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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.show moreshow less

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Metadaten
Author:Lukas HaasORCiDGND, Roland Wölfle, Matthias Kuba, Thomas ZehORCiDGND
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
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