of the NEKF with a mobile robot, laboratory experimental environment, and a simple USB camera. The simulation and experimental results show that integration of neural network into EKF prediction-correction cycle results in improved consistency and accuracy.",
publisher = "Sage Publications Ltd, London",
journal = "Proceedings of The Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Scie",
title = "Neural extended Kalman filter for monocular SLAM in indoor environment",
pages = "866-856",
number = "5",
volume = "230",
doi = "10.1177/0954406215586589"
}

Miljković, Z., Vuković, N.,& Mitić, M.. (2016). Neural extended Kalman filter for monocular SLAM in indoor environment. in Proceedings of The Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Scie
Sage Publications Ltd, London., 230(5), 856-866.
https://doi.org/10.1177/0954406215586589

Miljković Z, Vuković N, Mitić M. Neural extended Kalman filter for monocular SLAM in indoor environment. in Proceedings of The Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Scie. 2016;230(5):856-866.
doi:10.1177/0954406215586589 .

Miljković, Zoran, Vuković, Najdan, Mitić, Marko, "Neural extended Kalman filter for monocular SLAM in indoor environment" in Proceedings of The Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Scie, 230, no. 5 (2016):856-866,
https://doi.org/10.1177/0954406215586589 . .

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  - GEN
AU  - Petrović, Milica
AU  - Petronijević, Jelena
AU  - Mitić, Marko
AU  - Vuković, Najdan
AU  - Miljković, Zoran
AU  - Babić, Bojan
PY  - 2015
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4813
AB  - Tehničko rešenje - nova metoda (M85), pripada oblasti mašinstva i direktno se odnosi na domen integrisanog projektovanja i terminiranja fleksibilnih tehnoloških procesa. Shodno tome, metoda rešava problem generisanja optimalnih planova terminiranja primenom biološki inspirisanog algoritma na bazi inteligencije roja čestica (engl. PSO – Particle Swarm Optimization) i teorije haosa (engl. Chaos theory). Jedan od nedostataka tradicionalnog PSO algoritma je i konvergencija ka lokalnom optimalnom rešenju u ranim fazama optimizacije. U cilju prevazilaženja nedostatka vezanih za brzu konvergenciju algoritma i povećavanje prostora alternativnih rešenja, haotične mape su implementirane u PSO algoritam. Rezultati optimizacije planova terminiranja za odabrane „benchmark“ delove iz literature pokazuju opravdanost primene predloženog koncepta. Razvijana je u okviru aktivnosti naučnog projekta pod oznakomТР-35004 MPNiTR Vlade Republike Srbije.
T2  - Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер
T1  - Integrisano projektovanje i terminiranje tehnoloških procesa primenom inteligencije roja čestica i teorije haosa
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4813
ER  - 

@misc{
author = "Petrović, Milica and Petronijević, Jelena and Mitić, Marko and Vuković, Najdan and Miljković, Zoran and Babić, Bojan",
year = "2015",
abstract = "Tehničko rešenje - nova metoda (M85), pripada oblasti mašinstva i direktno se odnosi na domen integrisanog projektovanja i terminiranja fleksibilnih tehnoloških procesa. Shodno tome, metoda rešava problem generisanja optimalnih planova terminiranja primenom biološki inspirisanog algoritma na bazi inteligencije roja čestica (engl. PSO – Particle Swarm Optimization) i teorije haosa (engl. Chaos theory). Jedan od nedostataka tradicionalnog PSO algoritma je i konvergencija ka lokalnom optimalnom rešenju u ranim fazama optimizacije. U cilju prevazilaženja nedostatka vezanih za brzu konvergenciju algoritma i povećavanje prostora alternativnih rešenja, haotične mape su implementirane u PSO algoritam. Rezultati optimizacije planova terminiranja za odabrane „benchmark“ delove iz literature pokazuju opravdanost primene predloženog koncepta. Razvijana je u okviru aktivnosti naučnog projekta pod oznakomТР-35004 MPNiTR Vlade Republike Srbije.",
journal = "Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер",
title = "Integrisano projektovanje i terminiranje tehnoloških procesa primenom inteligencije roja čestica i teorije haosa",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4813"
}

Petrović, M., Petronijević, J., Mitić, M., Vuković, N., Miljković, Z.,& Babić, B.. (2015). Integrisano projektovanje i terminiranje tehnoloških procesa primenom inteligencije roja čestica i teorije haosa. in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер.
https://hdl.handle.net/21.15107/rcub_machinery_4813

Petrović M, Petronijević J, Mitić M, Vuković N, Miljković Z, Babić B. Integrisano projektovanje i terminiranje tehnoloških procesa primenom inteligencije roja čestica i teorije haosa. in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер. 2015;.
https://hdl.handle.net/21.15107/rcub_machinery_4813 .

Petrović, Milica, Petronijević, Jelena, Mitić, Marko, Vuković, Najdan, Miljković, Zoran, Babić, Bojan, "Integrisano projektovanje i terminiranje tehnoloških procesa primenom inteligencije roja čestica i teorije haosa" in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер (2015),
https://hdl.handle.net/21.15107/rcub_machinery_4813 .

TY  - JOUR
AU  - Mitić, Marko
AU  - Vuković, Najdan
AU  - Petrović, Milica
AU  - Miljković, Zoran
PY  - 2015
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3929
AB  - Fruit fly optimization algorithm (FOA) is recently presented metaheuristic technique that is inspired by the behavior of fruit flies. This paper improves the standard FOA by introducing the novel parameter integrated with chaos. The performance of developed chaotic fruit fly algorithm (CFOA) is investigated in details on ten well known benchmark problems using fourteen different chaotic maps. Moreover, we performed comparison studies with basic FOA, FOA with Levy flight distribution, and other recently published chaotic algorithms. Statistical results on every optimization task indicate that the chaotic fruit fly algorithm (CFOA) has a very fast convergence rate. In addition, CFOA is compared with recently developed chaos enhanced algorithms such as chaotic bat algorithm, chaotic accelerated particle swarm optimization, chaotic firefly algorithm, chaotic artificial bee colony algorithm, and chaotic cuckoo search. Overall research findings show that FOA with Chebyshev map show superiority in terms of reliability of global optimality and algorithm success rate.
PB  - Elsevier, Amsterdam
T2  - Knowledge-Based Systems
T1  - Chaotic fruit fly optimization algorithm
EP  - 458
SP  - 446
VL  - 89
DO  - 10.1016/j.knosys.2015.08.010
ER  - 

@article{
author = "Mitić, Marko and Vuković, Najdan and Petrović, Milica and Miljković, Zoran",
year = "2015",
abstract = "Fruit fly optimization algorithm (FOA) is recently presented metaheuristic technique that is inspired by the behavior of fruit flies. This paper improves the standard FOA by introducing the novel parameter integrated with chaos. The performance of developed chaotic fruit fly algorithm (CFOA) is investigated in details on ten well known benchmark problems using fourteen different chaotic maps. Moreover, we performed comparison studies with basic FOA, FOA with Levy flight distribution, and other recently published chaotic algorithms. Statistical results on every optimization task indicate that the chaotic fruit fly algorithm (CFOA) has a very fast convergence rate. In addition, CFOA is compared with recently developed chaos enhanced algorithms such as chaotic bat algorithm, chaotic accelerated particle swarm optimization, chaotic firefly algorithm, chaotic artificial bee colony algorithm, and chaotic cuckoo search. Overall research findings show that FOA with Chebyshev map show superiority in terms of reliability of global optimality and algorithm success rate.",
publisher = "Elsevier, Amsterdam",
journal = "Knowledge-Based Systems",
title = "Chaotic fruit fly optimization algorithm",
pages = "458-446",
volume = "89",
doi = "10.1016/j.knosys.2015.08.010"
}

Mitić, M., Vuković, N., Petrović, M.,& Miljković, Z.. (2015). Chaotic fruit fly optimization algorithm. in Knowledge-Based Systems
Elsevier, Amsterdam., 89, 446-458.
https://doi.org/10.1016/j.knosys.2015.08.010

Mitić M, Vuković N, Petrović M, Miljković Z. Chaotic fruit fly optimization algorithm. in Knowledge-Based Systems. 2015;89:446-458.
doi:10.1016/j.knosys.2015.08.010 .

