TY  - JOUR
AU  - Pawlowski, Adam
AU  - Romaniuk, Slawomir
AU  - Kulesza, Zbigniew
AU  - Petrović, Milica
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4920
AB  - Nowadays, most robotic systems perform their tasks in an environment that is generally known. Thus, robot’s trajectory can be planned in advance depending on a given task. However, as a part of modern manufacturing systems which are faced with the requirements to produce high product variety, mobile robots should be flexible to adapt to changing and diverse environments and needs. In such scenarios, a modification of the task or a change in the environment, forces the operator to modify robot’s trajectory. Such modification is usually expensive and time-consuming, as experienced engineers must be involved to program robot’s movements. The current paper presents a solution to this problem by simplifying the process of teaching the robot a new trajectory. The proposed method generates a trajectory based on an initial raw demonstration of its shape. The new trajectory is generated in such a way that the errors between the actual and target end positions and orientations of the robot are minimized. To minimize those errors, the grey wolf optimization (GWO) algorithm is applied. The proposed approach is demonstrated for a two-wheeled mobile robot. Simulation and experimental results confirm high accuracy of generated trajectories.
PB  - Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU)
T2  - IAES International Journal of Robotics and Automation (IJRA)
T1  - Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm
EP  - 277
IS  - 4
SP  - 263
VL  - 11
DO  - 10.11591/ijra.v11i4.pp263-277
ER  - 

@article{
author = "Pawlowski, Adam and Romaniuk, Slawomir and Kulesza, Zbigniew and Petrović, Milica",
year = "2022",
abstract = "Nowadays, most robotic systems perform their tasks in an environment that is generally known. Thus, robot’s trajectory can be planned in advance depending on a given task. However, as a part of modern manufacturing systems which are faced with the requirements to produce high product variety, mobile robots should be flexible to adapt to changing and diverse environments and needs. In such scenarios, a modification of the task or a change in the environment, forces the operator to modify robot’s trajectory. Such modification is usually expensive and time-consuming, as experienced engineers must be involved to program robot’s movements. The current paper presents a solution to this problem by simplifying the process of teaching the robot a new trajectory. The proposed method generates a trajectory based on an initial raw demonstration of its shape. The new trajectory is generated in such a way that the errors between the actual and target end positions and orientations of the robot are minimized. To minimize those errors, the grey wolf optimization (GWO) algorithm is applied. The proposed approach is demonstrated for a two-wheeled mobile robot. Simulation and experimental results confirm high accuracy of generated trajectories.",
publisher = "Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU)",
journal = "IAES International Journal of Robotics and Automation (IJRA)",
title = "Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm",
pages = "277-263",
number = "4",
volume = "11",
doi = "10.11591/ijra.v11i4.pp263-277"
}

Pawlowski, A., Romaniuk, S., Kulesza, Z.,& Petrović, M.. (2022). Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm. in IAES International Journal of Robotics and Automation (IJRA)
Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU)., 11(4), 263-277.
https://doi.org/10.11591/ijra.v11i4.pp263-277

Pawlowski A, Romaniuk S, Kulesza Z, Petrović M. Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm. in IAES International Journal of Robotics and Automation (IJRA). 2022;11(4):263-277.
doi:10.11591/ijra.v11i4.pp263-277 .

Pawlowski, Adam, Romaniuk, Slawomir, Kulesza, Zbigniew, Petrović, Milica, "Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm" in IAES International Journal of Robotics and Automation (IJRA), 11, no. 4 (2022):263-277,
https://doi.org/10.11591/ijra.v11i4.pp263-277 . .

TY  - JOUR
AU  - Petrović, Milica
AU  - Ciezkowski, Maciej
AU  - Romaniuk, Slawomir
AU  - Wolniakowski, Adam
AU  - Miljković, Zoran
PY  - 2021
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3532
AB  - Positioning systems based on the lateration method utilize distance measurements and the knowledge of the location of the beacons to estimate the position of the target object. Although most of the global positioning techniques rely on beacons whose locations are known a priori, miscellaneous factors and disturbances such as obstacles, reflections, signal propagation speed, the orientation of antennas, measurement offsets of the beacons hardware, electromagnetic noise, or delays can affect the measurement accuracy. In this paper, we propose a novel hybrid calibration method based on Neural Networks (NN) and Apparent Beacon Position Estimation (ABPE) to improve the accuracy of a lateration positioning system. The main idea of the proposed method is to use a two-step position correction pipeline that first performs the ABPE step to estimate the perceived positions of the beacons that are used in the standard position estimation algorithm and then corrects these initial estimates by filtering them with a multi-layer feed-forward neural network in the second step. In order to find an optimal neural network, 16 NN architectures with 10 learning algorithms and 12 different activation functions for hidden layers were implemented and tested in the MATLAB environment. The best training outcomes for NNs were then employed in two real-world indoor scenarios: without and with obstacles. With the aim to validate the proposed methodology in a scenario where a fast set-up of the system is desired, we tested eight different uniform sampling patterns to establish the influence of the number of the training samples on the accuracy of the system. The experimental results show that the proposed hybrid NN-ABPE method can achieve a high level of accuracy even in scenarios when a small number of calibration reference points are measured.
PB  - MDPI, Basel
T2  - Sensors
T1  - A Novel Hybrid NN-ABPE-Based Calibration Method for Improving Accuracy of Lateration Positioning System
IS  - 24
VL  - 21
DO  - 10.3390/s21248204
ER  - 

