CRADLE brings together the industrial experience that Jacobs have in applied Robotics and Autonomous Systems (RAS) with the research expertise at the University of Manchester in this field, to create a collaborative research centre that is internationally leading and sustainable in the long-term. Our vision for CRADLE is that it will deliver novel and transformational RAS technology for demanding environments, such as space, nuclear, energy generation and urban infrastructure, allowing the benefits promised by this technology to be realised across wide sectors of UK industry. Whilst there has been significant progress made in robotic systems in recent years, the step to truly autonomous robotics and smart machines, which will deliver the greatest impact to UK industry, remains a significant barrier, particularly in complex, demanding and heavily regulated environments. Here, incorrect decisions and inappropriate actions can have significant consequences, such as the release of radioactive materials or the loss of high value equipment. We have seen that incremental extensions to RAS components have not been sufficient to surmount this autonomy barrier and believe that a step change is required to: - create an autonomy-focussed framework that brings together the many independent robotic components that includes sensors, actuators, software and safety systems; - address key research gaps that exist in the specific components within this framework that affect the reliability, resilience and trustworthiness of the overall autonomous system; and - clarify, and embed, the wide range of end-user, business and regulatory constraints that must be accommodated within this framework for long-lasting autonomy. CRADLE has been guided by future industry needs and addresses major research obstacles to RAS development. Furthermore, CRADLE will create a pathway to impact that enables low-TRL RAS technologies to be developed and then translated into safe, reliable and innovative solutions that can be deployed to address long-term industry and societal problems in a range of demanding environments. We will focus on generic technologies that will allow RAS to be deployed across multiple industry sectors, and we will target specific use cases that will enable this technology to be demonstrated in sectors of particular importance to the industrial supply chain. These use cases will be drawn from sectors where Jacobs have existing capability, such as nuclear, space and urban infrastructure, but we will also explore areas of growing interest and opportunity, such as clean energy generation, sustainable transportation, healthcare and security.

In March 2011 a magnitude-9.0 earthquake struck in the Pacific Ocean off the northeast coast of Japan's Honshu island. Named the Great East Japan Earthquake by the Japanese government, it triggered a massive tsunami that flooded more than 200 square miles of coastal land. This devastating disaster caused a series of catastrophic failures resulting in the meltdown of the Fukushima Daiichi Nuclear Power Plant (NPP) and initiated a nuclear emergency. Reactor meltdown occurs when the cooling systems used to maintain and control the temperature of the nuclear fuel fails. The fuel then heats up uncontrollably and breaches the containment vessel or creates enough pressure to cause an explosion. Reactor meltdown occurred at all three reactors at Fukushima, resulting in fuel debris being dispersed throughout the reactors. Retrieval of the fuel debris from the Fukushima Daiichi NPP is of great importance for decommissioning and waste management. It requires detailed understanding of the radioisotope composition within the debris and knowledge of their location. However, inside the stricken reactors' containment vessels, the radiation levels are so intense it presents a significant challenge. It prevents direct human intervention, can overwhelm detectors and sensors, damage electronics and cause materials to perish. Access routes to inside the containment vessels are also very narrow. To make general observations, identify fuel debris composition, location and retrieval, dedicated robots are deployed. Many of the robots deployed to date have failed due to radiation damage during operation or their function is severely hampered by the extreme environment. This project brings together two world-leading research activities in the United Kingdom associated with radiation-hard, portable radiation detection (Lancaster University) and the development of small, radiation-hard remotely-operated vehicles (The University of Manchester) in collaboration with Okayama University and Kobe City College of Technology who have pioneered radiation-hard processors. The key aim of the research is to develop and deploy a simplified robot that prioritises radiation hardness and reliability over functional complexity. The hypothesis is, 'can such robots be more effective than the sophisticated alternatives tried to date?'. The ground-based radiation-hard robot will be equipped with non-destructive sensors for remote inspection. A radiation tolerant payload consisting of radiation sensors and LiDAR (light detection and ranging) will afford 3-dimensional (3D) spatial mapping of highly radioactive environments superimposed with located radiation intensities and radioisotope identities. The robot will be tested in realistic fields to demonstrate its ability to locate and identify dispersed radioisotopes derived from nuclear fuel debris inside Fukushima's stricken reactors. Such technology is also applicable to the UK's nuclear decommissioning challenges, specifically at Sellafield Site Ltd., and world-leading research in fusion energy at the UK Atomic Energy Authority.