Mitić, Marko, Vuković, Najdan, Petrović, Milica, Miljković, Zoran, "Chaotic fruit fly optimization algorithm" in Knowledge-Based Systems, 89 (2015):446-458,
https://doi.org/10.1016/j.knosys.2015.08.010 . .

TY  - JOUR
AU  - Petrović, Milica
AU  - Mitić, Marko
AU  - Vuković, Najdan
AU  - Petronijević, Jelena
AU  - Miljković, Zoran
AU  - Babi, Bojan
PY  - 2015
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4557
AB  - The paper presents an approach based on the application of the Particle Swarm Intelligence algorithm for solving the combinatorial optimization problem of determining the order of execution of operations when processing parts on machines. The proposed approach considers the following types of flexibility: machine flexibility, tool flexibility, process flexibility, and operation sequence flexibility. To represent the flexibility of the machining process of part processing, the method of representing the manufacturing process through networks was chosen, while for the described mathematical model, the criteria for optimization are minimum production time and minimum costs. Experimental results show that the presented algorithm is more efficient, i.e. to give optimal orders of operations in less time and fewer iterations compared to single GA, SA and hybrid GA-SA algorithm.
PB  - Beograd : JUQS
T2  - International Journal Advanced Quality
T1  - Modified Chaotic Particle Swarm Optimization Algorithm for Flexible Process Planning
EP  - 32
IS  - 3
SP  - 25
VL  - 43
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4557
ER  - 

@article{
author = "Petrović, Milica and Mitić, Marko and Vuković, Najdan and Petronijević, Jelena and Miljković, Zoran and Babi, Bojan",
year = "2015",
abstract = "The paper presents an approach based on the application of the Particle Swarm Intelligence algorithm for solving the combinatorial optimization problem of determining the order of execution of operations when processing parts on machines. The proposed approach considers the following types of flexibility: machine flexibility, tool flexibility, process flexibility, and operation sequence flexibility. To represent the flexibility of the machining process of part processing, the method of representing the manufacturing process through networks was chosen, while for the described mathematical model, the criteria for optimization are minimum production time and minimum costs. Experimental results show that the presented algorithm is more efficient, i.e. to give optimal orders of operations in less time and fewer iterations compared to single GA, SA and hybrid GA-SA algorithm.",
publisher = "Beograd : JUQS",
journal = "International Journal Advanced Quality",
title = "Modified Chaotic Particle Swarm Optimization Algorithm for Flexible Process Planning",
pages = "32-25",
number = "3",
volume = "43",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4557"
}

Petrović, M., Mitić, M., Vuković, N., Petronijević, J., Miljković, Z.,& Babi, B.. (2015). Modified Chaotic Particle Swarm Optimization Algorithm for Flexible Process Planning. in International Journal Advanced Quality
Beograd : JUQS., 43(3), 25-32.
https://hdl.handle.net/21.15107/rcub_machinery_4557

Petrović M, Mitić M, Vuković N, Petronijević J, Miljković Z, Babi B. Modified Chaotic Particle Swarm Optimization Algorithm for Flexible Process Planning. in International Journal Advanced Quality. 2015;43(3):25-32.
https://hdl.handle.net/21.15107/rcub_machinery_4557 .

Petrović, Milica, Mitić, Marko, Vuković, Najdan, Petronijević, Jelena, Miljković, Zoran, Babi, Bojan, "Modified Chaotic Particle Swarm Optimization Algorithm for Flexible Process Planning" in International Journal Advanced Quality, 43, no. 3 (2015):25-32,
https://hdl.handle.net/21.15107/rcub_machinery_4557 .

TY  - JOUR
AU  - Petrović, Milica
AU  - Petronijević, Jelena
AU  - Mitić, Marko
AU  - Vuković, Najdan
AU  - Plemić, Aleksandar
AU  - Miljković, Zoran
AU  - Babić, Bojan
PY  - 2015
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4419
AB  - Obtaining an optimal process plan according to all alternative manufacturing resources has become very important task in flexible process planning problem research. In this paper, we use a novel nature-inspired algorithm called Ant Lion Optimizer (ALO) to solve this NP-hard combinatorial optimization problem. The network representation is adopted to describe flexibilities in process planning and mathematical model for the minimization of the total production time and cost is presented. The algorithm is implemented in Matlab environment and run on the 3.10 GHz processor with 2 GBs of RAM memory. The presented experimental results show that the proposed algorithm performs better in comparison with other bio-inspired optimization algorithms.
PB  - University of Novi Sad - Faculty of Technical Sciences
T2  - Journal of Production Engineering
T1  - The Ant Lion Optimization Algorithm for Flexible Process Planning
EP  - 68
IS  - 2
SP  - 65
VL  - 18
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4419
ER  - 

@article{
author = "Petrović, Milica and Petronijević, Jelena and Mitić, Marko and Vuković, Najdan and Plemić, Aleksandar and Miljković, Zoran and Babić, Bojan",
year = "2015",
abstract = "Obtaining an optimal process plan according to all alternative manufacturing resources has become very important task in flexible process planning problem research. In this paper, we use a novel nature-inspired algorithm called Ant Lion Optimizer (ALO) to solve this NP-hard combinatorial optimization problem. The network representation is adopted to describe flexibilities in process planning and mathematical model for the minimization of the total production time and cost is presented. The algorithm is implemented in Matlab environment and run on the 3.10 GHz processor with 2 GBs of RAM memory. The presented experimental results show that the proposed algorithm performs better in comparison with other bio-inspired optimization algorithms.",
publisher = "University of Novi Sad - Faculty of Technical Sciences",
journal = "Journal of Production Engineering",
title = "The Ant Lion Optimization Algorithm for Flexible Process Planning",
pages = "68-65",
number = "2",
volume = "18",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4419"
}

Petrović, M., Petronijević, J., Mitić, M., Vuković, N., Plemić, A., Miljković, Z.,& Babić, B.. (2015). The Ant Lion Optimization Algorithm for Flexible Process Planning. in Journal of Production Engineering
University of Novi Sad - Faculty of Technical Sciences., 18(2), 65-68.
https://hdl.handle.net/21.15107/rcub_machinery_4419

Petrović M, Petronijević J, Mitić M, Vuković N, Plemić A, Miljković Z, Babić B. The Ant Lion Optimization Algorithm for Flexible Process Planning. in Journal of Production Engineering. 2015;18(2):65-68.
https://hdl.handle.net/21.15107/rcub_machinery_4419 .

Petrović, Milica, Petronijević, Jelena, Mitić, Marko, Vuković, Najdan, Plemić, Aleksandar, Miljković, Zoran, Babić, Bojan, "The Ant Lion Optimization Algorithm for Flexible Process Planning" in Journal of Production Engineering, 18, no. 2 (2015):65-68,
https://hdl.handle.net/21.15107/rcub_machinery_4419 .

