Research publications

Browse through all publications from the Institute of Global Health Innovation, which our Patient Safety Research Collaboration is part of. This feed includes reports and research papers from our Centre. 

Citation

BibTex format

@article{Bai:2022:10.1109/TMRB.2021.3127366,
author = {Bai, W and Cursi, F and Guo, X and Huang, B and Lo, B and Yang, GZ and Yeatman, EM},
doi = {10.1109/TMRB.2021.3127366},
journal = {IEEE Transactions on Medical Robotics and Bionics},
pages = {339--342},
title = {Task-Based LSTM Kinematic Modeling for a Tendon-Driven Flexible Surgical Robot},
url = {http://dx.doi.org/10.1109/TMRB.2021.3127366},
volume = {4},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Tendon-driven flexible surgical robots are normally suffering from the inaccurate modeling and imprecise motion control problems due to the nonlinearities of tendon transmission. Learning-based approaches are experimental data-driven with uncertainties modeled empirically, which can be adopted to improve the inevitable issues. This work proposes a LSTM-based kinematic modeling approach with task-based data for a flexible tendon-driven surgical robot to improve the control accuracy. Real experiments demonstrated the effectiveness and superiority of the proposed learned model when completing path following tasks, especially compared to the traditional modeling.
AU  - Bai,W
AU  - Cursi,F
AU  - Guo,X
AU  - Huang,B
AU  - Lo,B
AU  - Yang,GZ
AU  - Yeatman,EM
DO  - 10.1109/TMRB.2021.3127366
EP  - 342
PY  - 2022///
SP  - 339
TI  - Task-Based LSTM Kinematic Modeling for a Tendon-Driven Flexible Surgical Robot
T2  - IEEE Transactions on Medical Robotics and Bionics
UR  - http://dx.doi.org/10.1109/TMRB.2021.3127366
VL  - 4
ER  -
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