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

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

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

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

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

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

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

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

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

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