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.

Since 9-11 and 7-7, terrorism has been a major public concern. To ensure public safety and to protect the UK economy, research is needed that offers new methods to foil attacks before they are executed, to identify people and networks who might be preparing for or undertaking an attack, and to provide clear evidence that can be used to justify questioning, arrests and prosecutions. In this study, we will investigate whether deception can be identified and proved from 'scent trails', that is, coherent accounts of suspects' activities over time compiled from tracking their movements, communications and behaviours. We will develop software to derive inferences about what activities are consistent with suspects' scent trails and what are ruled out. These inferences will allow investigators to challenge suspects, both in real time (e.g., to encourage suspects to abandon an ongoing attack) and during interviews (e.g., to point out inconsistencies between a suspect's account and scent trail evidence that might change the course of an interview). The project will investigate scent trails in the context of people undertaking deceptive activities to gain advantage in adversarial 'treasure hunt'-type games. The games will be developed in consultation with stakeholders to provide a non-sensitive analogy to counter-terrorism contexts. Players, typically undergraduate students paid for participation, will be monitored during games via positional and communication data obtained from mobile devices enabled with geospatial positioning devices. Novel software for integrating these data will be developed to build up scent trails of players' activities during game play. Methods of artificial intelligence will be combined to derive inferences from the scent trails about what kinds of activity are possible and impossible given a player's location, trajectory, activities and links with others. We envision games with 3 teams: Team A represent the adversary, Team B the police or general public, and Team C the intelligence services. Team A scores points by visiting target locations within a time limit under a set of game rules that they must violate if they are to win. They must try to hide rule violations from Team B, who score points by preventing or identifying Team A's deceptions successfully. Team C can challenge Team A by sending them indications of the scent trails that are held or can feed Team B intelligence information. Moreover, the inferences from scent trails will support Team C in deciding how best to prove or falsify a suspicion during an interview with Team A players at key points during the games. By conducting observation of players during games, we can investigate how people change their behaviours when they are confronted with evidence that reveals their deceptions. We will also interview players at key points during games as a simulation of interviews with suspects, eliciting from players accounts of their activities before presenting them with challenges based on their own scent trails that are either consistent or inconsistent with legal game playing. This will allow interview and analysis techniques to be improved and will provide clues as to how people subsequently change their behaviour after they have been confronted with their deception. The results will also allow us to test between hypotheses deriving from forensic psychology as to how best to detect deception. The research also allows us to explore public awareness of, and response to, monitoring and surveillance in counter-terrorism. With an advisory panel of stakeholders and subject specialists representing key public and academic bodies, we will identify ethical and legal issues associated with collecting and using data on peoples' movements through public spaces. We will also conduct questionnaire studies with game players and others not involved in the games, to measure attitudes to monitoring and surveillance in game-playing and other contexts.

Exascale computing offers the prospect of running numerical models, for example of nuclear fusion and the climate, at unprecedented resolution and fidelity, but such models are still subject to uncertainty and we need to able to quantify such uncertainties (and for example use data on model outputs to calibrate the model inputs). Exascale computing comes at a cost. We will never be able to run huge ensembles go models on Exascale computers. Naive methods, such as Monte Carlo where we simply sample from the probability distribution of the model inputs, run a huge ensemble of models and produce a sample from the output distribution, are not going to be feasible. We need to develop uncertainty quantification methodology that allows us to efficiently, and effectively, perform sensitivity and uncertainty calculations with the minimum number of exascale model runs. Our methods are based on the idea of an emulator. An emulator is a statistical approximation linking model inputs and outputs in a fast non-linear way. It also includes a measure of its own uncertainty so we know how well it is approximating the original numerical model. Our emulators are based on Gaussian processes. Normally we would run a designed experiment and use these results to train the emulator. Because of the cost of exascale computing we use a hierarchy of models from fast, low fidelity versions through higher fidelity more computationally expensive ones to the very expensive, very high fidelity one at the apex of the hierarchy. Building a joint emulator for all the models in the hierarchy allows us to gain strength from the low fidelity ones to emulate the exascale models. Although such ideas have been around for a number of years they have not been exploited much for very large models. We will expand on the existing theory on a number of new ways. First we will look at the problem of design. To exploit the hierarchy to its fullest extent we need an experimental design that allocates model runs to the correct layer of the model hierarchy. We will extend existing sequential design methodology to work with hierarchies of model, not only finding the optimal next set of inputs for running the model but also which level it should be run in. We will also ensure that the sequential design is 'batch' sequential, allowing us to run ensembles rather than waiting for each run to return answers. Because the inputs and outputs of exascale models are often fields of correlated values we will develop methods for handling such high dimensional inputs and outputs and how to relate them to other levels of the hierarchy. Finally we will investigate whether AI methods other than Gaussian processes can be used to build efficient emulators.