In high-value manufacturing sectors such as aerospace and nuclear, safety is paramount. For this reason, the design and qualification of inspection for safety-critical components is a crucial part of the overall development cycle. However, current practice makes extensive use of experimental trials on physical components and mock-ups, into which artificial defects, limited to small numbers of specific test cases, must be introduced to demonstrate that they can be detected and characterised. Inspection qualification is therefore extremely time-consuming and costly (with some full mock-ups of defect-containing components costing £millions), and at odds with the general move toward agile, small-batch, bespoke, digitally-enabled manufacturing. We propose replacing the use of these expensive, wasteful, physical test specimens with digital alternatives, to improve manufacturing efficiency. Delivering this will require high-speed, representative, realistic numerical simulation capabilities to be developed, in combination with solutions to reliably sample and interpolate across the high dimensionality of the parametric space. This virtual testing capability will enable the inspection of a high value component to be designed, optimised, and qualified before a single part has been manufactured. It will provide the basis of a simulation tool for operator training and be able to generate data at the scale and fidelity needed to train future machine learning solutions for inspection automation. Ultrasonic array inspection will be the demonstrator case as this is the most widely used method for assessing the internal integrity of safety-critical components, both at manufacture and in service. To achieve the goal requires validated tools to synthesise authentic inspection data at scale and a methodology to robustly explore the vast parameter space of possible defects to determine inspection performance. Our idea to achieve this ambitious vision is to approach the problem from two complimentary directions. Bottom-up: we will make the direct numerical simulation of raw data more efficient. Building on previous world-leading research by the applicants, we will show how numerical simulation tools can be better exploited to reduce the computational burden by at least one order of magnitude. Top-down: we will make the quantitative characterisation of the multi-dimensional parameter space to qualify inspection performance more efficient. Drawing on our domain knowledge and in extensive discussion with industrial collaborators (Rolls-Royce, EDF, Jacobs, Airbus, and KANDE), we will develop suitable surrogate modelling, sampling, and integration strategies for accurately characterising the parameter space with a small number of high-fidelity numerical simulations. In addressing this problem we will produce a set of tools and techniques that ensure that inspection qualification is reduced in cost and complexity by orders of magnitude, leaving it fit for the future of digital manufacturing.

