In this section

Mechatronics in Medicine

The MIM Lab develops robotic and mechatronics surgical systems for a variety of procedures.

Head of Group

B415C Bessemer Building
South Kensington Campus

+44 (0)20 7594 7046

What we do

The Mechatronics in Medicine Laboratory develops robotic and mechatronics surgical systems for a variety of procedures including neuro, cardiovascular, orthopaedic surgeries, and colonoscopies. Examples include bio-inspired catheters that can navigate along complex paths within the brain (such as EDEN2020), soft robots to explore endoluminal anatomies (such as the colon), and virtual reality solutions to support surgeons during knee replacement surgeries.

Meet the team

Mr Ayhan Aktas
Casual - Student demonstrator - lower rate

Dr Daniel Bautista Salinas
Research Associate

Dr Kaiwen Chen
Research Associate

Mr Zejian Cui
Research Assistant

Mr Connor Daly
Research Postgraduate

Emeritus Professor Brian L Davies FREng
Emeritus Professor

Mr Zhaoyang Jacopo Hu
Research Postgraduate

Dr Hisham M Iqbal
Research Associate

Mr Alex Ranne
Research Postgraduate

Professor Ferdinando M Rodriguez y Baena
Co-Director of Hamlyn Centre, Professor of Medical Robotics

Dr Jialei Shi
Research Associate

Mr Spyridon Souipas
Casual - Other work

Ms Emilia Zari
Research Postgraduate

Citation

BibTex format

@article{Souipas:2024:10.3389/frobt.2024.1365632,
author = {Souipas, S and Nguyen, A and Laws, SG and Davies, B and Rodriguez, y Baena F},
doi = {10.3389/frobt.2024.1365632},
journal = {Frontiers in Robotics and AI},
title = {Real-time active constraint generation and enforcement for surgical tools using 3D detection and localisation network},
url = {http://dx.doi.org/10.3389/frobt.2024.1365632},
volume = {11},
year = {2024}
}

Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Introduction: Collaborative robots, designed to work alongside humans for manipulating end-effectors, greatly benefit from the implementation of active constraints. This process comprises the definition of a boundary, followed by the enforcement of some control algorithm when the robot tooltip interacts with the generated boundary. Contact with the constraint boundary is communicated to the human operator through various potential forms of feedback. In fields like surgical robotics, where patient safety is paramount, implementing active constraints can prevent the robot from interacting with portions of the patient anatomy that shouldn’t be operated on. Despite improvements in orthopaedic surgical robots, however, there exists a gap between bulky systems with haptic feedback capabilities and miniaturised systems that only allow for boundary control, where interaction with the active constraint boundary interrupts robot functions. Generally, active constraint generation relies on optical tracking systems and preoperative imaging techniques.Methods: This paper presents a refined version of the Signature Robot, a three degrees-of-freedom, hands-on collaborative system for orthopaedic surgery. Additionally, it presents a method for generating and enforcing active constraints “on-the-fly” using our previously introduced monocular, RGB, camera-based network, SimPS-Net. The network was deployed in real-time for the purpose of boundary definition. This boundary was subsequently used for constraint enforcement testing. The robot was utilised to test two different active constraints: a safe region and a restricted region.Results: The network success rate, defined as the ratio of correct over total object localisation results, was calculated to be 54.7% ± 5.2%. In the safe region case, haptic feedback resisted tooltip manipulation beyond the active constraint boundary, with a mean distance from the boundary of 2.70 mm ± 0.37 mm and a mean exit d
AU  - Souipas,S
AU  - Nguyen,A
AU  - Laws,SG
AU  - Davies,B
AU  - Rodriguez,y Baena F
DO  - 10.3389/frobt.2024.1365632
PY  - 2024///
SN  - 2296-9144
TI  - Real-time active constraint generation and enforcement for surgical tools using 3D detection and localisation network
T2  - Frontiers in Robotics and AI
UR  - http://dx.doi.org/10.3389/frobt.2024.1365632
UR  - https://www.frontiersin.org/articles/10.3389/frobt.2024.1365632/full
UR  - http://hdl.handle.net/10044/1/110432
VL  - 11
ER  -