There are many hostile working environments that require sophisticated tasks to be performed, such as the building of structures and deployment of tools where there is significant risk to the health and safety of any manual workers involved, high cost of deployment and significant timescales for completion. Examples are Nuclear Decommissioning, Oil and Gas, Mining and Space systems. Common to many of these environments is the extreme difficulty of effective deployment of the sophisticated kind of equipment that replaces human beings. These environments present the following challenges:

• Hazardous working environments requiring protective equipment and limited time windows for operation
• Limited access through which to deploy the systems
• Unstable structures present that prevent occupation
• Lifting of heavy objects (~50kg) that require mechanical assistance
• Processing of large volumes of liquids (1000s litres)

The MIMRee project will introduce a step-change in the Operations and Maintenance of offshore wind farms by removing humans from the loop during the inspection, maintenance and repair (IMR) of offshore wind turbine blades. The aim is to significantly reduce the costs and turbine downtime associated with these tasks and reduce the H&S risks of using rope access technicians. In this project, the multi autonomous platform approach will be demonstrated for a use case in offshore renewables; however, the developed autonomous vehicle surface vessel hub, Human-Machine Interface, robotic teaming and communications, and automated mission planning will also have applications in the offshore Oil & Gas, Search and Rescue and Defense sectors. The key objectives are:

• Remove the need to send humans offshore to carry out wind turbine blade IMR tasks
• Remove the need to shut wind turbines down to carry out blade inspections
• Reduce the risk of using autonomous vehicles offshore to carry out asset IMR tasks
• Safely demonstrate a fully autonomous approach to blade IMR tasks in real-world operating conditions
• Establish the business case for using autonomous vehicles for blade IMR
• Develop a roadmap for transferring the MIMRee system to other relevant industries

The Prometheus project will develop a fully autonomous robot capable of geo-technical surveys in unknown voids for use in the mining, water infrastructure monitoring and offshore industries. This robot will be able to be automatically deployed and recovered through a standard restricted access bore of 150mm diameter, significantly increasing potential use cases over existing systems. The joint requirements of fully autonomous operation beyond visual line of sight (BVLOS) combined with deployment through a limited access 150-diameter borehole will be demonstrated both in a university lab environment and at key milestone demonstrations in conjunction with Network Rail. Further applications are within the water industry with aging water infrastructure. Prometheus will be an excellent illustration of robotics, autonomy and AI in extreme environments with widespread applications. The final system will demonstrate a step change in autonomous capability, highly flexible operation and deployment, meeting a real and existing industrial need for rapid inspection of areas that are difficult to access and complex to navigate.

The overarching objective of this project is to endow autonomous systems with advanced decision-making capabilities and collaboration skills. We aim to build artificial systems that, in real-world environments, are capable of reasoning about high-level goals specified by human operators and formulating, in collaboration with them, a course of actions to successfully achieve such goals. Strategic reasoning and fluid teaming are fundamental skills of cognitive systems: they are needed in a variety of situations, from day-to-day tasks such as assisting humans in household chores, to extreme missions, such as space exploration. The techniques that we propose are general and can be used to support both robotic systems and software agents. We choose disaster response operations where unmanned aerial vehicles (UAVs) assist emergency responders as our demonstration arena. In this domain, in fact, it is crucial for the UAVs to think strategically to pursue goals efficiently and to act in concert with the human operators who are ultimately in charge of critical decisions.

Search and tracking is the problem of locating a moving target and following it to its destination. In this project, we consider a scenario in which the target moves across a large geographical area by following a road network and the search is performed by a team of coordinated unmanned aerial vehicles (UAVs). We formulate search and tracking as a combinatorial optimisation problem and explore different optimisation techniques to solve it, from Greedy Algorithms to AI Planning and Constraint Programming (CP). We also study the formal properties of the objective function, which has lead us into an investigation of submodularity and how this feature can be exploited to improve our algorithms. Via an extensive experimental evaluation, we study the scalability of the different techniques and identifies the conditions under which one approach becomes preferable to the others.