| EPSRC Reference: |
EP/R026084/1 |
| Title: |
Robotics and Artificial Intelligence for Nuclear (RAIN) |
| Principal Investigator: |
Lennox, Professor B |
| Other Investigators: |
| Brown, Professor G |
Watson, Dr SA |
Scott, Professor TB |
| Hawes, Professor N |
Furber, Professor S B |
Veres, Professor SM |
| Dixon, Professor C |
Lujan, Professor M |
Herrmann, Professor G |
| Buckingham, Dr R |
Weightman, Professor A |
Payton, Dr O D |
| Richards, Professor A |
Richardson, Dr TO |
Axinte, Professor DA |
| Da Via, Professor C |
Watts, Professor S |
Fisher, Professor M |
| Joyce, Professor MJ |
Dennis, Dr L |
Newman, Professor PM |
| Fallon, Professor M |
Havoutis, Professor I |
Carrasco, Dr J |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Electrical and Electronic Engineering |
| Organisation: |
University of Manchester, The |
| Scheme: |
Standard Research |
| Starts: |
02 October 2017 |
Ends: |
31 March 2022 |
Value (£): |
12,807,912
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| EPSRC Research Topic Classifications: |
| Artificial Intelligence |
Computer Sys. & Architecture |
| Fundamentals of Computing |
Human-Computer Interactions |
| Robotics & Autonomy |
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| EPSRC Industrial Sector Classifications: |
| Aerospace, Defence and Marine |
Environment |
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| Related Grants: |
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| Panel History: |
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Summary on Grant Application Form |
The nuclear industry has some of the most extreme environments in the world, with radiation levels and other hazards frequently restricting human access to facilities. Even when human entry is possible, the risks can be significant and very low levels of productivity. To date, robotic systems have had limited impact on the nuclear industry, but it is clear that they offer considerable opportunities for improved productivity and significantly reduced human risk. The nuclear industry has a vast array of highly complex and diverse challenges that span the entire industry: decommissioning and waste management, Plant Life Extension (PLEX), Nuclear New Build (NNB), small modular reactors (SMRs) and fusion.
Whilst the challenges across the nuclear industry are varied, they share many similarities that relate to the extreme conditions that are present. Vitally these similarities also translate across into other environments, such as space, oil and gas and mining, all of which, for example, have challenges associated with radiation (high energy cosmic rays in space and the presence of naturally occurring radioactive materials (NORM) in mining and oil and gas). Major hazards associated with the nuclear industry include radiation; storage media (for example water, air, vacuum); lack of utilities (such as lighting, power or communications); restricted access; unstructured environments.
These hazards mean that some challenges are currently intractable in the absence of solutions that will rely on future capabilities in Robotics and Artificial Intelligence (RAI). Reliable robotic systems are not just essential for future operations in the nuclear industry, but they also offer the potential to transform the industry globally. In decommissioning, robots will be required to characterise facilities (e.g. map dose rates, generate topographical maps and identify materials), inspect vessels and infrastructure, move, manipulate, cut, sort and segregate waste and assist operations staff. To support the life extension of existing nuclear power plants, robotic systems will be required to inspect and assess the integrity and condition of equipment and facilities and might even be used to implement urgent repairs in hard to reach areas of the plant. Similar systems will be required in NNB, fusion reactors and SMRs.
Furthermore, it is essential that past mistakes in the design of nuclear facilities, which makes the deployment of robotic systems highly challenging, do not perpetuate into future builds. Even newly constructed facilities such as CERN, which now has many areas that are inaccessible to humans because of high radioactive dose rates, has been designed for human, rather than robotic intervention. Another major challenge that RAIN will grapple with is the use of digital technologies within the nuclear sector. Virtual and Augmented Reality, AI and machine learning have arrived but the nuclear sector is poorly positioned to understand and use these rapidly emerging technologies.
RAIN will deliver the necessary step changes in fundamental robotics science and establish the pathways to impact that will enable the creation of a research and innovation ecosystem with the capability to lead the world in nuclear robotics. While our centre of gravity is around nuclear we have a keen focus on applications and exploitation in a much wider range of challenging environments.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.man.ac.uk |