Neural network enhanced robot tool identification and calibration for bilateral teleoperation

Su, Hang; Yang, Chenguang; Mdeihly, Hussein; Rizzo, Alessandro; Ferrigno, Giancarlo; De Momi, Elena

Abstract

© 2013 IEEE. In teleoperated surgery, the transmission of force feedback from the remote environment to the surgeon at the local site requires the availability of reliable force information in the system. In general, a force sensor is mounted between the slave end-effector and the tool for measuring the interaction forces generated at the remote sites. Such as the acquired force value includes not only the interaction force but also the tool gravity. This paper presents a neural network (NN) enhanced robot tool identification and calibration for bilateral teleoperation. The goal of this experimental study is to implement and validate two different techniques for tool gravity identification using Curve Fitting (CF) and Artificial Neural Networks (ANNs), separately. After tool identification, calibration of multi-axis force sensor based on Singular Value Decomposition (SVD) approach is introduced for alignment of the forces acquired from the force sensor and acquired from the robot. Finally, a bilateral teleoperation experiment is demonstrated using a serial robot (LWR4+, KUKA, Germany) and a haptic manipulator (SIGMA 7, Force Dimension, Switzerland). Results demonstrated that the calibration of the force sensor after identifying tool gravity component by using ANN shows promising performance than using CF. Additionally, the transparency of the system was demonstrated using the force and position tracking between the master and slave manipulators.

Citation

Su, H., Yang, C., Mdeihly, H., Rizzo, A., Ferrigno, G., & De Momi, E. (2019). Neural network enhanced robot tool identification and calibration for bilateral teleoperation. IEEE Access, 7, 122041-122051. https://doi.org/10.1109/ACCESS.2019.2936334

• Journal Article Type: Article
• Acceptance Date: Aug 7, 2019
• Online Publication Date: Aug 29, 2019
• Publication Date: 2019
• Deposit Date: Aug 27, 2019
• Publicly Available Date: Aug 29, 2019
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Pages: 122041-122051
• DOI: https://doi.org/10.1109/ACCESS.2019.2936334
• Keywords: General Engineering; General Materials Science; General Computer Science
• Public URL: https://uwe-repository.worktribe.com/output/2503969