TY  - JOUR
AU  - Vuković, Najdan
AU  - Mitić, Marko
AU  - Miljković, Zoran
PY  - 2015
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/2256
AB  - In this paper, we present new Learning from Demonstration-based algorithm that generalizes and extracts relevant features of desired motion trajectories for differential drive mobile robots. The algorithm is tested through series of simulations and real world experiments in which desired task is demonstrated by the human teacher while teleoperating the mobile robot in the working environment. In the first step of the developed method, Gaussian Mixture Model (GMM) is built for incremental motions of the mobile robot between two consecutive poses. After this, the hidden Markov model is used to capture transitions between states (temporal variations of the data between clusters) which are missing from static GMM representation. Generalization of the motion is achieved by using the concept of keyframes, defined as points in which significant changes between GMM/HMM states occur. In the second step, the resulting GMM/HMM representation is used to generate optimal state sequences for each demonstration and to temporally align them, using 1D dynamic time warping, with respect to the one most consistent with the GMM/HMM model. This phase implies extraction of keyframes along all state sequences and projecting them into control space, in which controls are aligned in time as well. Finally, the generalized controls are obtained by averaging over all controls at the keyframes; simple piecewise cubic spline method is used for interpolation between generated control values. The main advantage of the developed algorithm is its ability to learn and generalize from all demonstrated examples which results in high quality reproductions of the motion. The proposed approach is verified both in simulated environment and using real mobile robot.
PB  - Pergamon-Elsevier Science Ltd, Oxford
T2  - Engineering Applications of Artificial Intelligence
T1  - Trajectory learning and reproduction for differential drive mobile robots based on GMM/HMM and dynamic time warping using learning from demonstration framework
EP  - 404
SP  - 388
VL  - 45
DO  - 10.1016/j.engappai.2015.07.002
ER  - 

@article{
author = "Vuković, Najdan and Mitić, Marko and Miljković, Zoran",
year = "2015",
abstract = "In this paper, we present new Learning from Demonstration-based algorithm that generalizes and extracts relevant features of desired motion trajectories for differential drive mobile robots. The algorithm is tested through series of simulations and real world experiments in which desired task is demonstrated by the human teacher while teleoperating the mobile robot in the working environment. In the first step of the developed method, Gaussian Mixture Model (GMM) is built for incremental motions of the mobile robot between two consecutive poses. After this, the hidden Markov model is used to capture transitions between states (temporal variations of the data between clusters) which are missing from static GMM representation. Generalization of the motion is achieved by using the concept of keyframes, defined as points in which significant changes between GMM/HMM states occur. In the second step, the resulting GMM/HMM representation is used to generate optimal state sequences for each demonstration and to temporally align them, using 1D dynamic time warping, with respect to the one most consistent with the GMM/HMM model. This phase implies extraction of keyframes along all state sequences and projecting them into control space, in which controls are aligned in time as well. Finally, the generalized controls are obtained by averaging over all controls at the keyframes; simple piecewise cubic spline method is used for interpolation between generated control values. The main advantage of the developed algorithm is its ability to learn and generalize from all demonstrated examples which results in high quality reproductions of the motion. The proposed approach is verified both in simulated environment and using real mobile robot.",
publisher = "Pergamon-Elsevier Science Ltd, Oxford",
journal = "Engineering Applications of Artificial Intelligence",
title = "Trajectory learning and reproduction for differential drive mobile robots based on GMM/HMM and dynamic time warping using learning from demonstration framework",
pages = "404-388",
volume = "45",
doi = "10.1016/j.engappai.2015.07.002"
}

Vuković, N., Mitić, M.,& Miljković, Z.. (2015). Trajectory learning and reproduction for differential drive mobile robots based on GMM/HMM and dynamic time warping using learning from demonstration framework. in Engineering Applications of Artificial Intelligence
Pergamon-Elsevier Science Ltd, Oxford., 45, 388-404.
https://doi.org/10.1016/j.engappai.2015.07.002

Vuković N, Mitić M, Miljković Z. Trajectory learning and reproduction for differential drive mobile robots based on GMM/HMM and dynamic time warping using learning from demonstration framework. in Engineering Applications of Artificial Intelligence. 2015;45:388-404.
doi:10.1016/j.engappai.2015.07.002 .

Vuković, Najdan, Mitić, Marko, Miljković, Zoran, "Trajectory learning and reproduction for differential drive mobile robots based on GMM/HMM and dynamic time warping using learning from demonstration framework" in Engineering Applications of Artificial Intelligence, 45 (2015):388-404,
https://doi.org/10.1016/j.engappai.2015.07.002 . .

TY  - JOUR
AU  - Mitić, Marko
AU  - Miljković, Zoran
PY  - 2015
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/2194
AB  - In this paper, a novel bio-inspired learning control approach (BILCA) for mobile robots based on Learning from Demonstration (LfD), Firefly Algorithm (FA), and homography between current and target camera view is developed. BILCA consists of two steps: (i) first step in which the actuator commands are learned using FA and demonstrations of desired behavior, and (ii) second step in which the obtained wheel commands are evaluated through the real world experiment. Two different problems are considered in this study: trajectory reproduction, and generation of visual control commands for correction of robot orientation. Developed simulations are used to evaluate BILCA in the domain of learning actuator commands for reproduction of different complex trajectories. Results show that the bigger firefly swarms produce better results in terms of accuracy in the final mobile robot pose, and that the desired trajectory is reproduced with minimal error in final control iteration. Likewise, simulations prove that the FA outperforms other metaheuristic techniques. Experiment conducted on a real mobile robot in indoor environment unifies two considered problems within a single transportation task. Depending of the feature position in the image plane, the homography controller for forward motion or the BILCA based controller for robot orientation correction is employed. Experimental results show the applicability and effectiveness of the developed intelligent approach in real world conditions.
PB  - Pergamon-Elsevier Science Ltd, Oxford
T2  - Expert Systems With Applications
T1  - Bio-inspired approach to learning robot motion trajectories and visual control commands
EP  - 2637
IS  - 5
SP  - 2624
VL  - 42
DO  - 10.1016/j.eswa.2014.10.053
ER  - 

@article{
author = "Mitić, Marko and Miljković, Zoran",
year = "2015",
abstract = "In this paper, a novel bio-inspired learning control approach (BILCA) for mobile robots based on Learning from Demonstration (LfD), Firefly Algorithm (FA), and homography between current and target camera view is developed. BILCA consists of two steps: (i) first step in which the actuator commands are learned using FA and demonstrations of desired behavior, and (ii) second step in which the obtained wheel commands are evaluated through the real world experiment. Two different problems are considered in this study: trajectory reproduction, and generation of visual control commands for correction of robot orientation. Developed simulations are used to evaluate BILCA in the domain of learning actuator commands for reproduction of different complex trajectories. Results show that the bigger firefly swarms produce better results in terms of accuracy in the final mobile robot pose, and that the desired trajectory is reproduced with minimal error in final control iteration. Likewise, simulations prove that the FA outperforms other metaheuristic techniques. Experiment conducted on a real mobile robot in indoor environment unifies two considered problems within a single transportation task. Depending of the feature position in the image plane, the homography controller for forward motion or the BILCA based controller for robot orientation correction is employed. Experimental results show the applicability and effectiveness of the developed intelligent approach in real world conditions.",
publisher = "Pergamon-Elsevier Science Ltd, Oxford",
journal = "Expert Systems With Applications",
title = "Bio-inspired approach to learning robot motion trajectories and visual control commands",
pages = "2637-2624",
number = "5",
volume = "42",
doi = "10.1016/j.eswa.2014.10.053"
}

Mitić, M.,& Miljković, Z.. (2015). Bio-inspired approach to learning robot motion trajectories and visual control commands. in Expert Systems With Applications
Pergamon-Elsevier Science Ltd, Oxford., 42(5), 2624-2637.
https://doi.org/10.1016/j.eswa.2014.10.053

Mitić M, Miljković Z. Bio-inspired approach to learning robot motion trajectories and visual control commands. in Expert Systems With Applications. 2015;42(5):2624-2637.
doi:10.1016/j.eswa.2014.10.053 .

Mitić, Marko, Miljković, Zoran, "Bio-inspired approach to learning robot motion trajectories and visual control commands" in Expert Systems With Applications, 42, no. 5 (2015):2624-2637,
https://doi.org/10.1016/j.eswa.2014.10.053 . .

