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Details of Grant 

EPSRC Reference: EP/R026084/1
Title: Robotics and Artificial Intelligence for Nuclear (RAIN)
Principal Investigator: Lennox, Professor B
Other Investigators:
Dennis, Dr L Lujan, Professor M Herrmann, Dr G
Richardson, Dr TO Newman, Professor PM Brown, Professor G
Watson, Dr SA Richards, Professor A Weightman, Dr A
Payton, Dr O D Scott, Professor TB Carrasco Gomez, Dr J
Axinte, Professor DA Hawes, Professor N Da Viá, Professor C
Furber, Professor S B Fallon, Dr M Buckingham, Dr R
Watts, Professor S Veres, Professor SM Fisher, Professor M
Havoutis, Dr I Dixon, Dr C Joyce, Professor MJ
Researcher Co-Investigators:
Project Partners:
ABB AWE Beihang University
BP (International) Chinese Academy of Science Createc Ltd
Department for International Trade EDF Energy Festo Ltd
Forth Engineering Ltd Fusion For Energy Gassco
Imitec Ltd Innotec Ltd Italian Institute of Technology
ITER - International Fusion Energy Org James Fisher Nuclear Limited Japan Atomic Energy Agency (JAEA)
Longenecker and Associates Manufacturing Technology Centre Moog Controls Ltd
National Nuclear Laboratory Nuclear AMRC Nuclear Decomissioning Authority
Nuvia Limited OC Robotics Oxford Investment Opportunity Network
Rolls-Royce Plc Shadow Robot Company Ltd Sprint Robotics
Tharsus Uniper Technologies Ltd. University of Florida
University of Texas at Austin Valtegra Virtual Engineering Centre (VEC)
Department: Electrical and Electronic Engineering
Organisation: University of Manchester, The
Scheme: Standard Research
Starts: 02 October 2017 Ends: 01 April 2021 Value (£): 12,203,190
EPSRC Research Topic Classifications:
Artificial Intelligence Computer Sys. & Architecture
Fundamentals of Computing Human-Computer Interactions
Robotics & Autonomy
EPSRC Industrial Sector Classifications:
Aerospace, Defence and Marine Environment
Related Grants:
Panel History:
Panel DatePanel NameOutcome
18 Sep 2017 ISCF - Robotics and Artificial Intelligence Hub Full Bids Panel Meeting Announced
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.
Key Findings
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Potential use in non-academic contexts
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Impacts
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Summary
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Organisation Website: http://www.man.ac.uk