Imperial College London
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DrBennyLo

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

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Contact

 

+44 (0)20 7594 0806benny.lo Website

 
 
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Location

 

Bessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Chen:2020:10.1109/iros45743.2020.9341383,
author = {Chen, J and Zhang, D and Munawar, A and Zhu, R and Lo, B and Fischer, GS and Yang, G-Z},
doi = {10.1109/iros45743.2020.9341383},
pages = {2943--2949},
publisher = {IEEE},
title = {Supervised semi-autonomous control for surgical robot based on Banoian optimization},
url = {http://dx.doi.org/10.1109/iros45743.2020.9341383},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - The recent development of Robot-Assisted Minimally Invasive Surgery (RAMIS) has brought much benefit to ease the performance of complex Minimally Invasive Surgery (MIS) tasks and lead to more clinical outcomes. Compared to direct master-slave manipulation, semi-autonomous control for the surgical robot can enhance the efficiency of the operation, particularly for repetitive tasks. However, operating in a highly dynamic in-vivo environment is complex. Supervisory control functions should be included to ensure flexibility and safety during the autonomous control phase. This paper presents a haptic rendering interface to enable supervised semi-autonomous control for a surgical robot. Bayesian optimization is used to tune user-specific parameters during the surgical training process. User studies were conducted on a customized simulator for validation. Detailed comparisons are made between with and without the supervised semi-autonomous control mode in terms of the number of clutching events, task completion time, master robot end-effector trajectory and average control speed of the slave robot. The effectiveness of the Bayesian optimization is also evaluated, demonstrating that the optimized parameters can significantly improve users' performance. Results indicate that the proposed control method can reduce the operator's workload and enhance operation efficiency.
AU  - Chen,J
AU  - Zhang,D
AU  - Munawar,A
AU  - Zhu,R
AU  - Lo,B
AU  - Fischer,GS
AU  - Yang,G-Z
DO  - 10.1109/iros45743.2020.9341383
EP  - 2949
PB  - IEEE
PY  - 2020///
SP  - 2943
TI  - Supervised semi-autonomous control for surgical robot based on Banoian optimization
UR  - http://dx.doi.org/10.1109/iros45743.2020.9341383
UR  - https://ieeexplore.ieee.org/document/9341383
UR  - http://hdl.handle.net/10044/1/89093
ER  -
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