Achieving a sub-micrometer surgical robot end-effector via hybrid sensing

Overview

When operating with a tool within the human body in the context of a medical procedure, it is crucial to be able to keep track of the pose of the tool. This project will develop a hybrid approach to end effector pose estimation by combing optical tracking with other sensor inputs (e.g. force, sound, acoustic emissions) to compliment and improve tracking accuracy with applications towards orthopaedic surgical robots. This project is part of a broader collaboration with industry partner Stryker.

Research activities

• Understanding the requirement of the problem and conducting an extensive review of the literature on pose tracking in the medical context.
• Exploring techniques and algorithms for sensor fusion.
• Setting up experimental platforms with robotic tools to validate algorithms.
• Communicating results at scientific venues.
• Working closely with the industry partner to integrate developed solutions.

Outcomes

• Prototype algorithms and methods for pose tracking in the surgical context.
• Demonstrators showcasing improvements in pose estimation, on robot platforms.

Skills and experience

• Strong background in engineering (mechanical, electrical, medical, design, control, mechatronics), or robotics.
• Experience with processing sensor data.
• Knowledge of machine learning and proficiency in programming.
• A master’s degree or a bachelor’s degree with excellent academic record.
• Must be interested in setting up experimental platforms and testing with hardware.
• Ability to communicate to people of various backgrounds - engineers, medical professionals, technicians.
• Excellent oral and written communication skills in English.

Scholarships

You may be eligible to apply for a research scholarship.

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Keywords

• robot
• surgery
• machine learning
• sensors
• design
• medical

Contact

ei.pickering@qut.edu.au