Optimisation of a Delta Robot Using Machine Learning
- In this article, the optimization of the control circuit and path planning of a delta kinematic with the help of machine learning is presented. The described delta kinematic is primarily used for pick-and-place applications in the field of packaging machines. The optimization of the path planning procedure aims to make the workflow more efficent and flexible for commissioning the delta kinematic. By optimizing the control circuit using machine learning, mechanical oscillations and the deviation of the specified path are to be minimized. The possible use of a simulator for training, the prediction quality and the implementation on the robot controller are discussed. Furthermore, the path planning procedure was optimized. For this purpose, an environment was implemented in which a reinforcement learning agent plans the path of the robot between a starting point and a target point in a time-optimized manner, considering interference contours e.g. from the machine. The obtained results show the optimization of the robot by machineIn this article, the optimization of the control circuit and path planning of a delta kinematic with the help of machine learning is presented. The described delta kinematic is primarily used for pick-and-place applications in the field of packaging machines. The optimization of the path planning procedure aims to make the workflow more efficent and flexible for commissioning the delta kinematic. By optimizing the control circuit using machine learning, mechanical oscillations and the deviation of the specified path are to be minimized. The possible use of a simulator for training, the prediction quality and the implementation on the robot controller are discussed. Furthermore, the path planning procedure was optimized. For this purpose, an environment was implemented in which a reinforcement learning agent plans the path of the robot between a starting point and a target point in a time-optimized manner, considering interference contours e.g. from the machine. The obtained results show the optimization of the robot by machine learning with a root mean squared error of the predicted torques of 0.06025 Nm in a prediction time of around 0.125 ms and the possibility of path planning with different criteria.…
Author: | Lukas HaasORCiDGND, Roland Wölfle, Matthias Kuba, Thomas ZehORCiDGND |
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DOI: | https://doi.org/10.1109/ICECCME57830.2023.10252507 |
Identifier: | 979-8-3503-2297-2 OPAC HS OPAC extern |
Identifier: | 979-8-3503-2298-9 OPAC HS OPAC extern |
Parent Title (English): | 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), |
Publisher: | IEEE |
Place of publication: | Piscataway |
Document Type: | conference proceeding (article) |
Conference: | IEEE 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 19-21 July 2023, Tenerife, Canary Islands, Spain |
Language: | English |
Date of Publication (online): | 2023/09/22 |
Year of first Publication: | 2023 |
Volume: | 2023 |
Number of pages: | 6 Seiten |
Institutes: | Fakultät Elektrotechnik |
Research focus: | FSP2: Mobilität |
FSP4: Soziale Innovationen | |
Eigenständige Forschungsfelder | |
Publication Lists: | Kuba, Matthias |
Zeh, Thomas | |
Haas, Lukas | |
Publication reviewed: | begutachtet |
Release Date: | 2024/01/05 |