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Funded PhD post available - "Advanced vision systems to aid robotic post operational clean out (POCO) of infrastructure" The Project: concerns the development of an intelligent vision system comprising numerous cameras to provide 4π steradians of coverage. This vision system will combine with a machine learning algorithm to provide the user with details of the identity of all the surrounding items and where they are in 3D space. This data, alongside radiological data, will be utilised within a heavy-duty dual arm Robot (KUKA KR180) system capable of sorting and segregating heavy items (such as concrete, rebar, soil etc…) that are placed upon a conveyor belt, and likely to be potentially contaminated by years of use within the nuclear industry. It is anticipated that this vision system could allow enhanced safety and productivity standards, with the sorting and segregation operations to occur while humans are also working in the same area for example. The system will be validated experimentally using both inactive waste and that combined with radiological sources simulating a potentially contaminated environment. Objectives · To design a computer vision system which takes in data from 6 depth cameras and stitches them together to create a 4π steradians “map” of everything in the local area. · Apply an off the shelf machine learning algorithm (such as YOLOv9) to identify all objects in the stitched together image. · Use the data obtained to control a robot and conveyor belt in order that items such as concrete and soil can be scanned and moved to different bins according to the radiological activity and identity of the item. · Testing with human analogues to determine the safety rating of your algorithm. Qualifications and experience Candidates should be highly motivated, well-organised and have a relevant degree at 2.1 minimum in Engineering, Computer Science or closely related area. Previous experience in writing code related to vision system would be beneficial but not essential Funding Supported by the Nuclear Decommissioning Authority (NDA), UKRI/EPSRC and Lancaster University through the SATURN CDT (Skills And Training Underpinning a Renaissance in Nuclear Centre for Doctoral Training), this studentship is available to start from 1st October 2024. For UK applicants the studentship is fully funded for 4 years, covering fees and a maintenance grant (~£23,000) (all tax free). For informal enquiries to learn more about the PhD project, contact Dr Stephen Monk (s.monk@lancaster.ac.uk) and/or Dr David Cheneler (d.cheneler@lancaster.ac.uk).
