A primary motivation of our research is the monitoring of physical, physiological, and biochemical parameters - in any environment and without activity restriction and behaviour modification - through using miniaturised, wireless Body Sensor Networks (BSN). Key research issues that are currently being addressed include novel sensor designs, ultra-low power microprocessor and wireless platforms, energy scavenging, biocompatibility, system integration and miniaturisation, processing-on-node technologies combined with novel ASIC design, autonomic sensor networks and light-weight communication protocols. Our research is aimed at addressing the future needs of life-long health, wellbeing and healthcare, particularly those related to demographic changes associated with an ageing population and patients with chronic illnesses. This research theme is therefore closely aligned with the IGHI’s vision of providing safe, effective and accessible technologies for both developed and developing countries.

Some of our latest works were exhibited at the 2015 Royal Society Summer Science Exhibition.


Citation

BibTex format

@inproceedings{Guo:2019:10.1109/icra.2019.8794123,
author = {Guo, Y and Sun, M and Lo, FPW and Lo, B},
doi = {10.1109/icra.2019.8794123},
pages = {8740--8746},
publisher = {IEEE},
title = {Visual guidance and automatic control for robotic personalized stent graft manufacturing},
url = {http://dx.doi.org/10.1109/icra.2019.8794123},
year = {2019}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - Personalized stent graft is designed to treat Abdominal Aortic Aneurysms (AAA). Due to the individual difference in arterial structures, stent graft has to be custom made for each AAA patient. Robotic platforms for autonomous personalized stent graft manufacturing have been proposed in recently which rely upon stereo vision systems for coordinating multiple robots for fabricating customized stent grafts. This paper proposes a novel hybrid vision system for real-time visual-sevoing for personalized stent-graft manufacturing. To coordinate the robotic arms, this system is based on projecting a dynamic stereo microscope coordinate system onto a static wide angle view stereo webcam coordinate system. The multiple stereo camera configuration enables accurate localization of the needle in 3D during the sewing process. The scale-invariant feature transform (SIFT) method and color filtering are implemented for stereo matching and feature identifications for object localization. To maintain the clear view of the sewing process, a visual-servoing system is developed for guiding the stereo microscopes for tracking the needle movements. The deep deterministic policy gradient (DDPG) reinforcement learning algorithm is developed for real-time intelligent robotic control. Experimental results have shown that the robotic arm can learn to reach the desired targets autonomously.
AU - Guo,Y
AU - Sun,M
AU - Lo,FPW
AU - Lo,B
DO - 10.1109/icra.2019.8794123
EP - 8746
PB - IEEE
PY - 2019///
SP - 8740
TI - Visual guidance and automatic control for robotic personalized stent graft manufacturing
UR - http://dx.doi.org/10.1109/icra.2019.8794123
UR - https://ieeexplore.ieee.org/document/8794123
UR - http://hdl.handle.net/10044/1/75187
ER -