Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm
Апстракт
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 m...inimized. 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.
Кључне речи:
Differential drive / Grey wolf optimizer / Learning from demonstration / Trajectory optimization / Ultra-wideband systemИзвор:
IAES International Journal of Robotics and Automation (IJRA), 2022, 11, 4, 263-277Издавач:
- Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU)
Финансирање / пројекти:
- Bialystok University of Technology, grant number WZ/WE-IA/4/2020
Колекције
Институција/група
Mašinski fakultetTY - 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 . .