TY - CONF A1 - Haas, Lukas A1 - Wölfle, Roland A1 - Kuba, Matthias A1 - Zeh, Thomas T1 - Optimisation of a Delta Robot Using Machine Learning T2 - 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), N2 - 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 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. Y1 - 2023 UR - https://opus4.kobv.de/opus4-hs-kempten/frontdoor/index/index/docId/2046 SN - 979-8-3503-2297-2 SN - 979-8-3503-2298-9 VL - 2023 PB - IEEE CY - Piscataway ER -