PhD Studentship: Developing a digital twin for validation of packing optimisers used for nuclear decommissioning

4 weeks ago

Leeds, Leeds, United Kingdom University of Leeds Full time

Lead Supervisor's full name & email address

Dr Xiaodong Jia -

Co-supervisor name(s) & email address(s)

Professor Shane Xie -

Dr Mehmet Dogar -

Project summary

When sorting and packing nuclear waste items, the current practice typically involves human operators wearing full PPEs and/or remotely controlled robotic arms. Major constraints of the current practice include short working shift, large support team, low efficiency, long project duration and high cost. The use of autonomous robotic systems is now an accepted future technological trend, as in principle they can overcome all the major constraints of the current practice, by embedding forward planning based on info from modern scanning technologies, by eliminating human operators' exposure to harmful radiations, and by making continuous 24/7 operation possible. Some supervisors of this PhD project were involved in a recently completed feasibility demo project (OptiSort), funded by IUK (Innovate UK) and NDA (Nuclear Decommissioning Authority). While the demo project successfully integrated some state-of-the-art technologies into an autonomous system and showcased its potentials, it also revealed some gaps to be filled before the system can be deployed for real-world applications.

This PhD project helps to fill one of the gaps: a real-time validation system for packing simulations, comprising a robotic packing setup and a digital twin (DT), such that packing simulation models can be plugged in to be tested, verified and corrected. The physical twin is a simple setup (consisting of a robotic arm, flat work surface to pick up well separated objects from, and a packing box) but equipped with necessary machine vision sensors to allow discrepancy detection and correction to be carried out in real-time. The digital twin is built on an existing packing software (DigiDEM) to provide a common virtual packing platform to handles digitised irregular shapes, their packing and mechanical stability, and an API to allow different packing algorithms or heuristics to be tested and validated.

Candidates will have, or be due to obtain, a Master's Degree or equivalent from a reputable university in an appropriate field of Engineering. Exceptional candidates with a First Class Bachelor's Degree in an appropriate field will also be considered.

Subject Area

Artificial Intelligence, Computer Vision, Machine Learning, Software Engineering, Chemical Engineering, Civil Engineering, Mechanical Engineering, Robotics, Geotechnology, Operational Research, Computational Physics

£19,237 per year for 4 years

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