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 UK along with the rest of the world is becoming increasingly dependent on technological systems, including satellite communications, global positioning systems, and power grids, that are at risk from space weather. Many space weather hazards originate in the ionosphere, the ionised upper part of the atmosphere at altitudes of 90 km and above, where solar wind energy channelled by the Earth's magnetic field can cause a variety of unpredictable and deleterious effects. It causes electrical currents to flow, which heat the atmosphere in a process known as Joule heating, which in turn can cause the atmosphere to expand upwards, producing drag on satellites, hence making their orbits harder to predict and reducing their lifetimes. It produces horizontal motions of the ionosphere which modify the neutral winds in the thermosphere through friction. It produces the auroras, associated with particle precipitation from the magnetosphere above, which modify the ionospheric structure. Moreover, it gives rise to plasma instabilities which cause the ionosphere to become corrugated, scattering radio waves from satellites consequently disturbing communications and GPS. Although the large-scale distribution of such space weather hazards is relatively well reproduced in global circulation models, the physics occurring on spatial scales smaller than the model grid is poorly understood, which holds back improvements in forecasting. The FINESSE project will exploit a new and unique NERC-funded incoherent scatter radar system, EISCAT_3D, located in northern Scandinavia, to study these sub-grid space weather scales. EISCAT_3D will be able to determine the ionospheric structure in a box roughly 200 km to a side horizontally and 800 km vertically, at an unprecedented spatial and temporal resolution, to image the processes leading to space weather effects. FINESSE will also exploit a next-generation coherent scatter radar to measure ionospheric motions, three neutral wind imagers to measure the interaction between the thermosphere and the ionosphere, three all-sky auroral cameras to view regions of precipitation from the magnetosphere above, a fine-scale auroral imager to observe auroral structures on spatial and temporal scales even finer than EISCAT_3D can probe, and a radio telescope and network of GPS receivers to look at the scintillation of radio signals from both cosmic sources and satellites. The main aims of FINESSE are as follows. 1) To determine the small-scale sources of Joule heating, to place these within the context of the larger picture of polar auroral disturbances, to determine the link between Joule heating and satellite drag, and to incorporate these results to improve forecast models. 2) To determine the cause of small-scale ionospheric structuring, and to understand how this leads to scintillation of radio signals. 3) To probe auroral dynamics at the very smallest temporal and spatial scales to understand the physics of coupling between the magnetosphere and ionosphere, the role auroral processes play in heating and structuring the ionosphere and atmosphere, and the instability that leads to substorms (explosive releases of energy into the nightside auroral ionosphere). FINESSE will liaise with space weather forecasters and other stakeholders to disseminate this greater understanding of small-scale processes in producing space weather hazards and to translate it into significant economic benefit to the UK.

There is an imminent need to make better use of existing aviation infrastructure as air traffic is predicted to increase 1.5 times by 2035. Many airports operate at near maximum capacity, and the European Commission recognises the necessity to increase capacity to satisfy demand. In addition, inefficient operations lead to delays, congestion, and increased fuel costs and noise levels inconveniencing all stakeholders, including airports, airlines, passengers and local residents. A critical issue is routing and scheduling the ground movements of aircraft. Although ground movement is only a small fraction of the overall flight, the inefficient operation of aircraft engines at taxiing speed can account for a significant fuel burn. This applies particularly at larger airports, where ground manoeuvres are more complex, but also for short-haul operations, where taxiing represents a larger fraction of an overall flight. It is estimated that fuel burnt during taxiing alone represents up to 6% of fuel consumption for short-haul flights resulting in 5m tonnes of fuel burnt per year globally. This project aims to investigate a decision support system which considers multiple factors to provide more robust taxiing routes. Current decision support systems for routing and scheduling taxiing aircraft suffer from several limitations: 1) The only objective they consider is minimising taxi time, ignoring other important factors. These other factors include taking into account engine performance which is linked to fuel consumption, environmental impact and cost. Routes and schedules, which are efficient in terms of fuel and cost, are therefore compromised as a result of considering a one dimensional objective. 2) Airframe dynamics are not taken into account during planning of routes and schedules. Consequently, the taxing instructions issued may be hard to follow, making compliance with the allocated routes unrealistic. 3) Taxi time is typically based on average speeds of aircraft. This is an over-simplification meaning that any taxiing manoeuvre which falls outside the expected duration can affect the taxiing of other aircraft. Furthermore, if the approach of including overly conservative time buffers to absorb uncertainty is adopted, the resulting overall airport operating efficiency will be degraded. 4) It is difficult to specify taxiing speeds and heuristic rules for routing and scheduling systems as: they depend on airport layout and operational requirements, which can vary throughout the day according to the volume of air traffic. Consequently, routing and scheduling systems have to be reconfigured for specific airports and operational constraints. 5) Due to variability in taxi speed and over-simplistic models of aircraft, there is lack of understanding as to how much benefit can be achieved by automated routing and scheduling in real-world settings. TRANSIT will directly address these limitations of current systems, to make better use of existing airport infrastructure and lessen the impact of the growing aviation sector on the environment. Multi-objective optimisation algorithms will be integrated with models of aircraft to balance the reduction of taxi time, cost and emissions. We aim to make the routing and scheduling system easily reconfigurable to any airport. The uncertainty will be directly incorporated in planning, resulting in robust taxiing, verified by pilot-in-the-loop trials. TRANSIT aims to investigate such a system and its associated benefits in collaboration across a broad range of disciplines and fields (Engineering, Operational Research, and Computer Science) needed to tackle such challenging problem. Cooperation with leading industrial stakeholders, and consultation with established academics, ensure that the work is cutting edge while reflecting needs of the industrial partners.