@article{Cursi:2022:10.1109/LRA.2022.3187519,
author = {Cursi, F and Bai, W and Yeatman, EM and Kormushev, P},
doi = {10.1109/LRA.2022.3187519},
journal = {IEEE Robotics and Automation Letters},
pages = {7958--7965},
title = {Model learning with backlash compensation for a tendon-driven surgical Robot},
url = {http://dx.doi.org/10.1109/LRA.2022.3187519},
volume = {7},
year = {2022}
}

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TY  - JOUR
AB  - 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.
AU  - Cursi,F
AU  - Bai,W
AU  - Yeatman,EM
AU  - Kormushev,P
DO  - 10.1109/LRA.2022.3187519
EP  - 7965
PY  - 2022///
SN  - 2377-3766
SP  - 7958
TI  - Model learning with backlash compensation for a tendon-driven surgical Robot
T2  - IEEE Robotics and Automation Letters
UR  - http://dx.doi.org/10.1109/LRA.2022.3187519
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000838441200023&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - http://hdl.handle.net/10044/1/102818
VL  - 7
ER  - 

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