Advances in robotics and autonomous systems are changing the way space is explored in ever more fundamental ways. Both human and scientific exploration missions are impacted by these developments. Where human exploration is concerned, robots act as proxy explorers: deploying infrastructure for human arrival, assisting human crews during in-space operations, and managing assets left behind. As humans extend their reach into space, they will increasingly rely on robots enabled by artificial intelligence to handle many support functions and repetitive tasks, allowing crews to apply themselves to problems that call for human cognition and judgment. Where scientific exploration is concerned, robotic spacecraft will continue to go out into Earth orbit and the far reaches of deep space, venturing to remote and hostile worlds, and returning valuable samples and data for scientific analysis. The aim of FAIR-SPACE is to go beyond the-state-of-the-art in robotic sensing and perception, mobility and manipulation, on-board and on-ground autonomous capabilities, and human-robot interaction, to enable space robots to perform more complex tasks on long-duration missions with minimal dependence on ground crew. More intelligent and dexterous robots will be more self-sufficient, being able to detect and respond to anomalies on board autonomously and requiring far less teleoperation. The research will see novel technologies being developed for robotic platforms used in orbit or on planet surfaces, namely: future on-orbit robots tasked with repairing satellites, assembling large space telescopes, manufacturing in space, removal of space junk; and future surface robots, also known as planetary rovers, for surveying, observation, extraction of resources, and deploying infrastructure for human arrival and habitation; a further case study will target human-robot interoperability aboard the International Space Station. The research will merge the best available off-the-shelf hardware and software solutions with trail-blazing innovations and new standards and frameworks, aiming at the development of a constellation of space robotics prototypes and tools. This aims to accelerate the prototyping of autonomous systems in a scalable way, where the innovations and methodologies developed can be rapidly spun out for wide adoption in the space sector worldwide. FAIR-SPACE directly addresses two of the priorities in the Industrial Strategy Green Paper: robotics & artificial intelligence and satellite & space technologies. The clear commitment offered by the industrial partners demonstrates the need for establishing a national asset that will help translate academic outputs into innovative products/services. Our impact plan will ensure we can maximise co-working with user organisations, align our work with other programmes (e.g. InnovateUK) and effectively transfer our research outputs and technology to other sectors beyond space such as nuclear, deep mining and offshore energy. FAIR-SPACE will therefore not only help in wealth creation but also help develop a robotics UK community with a leading international profile.

The development of robotic assistants is being held back by the lack of a coherent and credible safety framework. Consequently, robotic assistant applications are confined either to research labs or, in practice, to scenarios where physical interaction with humans is purposely limited, e.g. surveillance, transport or entertainment (e.g. museums). In the domestic/personal domain, however, interactions take place in an informal, unstructured, and typically highly complex way. Even in more constrained industrial settings, the need for reduced manufacturing costs is motivating the creation of robots capable of much greater flexibility and intelligence. These robots need to work near to, be taught by, and perhaps even interact physically with, human co-workers. So, how can we enhance robots so that they can participate in sophisticated interactions with humans in a safe and trustworthy manner? This is a fundamental research question that must be addressed before the traditional physical safety barrier between the robot and the human can be removed, which is essential for close-proximity human-robot interactions. How, then, might we establish such safety arguments? Intrinsically safe robots must incorporate safety at all levels (mechanical; control; and human interaction). There has been some work on safety at lower, mechanical, levels to severely restrict movements near humans, without regard to whether the movements are "safe" or not. Crucially, no one has yet tackled the high-level behaviours of robotic assistants during interaction with humans, i.e. not only whether the robot makes safe moves, but whether it knowingly or deliberately makes unsafe moves. This is the focus of our project. Formal verification exhaustively analyses all of the robot's possible choices, but uses a vastly simplified environmental model. Simulation-based testing of robot-human interactions can be carried out in a fast, directed way and involves a much more realistic environmental model, but is essentially selective and does not take into account true human interaction. Formative user evaluation provides exactly this validation, constructing a comprehensive analysis from the human participant's point of view. It is the aim of our project to bring these three approaches together to tackle the holistic analysis of safety in human-robot interactions. This will require significant research in enhancing each of the, very distinct, approaches so they can work together and subsequently be applied in realistic human-robot scenarios. This has not previously been achieved. Developing strong links between the techniques, for example through formal assertions and interaction hypotheses, together with extension of the basic techniques to cope with practical robotics, is the core part of our research. Though non-trivial to achieve, this combined approach will be very powerful. Not only will analysis from one technique stimulate new explorations for the others, but each distinct technique actually remedies some of the deficiencies of another. Thus, this combination provides a new, strong, comprehensive, end-to-end verification and validation method for assessing safety in human-robot interactions.