@article{
author = "Petrović, Milica and Ciezkowski, Maciej and Romaniuk, Slawomir and Wolniakowski, Adam and Miljković, Zoran",
year = "2021",
abstract = "Positioning systems based on the lateration method utilize distance measurements and the knowledge of the location of the beacons to estimate the position of the target object. Although most of the global positioning techniques rely on beacons whose locations are known a priori, miscellaneous factors and disturbances such as obstacles, reflections, signal propagation speed, the orientation of antennas, measurement offsets of the beacons hardware, electromagnetic noise, or delays can affect the measurement accuracy. In this paper, we propose a novel hybrid calibration method based on Neural Networks (NN) and Apparent Beacon Position Estimation (ABPE) to improve the accuracy of a lateration positioning system. The main idea of the proposed method is to use a two-step position correction pipeline that first performs the ABPE step to estimate the perceived positions of the beacons that are used in the standard position estimation algorithm and then corrects these initial estimates by filtering them with a multi-layer feed-forward neural network in the second step. In order to find an optimal neural network, 16 NN architectures with 10 learning algorithms and 12 different activation functions for hidden layers were implemented and tested in the MATLAB environment. The best training outcomes for NNs were then employed in two real-world indoor scenarios: without and with obstacles. With the aim to validate the proposed methodology in a scenario where a fast set-up of the system is desired, we tested eight different uniform sampling patterns to establish the influence of the number of the training samples on the accuracy of the system. The experimental results show that the proposed hybrid NN-ABPE method can achieve a high level of accuracy even in scenarios when a small number of calibration reference points are measured.",
publisher = "MDPI, Basel",
journal = "Sensors",
title = "A Novel Hybrid NN-ABPE-Based Calibration Method for Improving Accuracy of Lateration Positioning System",
number = "24",
volume = "21",
doi = "10.3390/s21248204"
}

Petrović, M., Ciezkowski, M., Romaniuk, S., Wolniakowski, A.,& Miljković, Z.. (2021). A Novel Hybrid NN-ABPE-Based Calibration Method for Improving Accuracy of Lateration Positioning System. in Sensors
MDPI, Basel., 21(24).
https://doi.org/10.3390/s21248204

Petrović M, Ciezkowski M, Romaniuk S, Wolniakowski A, Miljković Z. A Novel Hybrid NN-ABPE-Based Calibration Method for Improving Accuracy of Lateration Positioning System. in Sensors. 2021;21(24).
doi:10.3390/s21248204 .

Petrović, Milica, Ciezkowski, Maciej, Romaniuk, Slawomir, Wolniakowski, Adam, Miljković, Zoran, "A Novel Hybrid NN-ABPE-Based Calibration Method for Improving Accuracy of Lateration Positioning System" in Sensors, 21, no. 24 (2021),
https://doi.org/10.3390/s21248204 . .

TY  - CONF
AU  - Petrović, Milica
AU  - Wolniakowski, Adam
AU  - Ciezkowski, M.
AU  - Romaniuk, Slawomir
AU  - Miljković, Zoran
PY  - 2020
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3441
AB  - Mobile robot positioning is a crucial problem in modern industrial autonomous solutions. Lateration Positioning Systems base on the distance measurements to estimate the object's position. These measurements are however often affected by numerous sources of noise: obstacles, multi-pathing, signal propagation speed etc. Effective calibration methods are therefore required to eliminate these errors to achieve precise positioning. In this paper, we present the application of neural networks to improve the accuracy of a UWB lateration system. We present the network architecture and demonstrate how it can be used to alleviate the effects of multi-pathing and environment anisotropy in a real positioning setup. We furthermore compare the efficiency of the neural network with the state-of-the-art calibration methods.
PB  - Institute of Electrical and Electronics Engineers Inc.
C3  - 15th International Conference Mechatronic Systems and Materials, MSM 2020
T1  - Neural network-based calibration for accuracy improvement in lateration positioning system
DO  - 10.1109/MSM49833.2020.9201646
ER  - 

@conference{
author = "Petrović, Milica and Wolniakowski, Adam and Ciezkowski, M. and Romaniuk, Slawomir and Miljković, Zoran",
year = "2020",
abstract = "Mobile robot positioning is a crucial problem in modern industrial autonomous solutions. Lateration Positioning Systems base on the distance measurements to estimate the object's position. These measurements are however often affected by numerous sources of noise: obstacles, multi-pathing, signal propagation speed etc. Effective calibration methods are therefore required to eliminate these errors to achieve precise positioning. In this paper, we present the application of neural networks to improve the accuracy of a UWB lateration system. We present the network architecture and demonstrate how it can be used to alleviate the effects of multi-pathing and environment anisotropy in a real positioning setup. We furthermore compare the efficiency of the neural network with the state-of-the-art calibration methods.",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
journal = "15th International Conference Mechatronic Systems and Materials, MSM 2020",
title = "Neural network-based calibration for accuracy improvement in lateration positioning system",
doi = "10.1109/MSM49833.2020.9201646"
}

Petrović, M., Wolniakowski, A., Ciezkowski, M., Romaniuk, S.,& Miljković, Z.. (2020). Neural network-based calibration for accuracy improvement in lateration positioning system. in 15th International Conference Mechatronic Systems and Materials, MSM 2020
Institute of Electrical and Electronics Engineers Inc...
https://doi.org/10.1109/MSM49833.2020.9201646

Petrović M, Wolniakowski A, Ciezkowski M, Romaniuk S, Miljković Z. Neural network-based calibration for accuracy improvement in lateration positioning system. in 15th International Conference Mechatronic Systems and Materials, MSM 2020. 2020;.
doi:10.1109/MSM49833.2020.9201646 .

Petrović, Milica, Wolniakowski, Adam, Ciezkowski, M., Romaniuk, Slawomir, Miljković, Zoran, "Neural network-based calibration for accuracy improvement in lateration positioning system" in 15th International Conference Mechatronic Systems and Materials, MSM 2020 (2020),
https://doi.org/10.1109/MSM49833.2020.9201646 . .