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.

The long-term evolution of energy systems is set by the investment decisions of very many actors such as up-stream resource companies, power plant operators, network infrastructure providers, vehicle owners, transport system operators and building developers and occupiers. But these decisions are deliberately shaped by markets and incentives that have been designed by local and national governments to achieve policy objectives on energy, air-quality, economic growth and so on. It is clear then that government and businesses need detailed and dependable evidence of what can be achieved, what format of energy system we should aim for, what new technologies need to be encouraged, and how energy systems can form part of an industrial strategy to new goods and services. It is widely accepted that a whole-system view of energy is needed, covering not only multiple energy sectors (gas, heat, electricity and transport fuel) but also the behaviour of individuals and organisations within the energy consuming sectors such as transport and the built environment. This means that modelling energy production, delivery and use in a future integrated system is highly complex and analytically challenging. To provide evidence to government and business on what an optimised future system may look like, one has to rise to these modelling challenges. For electricity systems alone, there are established models that can optimise for security, cost and emissions given some assumptions (and sensitivities) and these have been used to provide policy and business strategy evidence. However, such models do not exist for the complex interactions of integrated systems and not at the level of fine detailed needed to expose particularly difficult operating conditions. Our vision is to tackle the very challenging modelling required for integrated energy systems by combining multi-physics optimising techno-economic models with machine learning of human behaviour and operational models emerging multi-carrier network and conversion technologies. The direction we wish to take is clear but there are many detailed challenges along the way for which highly innovative solutions will be needed to overcome the hurdles encountered. The programme grant structure enables us to assemble an exceptional team of experts across many disciplines. There are new and exciting opportunities, for instance, to apply machine learning to identify in a quantitative way models of consumer behaviour and responsiveness to incentives that can help explore demand-side flexibility within an integrated energy system. We have engaged four major partners from complementary sectors of the energy system that will support the programme with significant funding (approximately 35% additional funding) and more importantly engage with us and each other to share insights, challenges, data and case studies. EDF Energy provide the perspective on an energy retail business and access to smart meter trail data. Shell provide insights into the future fuels to be used in transport and building services. National Grid (System Operator) give the perspective of the use of flexibility and new service propositions for efficient system operations. ABB are a provider of data acquisition and control systems and provide industrial perspective of decentralisation of control. ABB have committed to providing substantial equipment and resource to build a verification and demonstration facility for decentralised control. We are also engaging examples of the new entrants, often smaller companies with potentially disruptive technologies and business models, who will engage and share some of their insights.