Since the 2004 Energy Act, nuclear fission has rapidly grown, and continues to grow, in significance in the UK's Energy and Net Zero Strategies. Government's Nuclear Industrial Strategy states clearly that the nuclear sector is integral to increasing productivity, driving growth across the country and meeting our Net Zero target. Nuclear is, and will continue to be, a vital part of our energy mix, providing low carbon power now and into the future, and the safe and efficient decommissioning of our nuclear legacy is an area of world-leading expertise. In order for this to be possible we need to underpin the skill base. The primary aim of SATURN is to provide high quality research training in the science and engineering underpinning nuclear fission technology, focussed on three broad themes: Current Nuclear Programmes. Decommissioning and cleanup; spent fuel and nuclear materials management; geological disposal; current operating reactors (AGRs, Sizewell B, propulsion); new build reactors (Hinkley C, Sizewell C, possibly Wylfa Newydd; Future Nuclear Energy: Advanced nuclear reactors (light water reactors, including PWR3, gas cooled reactors, liquid metal cooled reactors, other concepts); advanced fuel cycles; fusion (remote handling, tritium); Nuclear Energy in a Wider Context: Economics and finance; societal issues; management; regulation; technology transfer (e.g. robotics, sensors); manufacturing; interaction of infrastructure and environment; systems engineering. It has become clear that skills are very likely to limit the UK's nuclear capacity, with over half of the civil nuclear workforce and 70% of Subject Matter Experts due to retire by 2025. High level R&D skills are therefore on the critical path for all the UK's nuclear ambitions and, because of the 10-15 year lead time needed to address this shortage, urgent action is needed now. SATURN is a collaborative CDT involving the Universities of Manchester, Lancaster, Leeds, Liverpool, Sheffield and Strathclyde, which aims to develop the next generation of nuclear research leaders and deliver underpinning (Technology Readiness Level (TRL) 1-3), long term science and engineering to meet the national priorities identified in Government's Nuclear Industrial Vision. SATURN also provides a pathway for mid technology level research (TRL 4-6) to be carried out by allowing projects to be based partly or entirely in an industrial setting. The consortium partners have been instrumental in a series of highly successful CDTs, Nuclear FiRST (2009-2013), NGN (Next Generation Nuclear, 2013-2018) and GREEN (Growing skills for Reliable, Economic Energy from Nuclear, 2018-2023). In collaboration with an expanded group of key nuclear industry partners SATURN will create a step-change in PhD training to deliver a high-quality PhD programme tailored to student needs; high profile, high impact outreach; and adventurous doctoral research which underpins real industry challenges.

The job of materials science is to develop the materials that we need to make all of the things that we rely on in our daily lives. These range from the materials used to make large scale objects, such as aeroplanes and buildings, right down to the smallest scales like the processors in the electronic devices we use every day. These materials are often complicated and need to be carefully designed with just the right properties needed to do their jobs for many decades and often in incredibly harsh conditions. There are many current challenges that require us to develop new, improved materials. We need to meet our net-zero climate goals and get better at designing products that can be fully recycled, for example. And there are some resources that we currently use in important materials for which we would like to find alternatives. These are difficult challenges and we need to overcome them quickly. But the way that materials scientists have worked to develop a new material in the past is too slow: it can take up to 20 years to develop a new material and we cannot wait that long. Fortunately, recent developments in the computer simulation of materials, in robotics and sensor technology, in our ability to exploit large volumes of data through machine learning and in techniques for quickly making and testing large numbers of different materials can help to speed things up. This idea, bringing digital technologies together to help us make better materials more quickly, is called "Materials 4.0". If we are going to take advantage of Materials 4.0 then we need to make sure that materials scientists have the necessary digital skills. These skills, things like data informatics, machine learning and advanced computer simulation, are not usually covered in depth in undergraduate university courses in science and engineering. So, the Henry Royce Institute, the UK's national institute for advanced materials, in partnership with the National Physical Laboratory, is proposing to set up a Centre for Doctoral Training (CDT) that will take at least 70 science and engineering graduates and train them in the techniques of Materials 4.0. These students will work towards PhDs and become leaders in the field of Materials 4.0. They will undertake research projects in universities across the UK (Cambridge, Oxford, Imperial College, Manchester, Sheffield, Leeds and Strathclyde), tackling a broad range of materials science challenges and developing new approaches in Materials 4.0. The need for these new approaches is widespread, throughout academia and in industry. In recognition of this, the training programme that we develop for the CDT will be made available more widely, in different forms, so that we can disseminate skills in Materials 4.0 to existing researchers in universities and industrial companies as quickly as possible. The training approach of the CDT will be to take our students from "Learners to Leaders" over the course of four years. Our students will be working across boundaries between materials science and computer / data science and between academia and industry. They will build new interfaces and help to develop a common language for communication. To strengthen our students' own learning and to disseminate their skills more widely, we will train our students as trainers so that the students are actively involved in designing and delivering training for fellow researchers and take the role of ambassadors for a cultural shift in materials science to modern ways of working.