Imperial College London
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ProfessorEricYeatman

Faculty of Engineering, Department of Electrical and Electronic Engineering

Head of Department of Electrical and Electronic Engineering
 
 
 
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Contact

 

+44 (0)20 7594 6204e.yeatman CV

 
 
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Assistant

 

Ms Anna McCormick +44 (0)20 7594 6189

 
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Location

 

610aElectrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Xiao:2023:10.1109/IECON51785.2023.10312186,
author = {Xiao, B and Hong, W and Wang, Z and Lo, FPW and Wang, Z and Yu, Z and Chen, S and Liu, Z and Vaidyanathan, R and Yeatman, EM},
doi = {10.1109/IECON51785.2023.10312186},
title = {Learning-Based Inverse Kinematics Identification of the Tendon-Driven Robotic Manipulator for Minimally Invasive Surgery},
url = {http://dx.doi.org/10.1109/IECON51785.2023.10312186},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - It is well-known that the tendon-driven robotic manipulator plays an important role in robotic-assisted minimally invasive surgery (MIS). However, due to the intrinsic nonlinearities, uncertainties, slack and hysteresis introduced by the tendon-driven actuation, the tendon-driven robotic manipulator is difficult to model and control when compared with the traditional actuation styles. To serve the modeling purpose, in this paper, the deep-learning-based intelligent modeling of inverse kinematics in the snake-like tendon-driven surgical instrument is presented. In the proposed approach the Deep Recurrent Neural Network (DRNN) with Long Short-Term Memory (LSTM) architecture is adopted to memorize and identify the nonlinear inverse kinematics of the tendon-driven surgical instrument through the history of the motor and tip positions. To collect highly reliable data to train the DRNN, the experiment to generate training data is carefully designed with the consideration of the stainless tendon characters and motor limitations. During the designed controller movements, the kinematics data is obtained by recording the motor positions and the tip positions. Besides, it is noticed that there are correlations of the sequential data samples, which could significantly reduce the modeling accuracy. To remove the correlations and improve the modeling performance, the correlations of the sequential data samples are removed by modifying the training processes. Modeling results and detailed discussions verified the effectiveness of the proposed approach.
AU  - Xiao,B
AU  - Hong,W
AU  - Wang,Z
AU  - Lo,FPW
AU  - Wang,Z
AU  - Yu,Z
AU  - Chen,S
AU  - Liu,Z
AU  - Vaidyanathan,R
AU  - Yeatman,EM
DO  - 10.1109/IECON51785.2023.10312186
PY  - 2023///
SN  - 2162-4704
TI  - Learning-Based Inverse Kinematics Identification of the Tendon-Driven Robotic Manipulator for Minimally Invasive Surgery
UR  - http://dx.doi.org/10.1109/IECON51785.2023.10312186
ER  -
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