TUDOR tip position control with Neural Networks (Ohne Sprachangabe)

The forward and inverse kinematic model of a multi-flexible-link robot arm for varying payloads are each approximated by using artificial neural networks. The tip position is predicted from the joint angles and strain signals. The strain measurements allow the reaction to changes in the payload. Thus, the kinematic models can be applied in case of varying payloads. The closed loop controller corrects the joint angles at the target pose based on the pose predicted by the forward model and archives an average pose error of less than 3 mm. Timeline: 00:10 Deflection of TUDOR after adding 600g payload 00:25 Tip position control of TUDOR after adding 600g payload 00:42 Relaxation of TUDOR after removing 600g payload 00:57 Tip position control of TUDOR after removing 600g payload References: - Phung, A. S., J. Malzahn, F. Hoffmann und T. Bertram: Data Based Kinematic Model of a Multi-Flexible-Link Robot Arm for Varying Payloads, In IEEE International Conference on Robotics and Biomimetics, Phuket (Thailand),07.-11.12.2011, pp. 1255-1260 Dezember 2011 - Malzahn, J., A. S. Phung, F. Hoffmann und T. Bertram: Vibration Control of a Multi-Flexible-Link Robot Arm under Gravity, In IEEE International Conference on Robotics and Biomimetics, Phuket (Thailand),07.-11.12.2011, pp. 1249-1254 Dezember 2011 For more information on the project please visit: http://www.rst.e-technik.tu-dortmund.de/cms/de/Forschung/Schwerpunkte/Robotik/TUDOR_neu/index.html