Model learning with backlash compensation for a tendon-driven surgical Robot
File(s)
Author(s)
Cursi, Francesco
Bai, Weibang
Yeatman, Eric M
Kormushev, Petar
Type
Journal Article
Abstract
Robots for minimally invasive surgery are becoming more and more complex, due to miniaturization and flexibility requirements. The vast majority of surgical robots are tendon-driven and this, along with the complex design, causes high nonlinearities in the system which are difficult to model analytically. In this work we analyse how incorporating a backlash model and compensation can improve model learning and control. We combine a backlash compensation technique and a Feedforward Artificial Neural Network (ANN) with differential relationships to learn the kinematics at position and velocity level of highly articulated tendon-driven robots. Experimental results show that the proposed backlash compensation is effective in reducing nonlinearities in the system, that compensating for backlash improves model learning and control, and that our proposed ANN outperforms traditional ANN in terms of path tracking accuracy.
Date Issued
2022-07-01
Date Acceptance
2022-06-22
Citation
IEEE Robotics and Automation Letters, 2022, 7 (3), pp.7958-7965
ISSN
2377-3766
Publisher
Institute of Electrical and Electronics Engineers
Start Page
7958
End Page
7965
Journal / Book Title
IEEE Robotics and Automation Letters
Volume
7
Issue
3
Copyright Statement
© 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
License URL
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000838441200023&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
Science & Technology
Technology
Robotics
Model learning
backlash compensation
tendon-driven robots
minimally invasive surgery
CONTINUUM ROBOTS
Publication Status
Published
Date Publish Online
2022-06-30