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  - GEN
AU  - Mitić, Marko
AU  - Vuković, Najdan
AU  - Petrović, Milica
AU  - Petronijević, Jelena
AU  - Miljković, Zoran
AU  - Lazarević, Ivan
PY  - 2014
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4753
AB  - Tehničko rešenje - nova metoda (M85), odnosi se na rešavanje kompleksnog problema upravljanja inteligentnog mobilnog robota primenom empirijske upravljačke teorije na bazi biološki inspirisanih algoritama optimizacije i mašinskog učenja demonstracijom,  i to tako da se upravljačke komande mobilnog robota koriste za reprodukciju više trajektorija željenog oblika u okviru modula za demonstraciju, dok se u modulu mašinskog učenja vrši implementacija metoda optimizacije u cilju  određivanja optimalne trajektorija robota. Ova metoda je razvijana u okviru naučnog  projekta pod oznakom TR-35004 MPNiTR Vlade Republike Srbije
T2  - Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер
T1  - Reprodukcija kompleksnih trajektorija mobilnog robota na bazi biološki inspirisanih algoritama
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4753
ER  - 

@misc{
author = "Mitić, Marko and Vuković, Najdan and Petrović, Milica and Petronijević, Jelena and Miljković, Zoran and Lazarević, Ivan",
year = "2014",
abstract = "Tehničko rešenje - nova metoda (M85), odnosi se na rešavanje kompleksnog problema upravljanja inteligentnog mobilnog robota primenom empirijske upravljačke teorije na bazi biološki inspirisanih algoritama optimizacije i mašinskog učenja demonstracijom,  i to tako da se upravljačke komande mobilnog robota koriste za reprodukciju više trajektorija željenog oblika u okviru modula za demonstraciju, dok se u modulu mašinskog učenja vrši implementacija metoda optimizacije u cilju  određivanja optimalne trajektorija robota. Ova metoda je razvijana u okviru naučnog  projekta pod oznakom TR-35004 MPNiTR Vlade Republike Srbije",
journal = "Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер",
title = "Reprodukcija kompleksnih trajektorija mobilnog robota na bazi biološki inspirisanih algoritama",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4753"
}

Mitić, M., Vuković, N., Petrović, M., Petronijević, J., Miljković, Z.,& Lazarević, I.. (2014). Reprodukcija kompleksnih trajektorija mobilnog robota na bazi biološki inspirisanih algoritama. in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер.
https://hdl.handle.net/21.15107/rcub_machinery_4753

Mitić M, Vuković N, Petrović M, Petronijević J, Miljković Z, Lazarević I. Reprodukcija kompleksnih trajektorija mobilnog robota na bazi biološki inspirisanih algoritama. in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер. 2014;.
https://hdl.handle.net/21.15107/rcub_machinery_4753 .

Mitić, Marko, Vuković, Najdan, Petrović, Milica, Petronijević, Jelena, Miljković, Zoran, Lazarević, Ivan, "Reprodukcija kompleksnih trajektorija mobilnog robota na bazi biološki inspirisanih algoritama" in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер (2014),
https://hdl.handle.net/21.15107/rcub_machinery_4753 .

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  - JOUR
AU  - Mitić, Marko
AU  - Miljković, Zoran
PY  - 2014
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/1894
AB  - The control of a robot system using camera information is a challenging task regarding unpredictable conditions, such as feature point mismatch and changing scene illumination. This paper presents a solution for the visual control of a nonholonomic mobile robot in demanding real world circumstances based on machine learning techniques. A novel intelligent approach for mobile robots using neural networks (NNs), learning from demonstration (LfD) framework, and epipolar geometry between two views is proposed and evaluated in a series of experiments. A direct mapping from the image space to the actuator command is conducted using two phases. In an offline phase, NN-LfD approach is employed in order to relate the feature position in the image plane with the angular velocity for lateral motion correction. An online phase refers to a switching vision based scheme between the epipole based linear velocity controller and NN-LfD based angular velocity controller, which selection depends on the feature distance from the pre-defined interest area in the image. In total, 18 architectures and 6 learning algorithms are tested in order to find optimal solution for robot control. The best training outcomes for each learning algorithms are then employed in real time so as to discover optimal NN configuration for robot orientation correction. Experiments conducted on a nonholonomic mobile robot in a structured indoor environment confirm an excellent performance with respect to the system robustness and positioning accuracy in the desired location.
PB  - Springer, New York
T2  - Soft Computing
T1  - Neural network learning from demonstration and epipolar geometry for visual control of a nonholonomic mobile robot
EP  - 1025
IS  - 5
SP  - 1011
VL  - 18
DO  - 10.1007/s00500-013-1121-8
ER  - 

@article{
author = "Mitić, Marko and Miljković, Zoran",
year = "2014",
abstract = "The control of a robot system using camera information is a challenging task regarding unpredictable conditions, such as feature point mismatch and changing scene illumination. This paper presents a solution for the visual control of a nonholonomic mobile robot in demanding real world circumstances based on machine learning techniques. A novel intelligent approach for mobile robots using neural networks (NNs), learning from demonstration (LfD) framework, and epipolar geometry between two views is proposed and evaluated in a series of experiments. A direct mapping from the image space to the actuator command is conducted using two phases. In an offline phase, NN-LfD approach is employed in order to relate the feature position in the image plane with the angular velocity for lateral motion correction. An online phase refers to a switching vision based scheme between the epipole based linear velocity controller and NN-LfD based angular velocity controller, which selection depends on the feature distance from the pre-defined interest area in the image. In total, 18 architectures and 6 learning algorithms are tested in order to find optimal solution for robot control. The best training outcomes for each learning algorithms are then employed in real time so as to discover optimal NN configuration for robot orientation correction. Experiments conducted on a nonholonomic mobile robot in a structured indoor environment confirm an excellent performance with respect to the system robustness and positioning accuracy in the desired location.",
publisher = "Springer, New York",
journal = "Soft Computing",
title = "Neural network learning from demonstration and epipolar geometry for visual control of a nonholonomic mobile robot",
pages = "1025-1011",
number = "5",
volume = "18",
doi = "10.1007/s00500-013-1121-8"
}

Mitić, M.,& Miljković, Z.. (2014). Neural network learning from demonstration and epipolar geometry for visual control of a nonholonomic mobile robot. in Soft Computing
Springer, New York., 18(5), 1011-1025.
https://doi.org/10.1007/s00500-013-1121-8

Mitić M, Miljković Z. Neural network learning from demonstration and epipolar geometry for visual control of a nonholonomic mobile robot. in Soft Computing. 2014;18(5):1011-1025.
doi:10.1007/s00500-013-1121-8 .

Mitić, Marko, Miljković, Zoran, "Neural network learning from demonstration and epipolar geometry for visual control of a nonholonomic mobile robot" in Soft Computing, 18, no. 5 (2014):1011-1025,
https://doi.org/10.1007/s00500-013-1121-8 . .

TY  - CONF
AU  - Mitić, Marko
AU  - Miljković, Zoran
AU  - Vuković, Najdan
AU  - Lazarević, Ivan
PY  - 2013
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4619
AB  - The paper analyzed the robustness of the control system based on information obtained from the camera to unpredictable disturbances in a static environment such as the estimation of the distance to the selected object and/or a sudden change in lighting. Experimental results on the anthropomorphic robot named "NeuroArm Manipulator System" (with the use of the CMUCam 3 camera) indicate the necessity of implementing an empirical subsystem based on machine learning in order to successfully manipulate work-pieces within manufacturing systems for the production of sheet metal parts.
PB  - Belgrade : Serbian Society of Mechanics
C3  - Proceedings of the 4th Serbian Congress on Theoretical and Applied Mechanics, 4-7th June, 2013, Vrnjačka Banja
T1  - Visual Control of a Mobile Robot Using Homography and Learning from Demonstration Methodology
EP  - 680
SP  - 675
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4619
ER  - 

