Towards Long Lasting Robot Operations in Nuclear Facilities
- Develop long lasting robotics capability in nuclear facilities to improve safety and productivity
- Regular onsite visits to the Australian Nuclear Science & Technology Organisation (ANSTO)
- POSTED: September 12, 2024
- CLOSES: Open until filled
- LOCATION: Canberra, Australia
- POSITION: PhD Scholarship
- ORGANISATION: The Australian National University
- SUPERVISOR: Professor Hanna Kurniawati (ANU)
- PhD STIPEND: $40,000 AUD (Tax-Free)
Despite the mentioned potentials of robotics in nuclear processing facilities, their applications in nuclear facilities have been hindered by the detrimental effects of gamma radiation to robots. Most nuclear processing emits gamma radiation, which degrades many components of a robot, from its sensors to its motors. Some robots have been designed for operation in nuclear environment, and hence more immune to gamma radiation. However, existing nuclear-hardened feature in robots relies solely on the hardware, which tend to carry exorbitant price tags compared to even the best commonly used industrial robots. On the other hand, the tremendous advances made in robotics over the past two decades is due to both hardware and software.
This project aims to explore if and how advances in the robotic systems –both hardware and software– could alleviate the negative effects of gamma radiation in robotics for nuclear processing. To this end, the PhD project will focus on:
- Developing a system-based approach to prolong the usefulness of robots in nuclear processing facilities.
Supervised by Prof. Hanna Kurniawati, the PhD student will explore a system-based approach to prolong the use of robots in nuclear processing facilities. Questions to be explored include suitable sensor configurations, including the potential of mounting additional sensors inside and outside the work-cells, and the integrated planning and control of the robot in the presence of deteriorating sensors, sensing, and potentially the robot’s components.
Research Environment
ARIAM Hub is a collaboration between leading academic researchers and experts from the Australian robotics industry supported by the Australian Research Council to deliver research excellence in robotics for asset management. ARIAM spans 3 leading Australian universities: The University of Sydney, Queensland University of Technology (QUT) and Australian National University (ANU). These projects will be part of a collaboration between the Australian Centre for Robotics (ACFR) at the University of Sydney and the Robust Decision-Making Lab at the ANU. You will be supervised Professor Hanna Kurniawati of the Robust Decision-Making Lab within the School of Computing at ANU.
The School of Computing has a strong foundation in computing and information sciences at ANU. We are a transformative centre for research in artificial intelligence and machine learning, computer systems and software, and theoretical foundations of computing. Our mission is motivated by the need to design, drive and sustain strategic activities via five broad focus areas: Computing Foundations, Computational Science, Intelligent Systems, Data Science and Analytics, and the Software Innovation Institute. The PhD candidate will be part of the Intelligent Systems broad focus area, and specifically part of the Robotics group. The ANU Robust Decision-making and Learning Lab is a growing robotics group specialising in robot decision-making. Its members have developed multiple first and award-winning works in robot planning. The lab is part of the ANU Planning and Optimisation group, which is often ranked in the top three Automated Planning group in the world.
The roles advertised here is part of a larger opportunity that will hire and bring together a large group of PhD students and multiple Postdocs.
Offering
A fully funded 3.5-year PhD scholarship covering tuition fees and stipend of $40,000 (tax-free).
How to Apply
To apply, please fill out the form below with the subject line “Towards Long Lasting Robot Operations in Nuclear Facilities” and your name. Include the following:
- CV
- Unofficial transcripts
- Cover letter
Please also include a Google Drive link to a 2-minute selfie video covering the following:
- Your strongest engineering skills
- What do you enjoy most about developing technology
- A description of a project that you’re proud of, or plan to be when completed