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.

SoRo for Health is a unique interdisciplinary Platform uniting three new and rapidly advancing areas of science (soft robotics, advanced biomaterials and bioprinting, regenerative medicine) in a collaboration that will deliver transformative technological solutions to major unmet health problems. We are a collaborative scientific group including representatives from three of the most exciting and rapidly advancing technology areas in the world. Soft robotics is a new branch of robotics that uses compliant materials to create robots that move in ways mirroring those in nature; a new paradigm that is already transforming fields as diverse as aerospace and manufacturing. Advanced biomaterials is a rapidly progressing field exploring the application of novel and conventional materials to restoring structure and function. It has recently been augmented by advances in 3D- and Bio-printing with seminal clinical breakthroughs. Regenerative medicine uses a range of biological tools, such as cells, genes and biomaterials, to replace and restore function in patients with a range of disorders. It explores the interface between materials and cells and tissues and has been applied to regenerate critical organs and tissues. Our three groups have combined over the last few years to develop a range of prototype solutions to unmet health needs, in areas as diverse as breathing and swallowing, motor disorders and cardiovascular disease. Here we seek to further coalesce our activity in a unique EPSRC Platform with five primary goals. Firstly and most importantly, we will support, retain and develop the careers of three dynamic rising stars (postdoctoral research assistants, PDRAs) who might otherwise be lost from the field. Primarily supporting their career development, we will thereby also ensure the provision of a cadre of stellar individuals with cross-cutting scientific skills and leadership training who can provide leadership and direction to this nascent, but incredibly exciting, field of Soft Robotics (SoRo) for Health. This will benefit these scientists, the field, and the UK through scientific advance and commercial partnerships. Secondly, we will support our PDRAs to explore novel and high-risk hypotheses related to our combined fields through a flexible inbuilt funding stream. This will help their development, but also generate new ideas and technologies to take forward towards further scientific exploration and, where appropriate, clinic; ideas that might otherwise have fallen by the funding wayside. Thirdly, we will expand and develop a vibrant international network that will further support the development of our stars as well as energising the whole field internationally, with its hub here in the UK. Fourthly, we will engage with end-users, from both healthcare professional and patient/carer communities. We will use professional facilitators and established qualitative techniques to identify the key challenges and opportunities for SoRo as it seeks to address the outstanding and imminent issues in population health and healthcare. Finally, we will work with UK industry and biotech business leaders to develop an effective, streamlined route to IP protection, application and commercialisation that gives SoRo for Health technologies the best possible chance for widespread health gains and speedy application to those in need. Thus, the SoRo for Health Platform combines the talents, and specifically emergent talents, of internationally-leading groups in three new areas with the common Vision of transforming the lives of millions through the development of responsive, customised soft robotic-based implants and devices to address some of the major unmet health challenges of the 21st Century.

Disassembly is an essential operation in many industrial activities including repair, remanufacturing and recycling. Disassembly tends to be manually carried out - it is labour intensive and usually inefficient. Disassembly requires high-level dexterity in manipulations and thereby can be more difficult to robotise in comparison to the tasks that have no physical contacts (e.g. computer visual inspection) or simple contacts (e.g. cutting, welding, pick-and-place). Robotic disassembly has the potential to improve the productivity of repair, remanufacturing, recycling, all of which have been recognised as key components of a more circular economy. The existing procedure and state-of-the-art techniques for disassembly automation usually require a comprehensive analysis of a disassembly task, correct design of sensing and compliance facilities, efficient task plans, and a reliable system integration. It is usually a complex, expensive and time-consuming process to implement a robotic disassembly system. This project will develop a self-learning mechanism to allow robots to learn disassembly tasks and the respective control strategies autonomously, by combining multidimensional sensing and machine learning techniques. This capability will help build a more plug-and-play disassembly automation system, and reduce the technical difficulties and the implementation costs of disassembly automation. It is expected the next generation industrial robotics can be adopted in more complex and uncertain tasks such as maintenance, cleaning, repair, remanufacturing and recycling, where many processes are contact-rich. Disassembly is a typical contact-rich task. The Principal Investigator envisages that self-learning robotic disassembly will provide key understandings and technologies that can be adopted to the automation of other types of contact-rich tasks in the future to encourage a wider adoption of robots in the UK industry.

The research project will study the physics and mechanics of creep cavity nucleation and the reverse process of healing by sintering in polycrystalline materials for energy applications using both modelling and experimental approaches. The experimental work will focus on a model single phase material (commercially pure Nickel), a simple particle strengthened material (Nickel with addition of Carbon), a commercial austenitic stainless steel (Type 316H), a superalloy (IN718) and a martensitic steel P91/92. An array of state-of-the-art experimental techniques will be applied to inform the development of new physics-based cavity nucleation and sintering models for precipitation hardening materials. Once implemented in mechanical analyses, and validated, such models will form the basis for development of improved life estimation procedures for high thermal efficiency power plant components.

Rapid advances in fertiliser use and other inputs to crops have dramatically improved Chinese crop production over recent decades, but this has not been done in a sustainable manner and it is estimated that >10M t of synthetic nitrogen fertiliser is wasted annually in China. The number of small to medium-sized commercial family farms is increasing from a merging of smaller, non-commercial family plots. It is desirable to support these farms to maintain rural populations and economies. These family-farmers also need technological assistance to manage larger areas that they have no historical connection to. Precision agriculture, allowing for fine-scale within-field management of crops based on detailed spatial data collection, has an essential role to play in increasing fertiliser and resource use efficiency on farms. This will increase production efficiency (profitability) as well as reduce the environmental footprint of agricultural practices linked to fertilizer use. However, in China there are fundamental barriers to uptake of precision agriculture methods and technology, including high costs relative to income and unquantified financial benefits, a lack of data and services and a lack of awareness and acceptance by growers, communities and administrative agencies. This joint UK-China collaboration aims to improve the use efficiency of nutrients and agri-chemicals in crop production in China, by addressing key technological, agricultural and social or economic barriers to the use of precision agriculture methods in commercial family farms. The project will develop new technology and data sources for agricultural decision making, including the application of advanced hyperspectral cameras, able to measure many wavelengths of light and provide detailed information on crop health, and improved technology for precise spatial positioning within fields. Improved methods to utilise satellite imagery, especially from radar sensors systems, to provide accessible data on crop nutrient levels and growth will also be developed and the advantages of combining data from multiple sources (satellites, airborne sensors and ground monitoring) will be assessed. These improved data layers, providing frequent and detailed spatial information on crop growth, crop health and soils, will then be combined with models of crop growth to provide a system for agricultural decision making that is applicable to family farms in China. This will promote the optimal use of agricultural resources, such as fertiliser. Developed methods will be tested on exemplar farms in China, covering a range of geographic regions and crop systems that have been established in previous research projects. To facilitate both the maximum engagement from a diversity of community and industry members, and the maximum usage of the agri-technologies and precision agriculture methods by farmers, it is critical to incorporate both scientific and local (community and practitioner) expertise into the project. This is critical to understanding and addressing issues specific to these farming system. An integral aspect of the project is to therefore undertake focussed research on the societal and economic barriers to uptake and to use of these technologies. This research will identify and address these barriers via the mode of development and the delivery of the project outputs onto family-farms. This work will also form the basis for wide-reaching and effective public engagement, knowledge exchange and policy translation to ensure the latest methods are adopted in China. Activities will include the development of a data information portal for crop management, stakeholder workshops and technical training for local growers and agricultural specialists.