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{Liu:2016:10.1109/LRA.2016.2528292,
author = {Liu, F and Garriga-Casanovas, A and Secoli, R and Baena, FRY},
doi = {10.1109/LRA.2016.2528292},
journal = {IEEE Robotics and Automation Letters},
pages = {601--608},
title = {Fast and adaptive fractal tree-based path planning for programmable bevel tip steerable needles},
url = {http://dx.doi.org/10.1109/LRA.2016.2528292},
volume = {1},
year = {2016}
}

Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Steerable needles are a promising technology for minimally invasive surgery, as they can provide access to difficult to reach locations while avoiding delicate anatomical regions. However, due to the unpredictable tissue deformation associated with needle insertion and the complexity of many surgical scenarios, a real-time path planning algorithm with high update frequency would be advantageous. Real-time path planning for nonholonomic systems is commonly used in a broad variety of fields, ranging from aerospace to submarine navigation. In this letter, we propose to take advantage of the architecture of graphics processing units (GPUs) to apply fractal theory and thus parallelize real-time path planning computation. This novel approach, termed adaptive fractal trees (AFT), allows for the creation of a database of paths covering the entire domain, which are dense, invariant, procedurally produced, adaptable in size, and present a recursive structure. The generated cache of paths can in turn be analyzed in parallel to determine the most suitable path in a fraction of a second. The ability to cope with nonholonomic constraints, as well as constraints in the space of states of any complexity or number, is intrinsic to the AFT approach, rendering it highly versatile. Three-dimensional (3-D) simulations applied to needle steering in neurosurgery show that our approach can successfully compute paths in real-time, enabling complex brain navigation.
AU  - Liu,F
AU  - Garriga-Casanovas,A
AU  - Secoli,R
AU  - Baena,FRY
DO  - 10.1109/LRA.2016.2528292
EP  - 608
PY  - 2016///
SN  - 2377-3766
SP  - 601
TI  - Fast and adaptive fractal tree-based path planning for programmable bevel tip steerable needles
T2  - IEEE Robotics and Automation Letters
UR  - http://dx.doi.org/10.1109/LRA.2016.2528292
UR  - http://hdl.handle.net/10044/1/33050
VL  - 1
ER  -