Imagine a future where autonomous systems are widely available to improve our lives. In this future, autonomous robots unobtrusively maintain the infrastructure of our cities, and support people in living fulfilled independent lives. In this future, autonomous software reliably diagnoses disease at early stages, and dependably manages our road traffic to maximise flow and minimise environmental impact. Before this vision becomes reality, several major limitations of current autonomous systems need to be addressed. Key among these limitations is their reduced resilience: today's autonomous systems cannot avoid, withstand, recover from, adapt, and evolve to handle the uncertainty, change, faults, failure, adversity, and other disruptions present in such applications. Recent and forthcoming technological advances will provide autonomous systems with many of the sensors, actuators and other functional building blocks required to achieve the desired resilience levels, but this is not enough. To be resilient and trustworthy in these important applications, future autonomous systems will also need to use these building blocks effectively, so that they achieve complex technical requirements without violating our social, legal, ethical, empathy and cultural (SLEEC) rules and norms. Additionally, they will need to provide us with compelling evidence that the decisions and actions supporting their resilience satisfy both technical and SLEEC-compliance goals. To address these challenging needs, our project will develop a comprehensive toolbox of mathematically based notations and models, SLEEC-compliant resilience-enhancing methods, and systematic approaches for developing, deploying, optimising, and assuring highly resilient autonomous systems and systems of systems. To this end, we will capture the multidisciplinary nature of the social and technical aspects of the environment in which autonomous systems operate - and of the systems themselves - via mathematical models. For that, we have a team of Computer Scientists, Engineers, Psychologists, Philosophers, Lawyers, and Mathematicians, with an extensive track record of delivering research in all areas of the project. Working with such a mathematical model, autonomous systems will determine which resilience- enhancing actions are feasible, meet technical requirements, and are compliant with the relevant SLEEC rules and norms. Like humans, our autonomous systems will be able to reduce uncertainty, and to predict, detect and respond to change, faults, failures and adversity, proactively and efficiently. Like humans, if needed, our autonomous systems will share knowledge and services with humans and other autonomous agents. Like humans, if needed, our autonomous systems will cooperate with one another and with humans, and will proactively seek assistance from experts. Our work will deliver a step change in developing resilient autonomous systems and systems of systems. Developers will have notations and guidance to specify the socio-technical norms and rules applicable to the operational context of their autonomous systems, and techniques to design resilient autonomous systems that are trustworthy and compliant with these norms and rules. Additionally, developers will have guidance to build autonomous systems that can tolerate disruption, making the system usable in a larger set of circumstances. Finally, they will have techniques to develop resilient autonomous systems that can share information and services with peer systems and humans, and methods for providing evidence of the resilience of their systems. In such a context, autonomous systems and systems of systems will be highly resilient and trustworthy.

The vision for this Programme is to deliver the step changes in Robotics and Autonomous Systems (RAS) capability that are necessary to overcome crucial challenges facing the nuclear industry in the coming decades. The RAS challenges faced in the nuclear industry are extremely demanding and complex. Many nuclear installations, particularly the legacy facilities, present highly unstructured and uncertain environments. Additionally, these "high consequence" environments may contain radiological, chemical, thermal and other hazards. To minimise risks of contamination and radiological shine paths, many nuclear facilities have very small access ports (150 mm - 250 mm diameter), which prevent large robotic systems being deployed. Smaller robots have inherent limitations with power, sensing, communications and processing power, which remain unsolved. Thick concrete walls mean that communication bandwidths may be severely limited, necessitating increased levels of autonomy. Grasping and manipulation challenges, and the associated computer vision and perception challenges are profound; a huge variety of legacy waste materials must be sorted, segregated, and often also disrupted (cut or sheared). Some materials, such as plastic sheeting, contaminated suits/gloves/respirators, ropes, chains can be deformed and often present as chaotic self-occluding piles. Even known rigid objects (e.g. fuel rod casings) may present as partially visible or fragmented. Trivial tasks are complicated by the fact that the material properties of the waste, the dose rates and the layout of the facility within which the waste is stored may all be uncertain. It is therefore vital that any robotic solution be capable of robustly responding to uncertainties. The problems are compounded further by contamination risks, which typically mean that once deployed, human interaction with the robot will be limited at best, autonomy and fault tolerance are therefore important. The need for RAS in the nuclear industry is spread across the entire fuel cycle: reactor operations; new build reactors; decommissioning and waste storage and this Programme will address generic problems across all these areas. It is anticipated that the research will have a significant impact on many other areas of robotics: space, sub-sea, mining, bomb-disposal and health care, for example and cross sector initiatives will be pursued to ensure that there is a two-way transfer of knowledge and technology between these sectors, which have many challenges in common with the nuclear industry. The work will build on the robotics and nuclear engineering expertise available within the three academic organisations, who are each involved in cutting-edge, internationally leading research in relevant areas. This expertise will be complemented by the industrial and technology transfer experience and expertise of the National Nuclear Laboratory who have a proven track record of successfully delivering innovation in to the nuclear industry. The partners in the Programme will work jointly to develop new RAS related technologies (hardware and software), with delivery of nuclear focused demonstrators that will illustrate the successful outcomes of the Programme. Thus we will provide the nuclear supply chain and end-users with the confidence to apply RAS in the nuclear sector. To develop RAS technology that is suitable for the nuclear industry, it is essential that the partners work closely with the nuclear supply chain. To achieve this, the Programme will be based in west Cumbria, the centre of much of the UK's nuclear industry. Working with researchers at the home campuses of the academic institutions, the Programme will create a clear pipeline that propels early stage research from TRL 1 through to industrially relevant technology at TRL 3/4. Utilising the established mechanisms already available in west Cumbria, this technology can then be taken through to TRL 9 and commercial deployment.