@conference{
author = "Mitić, Marko and Miljković, Zoran and Vuković, Najdan and Lazarević, Ivan",
year = "2013",
abstract = "The paper analyzed the robustness of the control system based on information obtained from the camera to unpredictable disturbances in a static environment such as the estimation of the distance to the selected object and/or a sudden change in lighting. Experimental results on the anthropomorphic robot named "NeuroArm Manipulator System" (with the use of the CMUCam 3 camera) indicate the necessity of implementing an empirical subsystem based on machine learning in order to successfully manipulate work-pieces within manufacturing systems for the production of sheet metal parts.",
publisher = "Belgrade : Serbian Society of Mechanics",
journal = "Proceedings of the 4th Serbian Congress on Theoretical and Applied Mechanics, 4-7th June, 2013, Vrnjačka Banja",
title = "Visual Control of a Mobile Robot Using Homography and Learning from Demonstration Methodology",
pages = "680-675",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4619"
}

Mitić, M., Miljković, Z., Vuković, N.,& Lazarević, I.. (2013). Visual Control of a Mobile Robot Using Homography and Learning from Demonstration Methodology. in Proceedings of the 4th Serbian Congress on Theoretical and Applied Mechanics, 4-7th June, 2013, Vrnjačka Banja
Belgrade : Serbian Society of Mechanics., 675-680.
https://hdl.handle.net/21.15107/rcub_machinery_4619

Mitić M, Miljković Z, Vuković N, Lazarević I. Visual Control of a Mobile Robot Using Homography and Learning from Demonstration Methodology. in Proceedings of the 4th Serbian Congress on Theoretical and Applied Mechanics, 4-7th June, 2013, Vrnjačka Banja. 2013;:675-680.
https://hdl.handle.net/21.15107/rcub_machinery_4619 .

Mitić, Marko, Miljković, Zoran, Vuković, Najdan, Lazarević, Ivan, "Visual Control of a Mobile Robot Using Homography and Learning from Demonstration Methodology" in Proceedings of the 4th Serbian Congress on Theoretical and Applied Mechanics, 4-7th June, 2013, Vrnjačka Banja (2013):675-680,
https://hdl.handle.net/21.15107/rcub_machinery_4619 .

TY  - CONF
AU  - Vuković, Najdan
AU  - Miljković, Zoran
AU  - Mitić, Marko
AU  - Petrović, Milica
PY  - 2013
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4467
AB  - In this paper we focus on learning of motion trajectories for differential drive mobile robot. Learning from Demonstration (LfD) is applied to enable mobile robot to learn and reproduce trajectories which are hard to model due to unknown dynamics and uncertainty. To solve this problem, we build the probabilistic model of the mobile robot motion in two steps. Firstly, Gaussian Mixture Model (GMM) of incremental robot motions is built, while in the second step the hidden Markov Model (HMM) is applied to extract transition matrix and most likely sequence of GMMs for each training trajectory. The final step assumes estimating the optimal sequence of incremental motions for each of N different training trajectories. To summarize the main idea: each of N hidden Markov models model the desired robot motion given as a sequence of M low level robot motions. In contrast to conventional mixture models, hidden Markov model captures temporal dependencies between mixtures, which is i a significant advantage of HMM over conventional mixture models. Experimental results demonstrate applicability and optimal performance of the proposed learning algorithm.
PB  - Belgrade : Serbian Society of Mechanics
C3  - Proceedings of the 4th Serbian Congress on Theoretical and Applied Mechanics, Vrnjačka Banja, Serbia, 4-7 June 2013
T1  - Learning Motion Trajectories of Differential Drive Mobile Robot Using Gaussian Mixtures and Hidden Markov Model
EP  - A-13:6
SP  - A-13:1
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4467
ER  - 

@conference{
author = "Vuković, Najdan and Miljković, Zoran and Mitić, Marko and Petrović, Milica",
year = "2013",
abstract = "In this paper we focus on learning of motion trajectories for differential drive mobile robot. Learning from Demonstration (LfD) is applied to enable mobile robot to learn and reproduce trajectories which are hard to model due to unknown dynamics and uncertainty. To solve this problem, we build the probabilistic model of the mobile robot motion in two steps. Firstly, Gaussian Mixture Model (GMM) of incremental robot motions is built, while in the second step the hidden Markov Model (HMM) is applied to extract transition matrix and most likely sequence of GMMs for each training trajectory. The final step assumes estimating the optimal sequence of incremental motions for each of N different training trajectories. To summarize the main idea: each of N hidden Markov models model the desired robot motion given as a sequence of M low level robot motions. In contrast to conventional mixture models, hidden Markov model captures temporal dependencies between mixtures, which is i a significant advantage of HMM over conventional mixture models. Experimental results demonstrate applicability and optimal performance of the proposed learning algorithm.",
publisher = "Belgrade : Serbian Society of Mechanics",
journal = "Proceedings of the 4th Serbian Congress on Theoretical and Applied Mechanics, Vrnjačka Banja, Serbia, 4-7 June 2013",
title = "Learning Motion Trajectories of Differential Drive Mobile Robot Using Gaussian Mixtures and Hidden Markov Model",
pages = "A-13:6-A-13:1",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4467"
}

Vuković, N., Miljković, Z., Mitić, M.,& Petrović, M.. (2013). Learning Motion Trajectories of Differential Drive Mobile Robot Using Gaussian Mixtures and Hidden Markov Model. in Proceedings of the 4th Serbian Congress on Theoretical and Applied Mechanics, Vrnjačka Banja, Serbia, 4-7 June 2013
Belgrade : Serbian Society of Mechanics., A-13:1-A-13:6.
https://hdl.handle.net/21.15107/rcub_machinery_4467

Vuković N, Miljković Z, Mitić M, Petrović M. Learning Motion Trajectories of Differential Drive Mobile Robot Using Gaussian Mixtures and Hidden Markov Model. in Proceedings of the 4th Serbian Congress on Theoretical and Applied Mechanics, Vrnjačka Banja, Serbia, 4-7 June 2013. 2013;:A-13:1-A-13:6.
https://hdl.handle.net/21.15107/rcub_machinery_4467 .

Vuković, Najdan, Miljković, Zoran, Mitić, Marko, Petrović, Milica, "Learning Motion Trajectories of Differential Drive Mobile Robot Using Gaussian Mixtures and Hidden Markov Model" in Proceedings of the 4th Serbian Congress on Theoretical and Applied Mechanics, Vrnjačka Banja, Serbia, 4-7 June 2013 (2013):A-13:1-A-13:6,
https://hdl.handle.net/21.15107/rcub_machinery_4467 .

TY  - CONF
AU  - Vuković, Najdan
AU  - Miljković, Zoran
AU  - Mitić, Marko
AU  - Petrović, Milica
AU  - Husen, Mohamed A.
PY  - 2013
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4472
AB  - To navigate autonomously in a manufacturing environment Automated Guided Vehicle (AGV) needs the ability to infer its pose. This paper presents the implementation of the Extended Kalman Filter (EKF) coupled with a feedforward neural network for the Visual Simultaneous Localization and Mapping (VSLAM). The neural extended Kalman filter (NEKF) is applied on-line to model error between real and estimated robot motion. Implementation of the NEKF is achieved by using mobile robot, an experimental environment and a simple camera. By introducing neural
network into the EKF estimation procedure, the quality of performance can be improved.
PB  - Kraljevo : Faculty of Mechanical and Civil Engineering
C3  - Proceedings of the 35th International Conference on Production Engineering (ICPE-S 2013)
T1  - Neural Extended Kalman Filter for State Estimation of Automated Guided Vehicle in Manufacturing Environment
EP  - 334
SP  - 331
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4472
ER  - 

