We are working in the area of medical and healthcare robotics that focus on ways to help improve the efficiency of surgeons, and in particular in the area of minimally invasive surgery such as arthroscopy.
The use of arthroscopy, where a camera and cutting tool is inserted into a joint such as the knee, is now common place in the developed world. However, it is not yet widely available in the developing world due to a lack of specialised surgeons.
Problem: Currently, surgeons use their own vision to understand the operation before them and their own hands, and feet, to manipulate the camera and their tools. The procedure is difficult to master and takes a long time to learn.
Solution: Our interdisciplinary research team develops the techniques and technology to enable future robotic assistants that will work in tandem with a human surgeon in performing joint arthroscopy. In particular, the research is developing robotic vision systems that are capable of mapping joints in real-time via arthroscopically sourced video streams. Our research team also explores control schemes that allow robots to hold and manipulate both the arthroscope and the surgical tools using robotic vision in the control feedback loop (visual servoing).
The medical robotics researchers within QUT’s robotics and autonomous systems also have an interest in the use of vision-based machine learning techniques to lower the cost of diagnostic testing of blood and retinal images. The team is exploring the use of machine learning techniques while at the same time developing new cost-effective mobile phone-based image acquisition systems.
We welcome industry collaborations in this field. For more information about collaborating with our team, please contact us.
This project aims to develop highly dexterous snake-like tools for both manual and robotic orthopaedic surgeries by applying novel continuum mechanisms to the design and implementation.
Cameras and sensors
The system designed by the interdiscipliary robotics team combines advanced functionalities in pixel design to develop state-of-the-art miniaturised cameras for robotic vision.