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 use of autonomous robotic technologies is increasingly common for applications such as manufacturing, warehousing, and driverless vehicles. Automated robots have been used in chemistry research, too, but their widespread application is limited by the cost of the technology, and the need to build a bespoke automated version of each instrument that is required. We have developed a different approach by using mobile 'robotic chemists' that can work within a relatively standard laboratory, replicating the dexterous tasks that are carried out by human researchers. These robots can operate autonomously, 24/7, for extended periods, and they can therefore cover a much larger search space that would usually be possible. Also, the robots are driven by artificial intelligence (AI) and can search highly complex multidimensional experimental spaces, offering the potential to find revolutionary new materials. They can also carry out multiple separate experiments in parallel, if needed, to make optimal use of the available hardware in a highly cost-effective way. Our proposal is to establish a globally unique user facility in Liverpool that covers a broad range of materials research problems, allowing the discovery of useful products such as clean solar fuels catalysts, catalysts for plastics recycling, medicinal materials, and energy materials. This facility will allow researchers from both academic teams and from industry to access this new technology, which would otherwise be unavailable to them. Because the automation approach is modular, it will be possible for users to bring along specific equipment for their experiments to be 'dropped in' temporarily to create new workflows, greatly expanding the possible user base. The scope here is very broad because we have recently developed methods that give these robots have very high placement precision (+/- 0.12 mm): to a large extent, if a human can use the instrument, then so can the robot. We have identified, initially, a group of 25 academic users across 12 universities as 'day one' prospective users, as well as 7 industrial organisations with a specific interest in this technology. The potential user base, however, is far broader than this, and we will solicit applications for access throughout the project and beyond. This will be managed by a Strategic Management Team and an Operational Management Team that involves academics as well as permanent technical, administrative, and business development staff in the Materials Innovation Factory in Liverpool. Our overall objective is to build a sustainable AI-driven robotic facility that will provide a unique competitive advantage for the UK to discover new functional materials on a timescale that would be impossible using more conventional research methodology. In addition to focusing on excellent science, we will also consider diversity and career stage when prioritising access; for example, even a short, one-week visit to this autonomous facility might lead to 100's or even 1000's of new materials with associated property measurements, which might radically transform a PhD project or the change the direction of the research programme for an Early Career Researcher. This facility will therefore build the base of the UK research pyramid, as well as supporting activity that is already internationally leading, and our day-one user base includes researchers at all career stages.

Nuclear facilities require a wide variety of robotics capabilities, engendering a variety of extreme RAI challenges. NCNR brings together a diverse consortium of experts in robotics, AI, sensors, radiation and resilient embedded systems, to address these complex problems. In high gamma environments, human entries are not possible at all. In alpha-contaminated environments, air-fed suited human entries are possible, but engender significant secondary waste (contaminated suits), and reduced worker capability. We have a duty to eliminate the need for humans to enter such hazardous environments wherever technologically possible. Hence, nuclear robots will typically be remote from human controllers, creating significant opportunities for advanced telepresence. However, limited bandwidth and situational awareness demand increased intelligence and autonomous control capabilities on the robot, especially for performing complex manipulations. Shared control, where both human and AI collaboratively control the robot, will be critical because i) safety-critical environments demand a human in the loop, however ii) complex remote actions are too difficult for a human to perform reliably and efficiently. Before decommissioning can begin, and while it is progressing, characterization is needed. This can include 3D modelling of scenes, detection and recognition of objects and materials, as well as detection of contaminants, measurement of types and levels of radiation, and other sensing modalities such as thermal imaging. This will necessitate novel sensor design, advanced algorithms for robotic perception, and new kinds of robots to deploy sensors into hard-to-reach locations. To carry out remote interventions, both situational awareness for the remote human operator, and also guidance of autonomous/semi-autonomous robotic actions, will need to be informed by real-time multi-modal vision and sensing, including: real-time 3D modelling and semantic understanding of objects and scenes; active vision in dynamic scenes and vision-guided navigation and manipulation. The nuclear industry is high consequence, safety critical and conservative. It is therefore critically important to rigorously evaluate how well human operators can control remote technology to safely and efficiently perform the tasks that industry requires. All NCNR research will be driven by a set of industry-defined use-cases, WP1. Each use-case is linked to industry-defined testing environments and acceptance criteria for performance evaluation in WP11. WP2-9 deliver a variety of fundamental RAI research, including radiation resilient hardware, novel design of both robotics and radiation sensors, advanced vision and perception algorithms, mobility and navigation, grasping and manipulation, multi-modal telepresence and shared control. The project is based on modular design principles. WP10 develops standards for modularisation and module interfaces, which will be met by a diverse range of robotics, sensing and AI modules delivered by WPs2-9. WP10 will then integrate multiple modules onto a set of pre-commercial robot platforms, which will then be evaluated according to end-user acceptance criteria in WP11. WP12 is devoted to technology transfer, in collaboration with numerous industry partners and the Shield Investment Fund who specialise in venture capital investment in RAI technologies, taking novel ideas through to fully fledged commercial deployments. Shield have ring-fenced £10million capital to run alongside all NCNR Hub research, to fund spin-out companies and industrialisation of Hub IP. We have rich international involvement, including NASA Jet Propulsion Lab and Carnegie Melon National Robotics Engineering Center as collaborators in USA, and collaboration from Japan Atomic Energy Agency to help us carry out test-deployments of NCNR robots in the unique Fukushima mock-up testing facilities at the Naraha Remote Technology Development Center.