@conference{
author = "Vuković, Najdan and Miljković, Zoran and Mitić, Marko and Petrović, Milica and Husen, Mohamed A.",
year = "2013",
abstract = "To navigate autonomously in a manufacturing environment Automated Guided Vehicle (AGV) needs the ability to infer its pose. This paper presents the implementation of the Extended Kalman Filter (EKF) coupled with a feedforward neural network for the Visual Simultaneous Localization and Mapping (VSLAM). The neural extended Kalman filter (NEKF) is applied on-line to model error between real and estimated robot motion. Implementation of the NEKF is achieved by using mobile robot, an experimental environment and a simple camera. By introducing neural
network into the EKF estimation procedure, the quality of performance can be improved.",
publisher = "Kraljevo : Faculty of Mechanical and Civil Engineering",
journal = "Proceedings of the 35th International Conference on Production Engineering (ICPE-S 2013)",
title = "Neural Extended Kalman Filter for State Estimation of Automated Guided Vehicle in Manufacturing Environment",
pages = "334-331",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4472"
}

Vuković, N., Miljković, Z., Mitić, M., Petrović, M.,& Husen, M. A.. (2013). Neural Extended Kalman Filter for State Estimation of Automated Guided Vehicle in Manufacturing Environment. in Proceedings of the 35th International Conference on Production Engineering (ICPE-S 2013)
Kraljevo : Faculty of Mechanical and Civil Engineering., 331-334.
https://hdl.handle.net/21.15107/rcub_machinery_4472

Vuković N, Miljković Z, Mitić M, Petrović M, Husen MA. Neural Extended Kalman Filter for State Estimation of Automated Guided Vehicle in Manufacturing Environment. in Proceedings of the 35th International Conference on Production Engineering (ICPE-S 2013). 2013;:331-334.
https://hdl.handle.net/21.15107/rcub_machinery_4472 .

Vuković, Najdan, Miljković, Zoran, Mitić, Marko, Petrović, Milica, Husen, Mohamed A., "Neural Extended Kalman Filter for State Estimation of Automated Guided Vehicle in Manufacturing Environment" in Proceedings of the 35th International Conference on Production Engineering (ICPE-S 2013) (2013):331-334,
https://hdl.handle.net/21.15107/rcub_machinery_4472 .

TY  - JOUR
AU  - Miljković, Zoran
AU  - Vuković, Najdan
AU  - Mitić, Marko
AU  - Babić, Bojan
PY  - 2013
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/1691
AB  - Automated guided vehicles (AGVs) are a common choice made by many companies for material handling (MH) in manufacturing systems. AGV-based internal transport of raw materials, goods, and parts is becoming improved with advances in technology. Demands for fast, efficient, and reliable transport imply the usage of the flexible AGVs with onboard sensing and special kinds of algorithms needed for daily operations. So far, the majority of these transport solutions have not considered the modern techniques for visual servoing, monocular SLAM, and consequently, the usage of camera as onboard sensor for AGVs. In this research, a new hybrid control of AGV is proposed. The main control algorithm consists of two independent control loops: position-based control (PBC) for global navigation and image based visual seroving (IBVS) for fine motions needed for accurate steering towards loading/unloading point. By separating the initial transportation task into two parts (global navigation towards the goal pose near the loading/unloading point and fine motion from the goal pose to the loading/unloading point), the proposed hybrid control bypasses the need for artificial landmarks or accurate map of the environment. The state estimation of the robot pose is determined in terms of monocular SLAM, via extended Kalman filter coupled with feedforward neural network-the neural extended Kalman filter (NEKF). NEKF is used to model unknown disturbances and to improve the robot state transition model. The integration of the new hybrid control and NEKF has been tested in laboratory with the mobile robot and simple camera. Experimental results present the effectiveness of the proposed hybrid control approach.
PB  - Springer London Ltd, London
T2  - International Journal of Advanced Manufacturing Technology
T1  - New hybrid vision-based control approach for automated guided vehicles
EP  - 249
IS  - 1-4
SP  - 231
VL  - 66
DO  - 10.1007/s00170-012-4321-y
ER  - 

@article{
author = "Miljković, Zoran and Vuković, Najdan and Mitić, Marko and Babić, Bojan",
year = "2013",
abstract = "Automated guided vehicles (AGVs) are a common choice made by many companies for material handling (MH) in manufacturing systems. AGV-based internal transport of raw materials, goods, and parts is becoming improved with advances in technology. Demands for fast, efficient, and reliable transport imply the usage of the flexible AGVs with onboard sensing and special kinds of algorithms needed for daily operations. So far, the majority of these transport solutions have not considered the modern techniques for visual servoing, monocular SLAM, and consequently, the usage of camera as onboard sensor for AGVs. In this research, a new hybrid control of AGV is proposed. The main control algorithm consists of two independent control loops: position-based control (PBC) for global navigation and image based visual seroving (IBVS) for fine motions needed for accurate steering towards loading/unloading point. By separating the initial transportation task into two parts (global navigation towards the goal pose near the loading/unloading point and fine motion from the goal pose to the loading/unloading point), the proposed hybrid control bypasses the need for artificial landmarks or accurate map of the environment. The state estimation of the robot pose is determined in terms of monocular SLAM, via extended Kalman filter coupled with feedforward neural network-the neural extended Kalman filter (NEKF). NEKF is used to model unknown disturbances and to improve the robot state transition model. The integration of the new hybrid control and NEKF has been tested in laboratory with the mobile robot and simple camera. Experimental results present the effectiveness of the proposed hybrid control approach.",
publisher = "Springer London Ltd, London",
journal = "International Journal of Advanced Manufacturing Technology",
title = "New hybrid vision-based control approach for automated guided vehicles",
pages = "249-231",
number = "1-4",
volume = "66",
doi = "10.1007/s00170-012-4321-y"
}

Miljković, Z., Vuković, N., Mitić, M.,& Babić, B.. (2013). New hybrid vision-based control approach for automated guided vehicles. in International Journal of Advanced Manufacturing Technology
Springer London Ltd, London., 66(1-4), 231-249.
https://doi.org/10.1007/s00170-012-4321-y

Miljković Z, Vuković N, Mitić M, Babić B. New hybrid vision-based control approach for automated guided vehicles. in International Journal of Advanced Manufacturing Technology. 2013;66(1-4):231-249.
doi:10.1007/s00170-012-4321-y .

Miljković, Zoran, Vuković, Najdan, Mitić, Marko, Babić, Bojan, "New hybrid vision-based control approach for automated guided vehicles" in International Journal of Advanced Manufacturing Technology, 66, no. 1-4 (2013):231-249,
https://doi.org/10.1007/s00170-012-4321-y . .

TY  - JOUR
AU  - Miljković, Zoran
AU  - Mitić, Marko
AU  - Lazarević, Mihailo
AU  - Babić, Bojan
PY  - 2013
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/1690
AB  - It is known that most of the key problems in visual servo control of robots are related to the performance analysis of the system considering measurement and modeling errors. In this paper, the development and performance evaluation of a novel intelligent visual servo controller for a robot manipulator using neural network Reinforcement Learning is presented. By implementing machine learning techniques into the vision based control scheme, the robot is enabled to improve its performance online and to adapt to the changing conditions in the environment. Two different temporal difference algorithms (Q-learning and SARSA) coupled with neural networks are developed and tested through different visual control scenarios. A database of representative learning samples is employed so as to speed up the convergence of the neural network and real-time learning of robot behavior. Moreover, the visual servoing task is divided into two steps in order to ensure the visibility of the features: in the first step centering behavior of the robot is conducted using neural network Reinforcement Learning controller, while the second step involves switching control between the traditional Image Based Visual Servoing and the neural network Reinforcement Learning for enabling approaching behavior of the manipulator. The correction in robot motion is achieved with the definition of the areas of interest for the image features independently in both control steps. Various simulations are developed in order to present the robustness of the developed system regarding calibration error, modeling error, and image noise. In addition, a comparison with the traditional Image Based Visual Servoing is presented. Real world experiments on a robot manipulator with the low cost vision system demonstrate the effectiveness of the proposed approach.
PB  - Pergamon-Elsevier Science Ltd, Oxford
T2  - Expert Systems With Applications
T1  - Neural network Reinforcement Learning for visual control of robot manipulators
EP  - 1736
IS  - 5
SP  - 1721
VL  - 40
DO  - 10.1016/j.eswa.2012.09.010
ER  - 

