Imperial College London
Portrait Picture

Dr Dandan Zhang

Faculty of Engineering, Department of Bioengineering

Lecturer in Artificial Intelligence & Machine Learning
 
 
 
//

Contact

 

d.zhang17 Website

 
 
//

Location

 

402Sir Michael Uren HubWhite City Campus

//

Summary

 

Publications

Citation

BibTex format

@inbook{Payne:2021:10.1007/978-1-0716-0993-4_4,
author = {Payne, CJ and Vyas, K and Bautista-Salinas, D and Zhang, D and Marcus, HJ and Yang, GZ},
booktitle = {Neuromethods},
doi = {10.1007/978-1-0716-0993-4_4},
pages = {63--79},
title = {Shared-control robots},
url = {http://dx.doi.org/10.1007/978-1-0716-0993-4_4},
year = {2021}
}
Download

RIS format (EndNote, RefMan)

TY  - CHAP
AB  - This chapter reviews shared-control robots, a class of robotic device in which the surgeon and the robot simultaneously manipulate the surgical tool together. The shared-control approach seeks to exploit the superior aspects of humans and machines, to enable more precise interventions while ensuring the human surgeon retains executive control. Much of the technology discussed in this chapter is emerging research and many of the described systems have been developed for generic microsurgical interventions. Nonetheless, the broad concepts behind these surgical systems are highly applicable to neurosurgery and particularly to microsurgical procedures. We start by presenting an exemplar of a grounded, shared-control robot: the Steady-Hand system. We then review a series of handheld smart surgical devices, including Micron, a handheld tremor cancellation device. This chapter also presents handheld devices capable of augmenting haptic feedback to surgeons performing delicate neurosurgical tasks, image-guided handheld devices with embedded robotic actuation, and a new generation of handheld microscopic imaging devices for visualizing tumors.
AU  - Payne,CJ
AU  - Vyas,K
AU  - Bautista-Salinas,D
AU  - Zhang,D
AU  - Marcus,HJ
AU  - Yang,GZ
DO  - 10.1007/978-1-0716-0993-4_4
EP  - 79
PY  - 2021///
SP  - 63
TI  - Shared-control robots
T1  - Neuromethods
UR  - http://dx.doi.org/10.1007/978-1-0716-0993-4_4
ER  -
Download