Establishing a hydrogen fuelled transportation network is a research challenge that cuts across both the energy and transport sectors. It is a truly multi-disciplinary challenge which will require the advancement of many mutually dependent research disciplines. This Network will support the dissemination and impact of these activities between academia, industry, policymakers and the general public. Under the hydrogen fuelled transportation theme, the Network aims to bring together the knowledge obtained through research projects funded by the RCUK Programme and other national and international cross-disciplinary research aimed at developing a "hydrogen" for transport economy. It will have a strong multi-disciplinary focus and aim to ensure engagement and knowledge transfer takes place across all modes of transport and hydrogen energy including technology, socio-economics, behavioural science and policy. The Network team will manage a £500k feasibility fund for cutting edge projects which also meet the wider objectives of facilitating collaboration and multi-disciplinary research.

Since 9-11 and 7-7, terrorism has been a major public concern. To ensure public safety and to protect the UK economy, research is needed that offers new methods to foil attacks before they are executed, to identify people and networks who might be preparing for or undertaking an attack, and to provide clear evidence that can be used to justify questioning, arrests and prosecutions. In this study, we will investigate whether deception can be identified and proved from 'scent trails', that is, coherent accounts of suspects' activities over time compiled from tracking their movements, communications and behaviours. We will develop software to derive inferences about what activities are consistent with suspects' scent trails and what are ruled out. These inferences will allow investigators to challenge suspects, both in real time (e.g., to encourage suspects to abandon an ongoing attack) and during interviews (e.g., to point out inconsistencies between a suspect's account and scent trail evidence that might change the course of an interview). The project will investigate scent trails in the context of people undertaking deceptive activities to gain advantage in adversarial 'treasure hunt'-type games. The games will be developed in consultation with stakeholders to provide a non-sensitive analogy to counter-terrorism contexts. Players, typically undergraduate students paid for participation, will be monitored during games via positional and communication data obtained from mobile devices enabled with geospatial positioning devices. Novel software for integrating these data will be developed to build up scent trails of players' activities during game play. Methods of artificial intelligence will be combined to derive inferences from the scent trails about what kinds of activity are possible and impossible given a player's location, trajectory, activities and links with others. We envision games with 3 teams: Team A represent the adversary, Team B the police or general public, and Team C the intelligence services. Team A scores points by visiting target locations within a time limit under a set of game rules that they must violate if they are to win. They must try to hide rule violations from Team B, who score points by preventing or identifying Team A's deceptions successfully. Team C can challenge Team A by sending them indications of the scent trails that are held or can feed Team B intelligence information. Moreover, the inferences from scent trails will support Team C in deciding how best to prove or falsify a suspicion during an interview with Team A players at key points during the games. By conducting observation of players during games, we can investigate how people change their behaviours when they are confronted with evidence that reveals their deceptions. We will also interview players at key points during games as a simulation of interviews with suspects, eliciting from players accounts of their activities before presenting them with challenges based on their own scent trails that are either consistent or inconsistent with legal game playing. This will allow interview and analysis techniques to be improved and will provide clues as to how people subsequently change their behaviour after they have been confronted with their deception. The results will also allow us to test between hypotheses deriving from forensic psychology as to how best to detect deception. The research also allows us to explore public awareness of, and response to, monitoring and surveillance in counter-terrorism. With an advisory panel of stakeholders and subject specialists representing key public and academic bodies, we will identify ethical and legal issues associated with collecting and using data on peoples' movements through public spaces. We will also conduct questionnaire studies with game players and others not involved in the games, to measure attitudes to monitoring and surveillance in game-playing and other contexts.