@article{
author = "Miljković, Zoran and Mitić, Marko and Lazarević, Mihailo and Babić, Bojan",
year = "2013",
abstract = "It is known that most of the key problems in visual servo control of robots are related to the performance analysis of the system considering measurement and modeling errors. In this paper, the development and performance evaluation of a novel intelligent visual servo controller for a robot manipulator using neural network Reinforcement Learning is presented. By implementing machine learning techniques into the vision based control scheme, the robot is enabled to improve its performance online and to adapt to the changing conditions in the environment. Two different temporal difference algorithms (Q-learning and SARSA) coupled with neural networks are developed and tested through different visual control scenarios. A database of representative learning samples is employed so as to speed up the convergence of the neural network and real-time learning of robot behavior. Moreover, the visual servoing task is divided into two steps in order to ensure the visibility of the features: in the first step centering behavior of the robot is conducted using neural network Reinforcement Learning controller, while the second step involves switching control between the traditional Image Based Visual Servoing and the neural network Reinforcement Learning for enabling approaching behavior of the manipulator. The correction in robot motion is achieved with the definition of the areas of interest for the image features independently in both control steps. Various simulations are developed in order to present the robustness of the developed system regarding calibration error, modeling error, and image noise. In addition, a comparison with the traditional Image Based Visual Servoing is presented. Real world experiments on a robot manipulator with the low cost vision system demonstrate the effectiveness of the proposed approach.",
publisher = "Pergamon-Elsevier Science Ltd, Oxford",
journal = "Expert Systems With Applications",
title = "Neural network Reinforcement Learning for visual control of robot manipulators",
pages = "1736-1721",
number = "5",
volume = "40",
doi = "10.1016/j.eswa.2012.09.010"
}

Miljković, Z., Mitić, M., Lazarević, M.,& Babić, B.. (2013). Neural network Reinforcement Learning for visual control of robot manipulators. in Expert Systems With Applications
Pergamon-Elsevier Science Ltd, Oxford., 40(5), 1721-1736.
https://doi.org/10.1016/j.eswa.2012.09.010

Miljković Z, Mitić M, Lazarević M, Babić B. Neural network Reinforcement Learning for visual control of robot manipulators. in Expert Systems With Applications. 2013;40(5):1721-1736.
doi:10.1016/j.eswa.2012.09.010 .

Miljković, Zoran, Mitić, Marko, Lazarević, Mihailo, Babić, Bojan, "Neural network Reinforcement Learning for visual control of robot manipulators" in Expert Systems With Applications, 40, no. 5 (2013):1721-1736,
https://doi.org/10.1016/j.eswa.2012.09.010 . .

TY  - CONF
AU  - Vuković, Najdan
AU  - Miljković, Zoran
AU  - Mitić, Marko
AU  - Babić, Bojan
AU  - Lazarević, Ivan
PY  - 2012
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4627
AB  - This paper presents a hybrid control algorithm for Automated Guided Vehicle (AGV) consisting of two independent control loops: Position Based Control (PBC) for global navigation within manufacturing environment and Image Based Visual Servoing (IBVS) for fine motions needed for accurate steering towards loading/unloading point. The proposed hybrid control separates the initial transportation task into global navigation towards the goal point, and fine motion from the goal point to the loading/unloading point. In this manner, the need for artificial landmarks or accurate map of the environment is bypassed. Initial experimental results show the usefulness of the proposed approach.
PB  - University of Novi Sad - Faculty of Technical Sciences
C3  - Proceedings of the 11th International Scientific Conference MMA 2012 – Advanced Production Technologies, Novi Sad, 20-21 September, 2012
T1  - Autonomous Navigation of Automated Guided Vehicle Using Monocular Camera
EP  - 304
SP  - 301
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4627
ER  - 

@conference{
author = "Vuković, Najdan and Miljković, Zoran and Mitić, Marko and Babić, Bojan and Lazarević, Ivan",
year = "2012",
abstract = "This paper presents a hybrid control algorithm for Automated Guided Vehicle (AGV) consisting of two independent control loops: Position Based Control (PBC) for global navigation within manufacturing environment and Image Based Visual Servoing (IBVS) for fine motions needed for accurate steering towards loading/unloading point. The proposed hybrid control separates the initial transportation task into global navigation towards the goal point, and fine motion from the goal point to the loading/unloading point. In this manner, the need for artificial landmarks or accurate map of the environment is bypassed. Initial experimental results show the usefulness of the proposed approach.",
publisher = "University of Novi Sad - Faculty of Technical Sciences",
journal = "Proceedings of the 11th International Scientific Conference MMA 2012 – Advanced Production Technologies, Novi Sad, 20-21 September, 2012",
title = "Autonomous Navigation of Automated Guided Vehicle Using Monocular Camera",
pages = "304-301",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4627"
}

Vuković, N., Miljković, Z., Mitić, M., Babić, B.,& Lazarević, I.. (2012). Autonomous Navigation of Automated Guided Vehicle Using Monocular Camera. in Proceedings of the 11th International Scientific Conference MMA 2012 – Advanced Production Technologies, Novi Sad, 20-21 September, 2012
University of Novi Sad - Faculty of Technical Sciences., 301-304.
https://hdl.handle.net/21.15107/rcub_machinery_4627

Vuković N, Miljković Z, Mitić M, Babić B, Lazarević I. Autonomous Navigation of Automated Guided Vehicle Using Monocular Camera. in Proceedings of the 11th International Scientific Conference MMA 2012 – Advanced Production Technologies, Novi Sad, 20-21 September, 2012. 2012;:301-304.
https://hdl.handle.net/21.15107/rcub_machinery_4627 .

Vuković, Najdan, Miljković, Zoran, Mitić, Marko, Babić, Bojan, Lazarević, Ivan, "Autonomous Navigation of Automated Guided Vehicle Using Monocular Camera" in Proceedings of the 11th International Scientific Conference MMA 2012 – Advanced Production Technologies, Novi Sad, 20-21 September, 2012 (2012):301-304,
https://hdl.handle.net/21.15107/rcub_machinery_4627 .

TY  - CONF
AU  - Babić, Bojan
AU  - Miljković, Zoran
AU  - Bugarić, Uglješa
AU  - Bojović, Božica
AU  - Vuković, Najdan
AU  - Mitić, Marko
AU  - Petrović, Milica
PY  - 2012
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4564
AB  - У раду је представљен део резултата који су настали током прве године истраживања на пројекту „Иновативни приступ у примени интелигентних технолошких система за производњу делова од лима заснован на еколошким принципима“ (евид. бр. ТР-35004) Министарства просвете и науке Републике Србије. Пројектним активностима су обухваћена два основна правца истраживања: испитивање трења у микро подручју применом метода скенирајуће микроскопије и развој алгоритама за управљање интелигентних робота, са акцентом на примени еколошких принципа који подразумевају уштеду енергије, материјала и средстава за подмазивање. Приказани резултати су укључени у предавања и лабораторијске вежбе на предметима Катедре за производно машинство, а њихова применљивост верификована је и кроз сарадњу са корисницима из домаће индустрије, ФМП д.о.о. из Београда и OPTIX д.о.о. из Земуна.
AB  - This paper presents a part of results conducted within the project „An innovative ecologically based approach to implementation of intelligent manufacturing systems for production of sheet metal parts“(TR35004), supported by the Serbian Government - the Ministry of Education and Science. The two primary areas of research covered by the project activities are: an examination of friction in micro area by using scanning microscopy method and development of algorithms for intelligent robots control, prioritizing ecological principles of energy, material, and lubricant saving. The presented results are included in lectures and laboratory exercises at the Production Engineering Department courses and verified through the collaboration with participants from the domestic industry, FMP d.o.o. Belgrade and OPTIX d.о.о. Zemun.
PB  - JUPITER Asocijacija, Univerzitet u Beogradu - Mašinski fakultet
C3  - 38th JUPITER Conference : Proceedings, Beograd, maj 2012
T1  - Примена интелигентних технолошких система за производњу делова од лима заснована на еколошким принципима – преглед резултата истраживања на пројекту ТР-35004
T1  - Apllication of the Ecologically Based Approaches to Implementation of Intelligent Manufacturing Systems for Production of Sheet Metal Parts – Overview of Research Results within the Project TR-35004
EP  - UR.75
SP  - UR.67
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4564
ER  - 

@conference{
author = "Babić, Bojan and Miljković, Zoran and Bugarić, Uglješa and Bojović, Božica and Vuković, Najdan and Mitić, Marko and Petrović, Milica",
year = "2012",
abstract = "У раду је представљен део резултата који су настали током прве године истраживања на пројекту „Иновативни приступ у примени интелигентних технолошких система за производњу делова од лима заснован на еколошким принципима“ (евид. бр. ТР-35004) Министарства просвете и науке Републике Србије. Пројектним активностима су обухваћена два основна правца истраживања: испитивање трења у микро подручју применом метода скенирајуће микроскопије и развој алгоритама за управљање интелигентних робота, са акцентом на примени еколошких принципа који подразумевају уштеду енергије, материјала и средстава за подмазивање. Приказани резултати су укључени у предавања и лабораторијске вежбе на предметима Катедре за производно машинство, а њихова применљивост верификована је и кроз сарадњу са корисницима из домаће индустрије, ФМП д.о.о. из Београда и OPTIX д.о.о. из Земуна., This paper presents a part of results conducted within the project „An innovative ecologically based approach to implementation of intelligent manufacturing systems for production of sheet metal parts“(TR35004), supported by the Serbian Government - the Ministry of Education and Science. The two primary areas of research covered by the project activities are: an examination of friction in micro area by using scanning microscopy method and development of algorithms for intelligent robots control, prioritizing ecological principles of energy, material, and lubricant saving. The presented results are included in lectures and laboratory exercises at the Production Engineering Department courses and verified through the collaboration with participants from the domestic industry, FMP d.o.o. Belgrade and OPTIX d.о.о. Zemun.",
publisher = "JUPITER Asocijacija, Univerzitet u Beogradu - Mašinski fakultet",
journal = "38th JUPITER Conference : Proceedings, Beograd, maj 2012",
title = "Примена интелигентних технолошких система за производњу делова од лима заснована на еколошким принципима – преглед резултата истраживања на пројекту ТР-35004, Apllication of the Ecologically Based Approaches to Implementation of Intelligent Manufacturing Systems for Production of Sheet Metal Parts – Overview of Research Results within the Project TR-35004",
pages = "UR.75-UR.67",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4564"
}

Babić, B., Miljković, Z., Bugarić, U., Bojović, B., Vuković, N., Mitić, M.,& Petrović, M.. (2012). Примена интелигентних технолошких система за производњу делова од лима заснована на еколошким принципима – преглед резултата истраживања на пројекту ТР-35004. in 38th JUPITER Conference : Proceedings, Beograd, maj 2012
JUPITER Asocijacija, Univerzitet u Beogradu - Mašinski fakultet., UR.67-UR.75.
https://hdl.handle.net/21.15107/rcub_machinery_4564

Babić B, Miljković Z, Bugarić U, Bojović B, Vuković N, Mitić M, Petrović M. Примена интелигентних технолошких система за производњу делова од лима заснована на еколошким принципима – преглед резултата истраживања на пројекту ТР-35004. in 38th JUPITER Conference : Proceedings, Beograd, maj 2012. 2012;:UR.67-UR.75.
https://hdl.handle.net/21.15107/rcub_machinery_4564 .

Babić, Bojan, Miljković, Zoran, Bugarić, Uglješa, Bojović, Božica, Vuković, Najdan, Mitić, Marko, Petrović, Milica, "Примена интелигентних технолошких система за производњу делова од лима заснована на еколошким принципима – преглед резултата истраживања на пројекту ТР-35004" in 38th JUPITER Conference : Proceedings, Beograd, maj 2012 (2012):UR.67-UR.75,
https://hdl.handle.net/21.15107/rcub_machinery_4564 .

TY  - GEN
AU  - Mitić, Marko
AU  - Miljković, Zoran
AU  - Vuković, Najdan
AU  - Babić, Bojan
PY  - 2012
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4734
AB  - Tehničko rešenje - nova metoda (M85) odnosi se na rešavanje problema upravljanja inteligentnog mobilnog robota primenom empirijske upravljačke teorije na bazi mašinskog učenja demonstracijom i homografije dobijene od nekalibrisane kamere, a razvijana je u projektu TR-35004 MPNiTR Vlade Republike Srbije. Novi algoritam empirijskog upravljanja obuhvata dve nezavisne faze: "off-line" (odnosi se na obučavanje ukupno 18 različitih arhitektura veštačkih neuronskih mreža korišćenjem 6 najzastupljenijih algoritama učenja) i "on-line" koja se odnosi na zadatak vizuelnog navođenja mobilnog robota u laboratorijskom modelu tehnološkog okruženja. Eksperiment obuhvata implementaciju razvijenog empirijskog upravljačkog sistema na mobilnom robotu Khepera II (sa kamerom KheCMUCam i hvatačem KheGrip) u domenu vizuelnog upravljanja u okviru tehnološkog zadatka pristupanja radnom predmetu.
T2  - Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер
T1  - Empirijsko upravljanje inteligentnog mobilnog robota na bazi mašinskog učenja demonstracijom i homografije dobijene od nekalibrisane kamere
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4734
ER  - 

@misc{
author = "Mitić, Marko and Miljković, Zoran and Vuković, Najdan and Babić, Bojan",
year = "2012",
abstract = "Tehničko rešenje - nova metoda (M85) odnosi se na rešavanje problema upravljanja inteligentnog mobilnog robota primenom empirijske upravljačke teorije na bazi mašinskog učenja demonstracijom i homografije dobijene od nekalibrisane kamere, a razvijana je u projektu TR-35004 MPNiTR Vlade Republike Srbije. Novi algoritam empirijskog upravljanja obuhvata dve nezavisne faze: "off-line" (odnosi se na obučavanje ukupno 18 različitih arhitektura veštačkih neuronskih mreža korišćenjem 6 najzastupljenijih algoritama učenja) i "on-line" koja se odnosi na zadatak vizuelnog navođenja mobilnog robota u laboratorijskom modelu tehnološkog okruženja. Eksperiment obuhvata implementaciju razvijenog empirijskog upravljačkog sistema na mobilnom robotu Khepera II (sa kamerom KheCMUCam i hvatačem KheGrip) u domenu vizuelnog upravljanja u okviru tehnološkog zadatka pristupanja radnom predmetu.",
journal = "Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер",
title = "Empirijsko upravljanje inteligentnog mobilnog robota na bazi mašinskog učenja demonstracijom i homografije dobijene od nekalibrisane kamere",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4734"
}

Mitić, M., Miljković, Z., Vuković, N.,& Babić, B.. (2012). Empirijsko upravljanje inteligentnog mobilnog robota na bazi mašinskog učenja demonstracijom i homografije dobijene od nekalibrisane kamere. in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер.
https://hdl.handle.net/21.15107/rcub_machinery_4734

Mitić M, Miljković Z, Vuković N, Babić B. Empirijsko upravljanje inteligentnog mobilnog robota na bazi mašinskog učenja demonstracijom i homografije dobijene od nekalibrisane kamere. in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер. 2012;.
https://hdl.handle.net/21.15107/rcub_machinery_4734 .

Mitić, Marko, Miljković, Zoran, Vuković, Najdan, Babić, Bojan, "Empirijsko upravljanje inteligentnog mobilnog robota na bazi mašinskog učenja demonstracijom i homografije dobijene od nekalibrisane kamere" in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер (2012),
https://hdl.handle.net/21.15107/rcub_machinery_4734 .