Human-Robot Teaming for Long-Term Operations and Maintenance (O&M) of Offshore Renewable Energy Devices

The main aim of this interdisciplinary program is to develop a framework based on Deep Learning and Swarm Optimization for the coordination and cooperation of a fleet of Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (USVs) for long-term Operation and Maintenance (O&M) of offshore wind farms.

O&M activities make up almost a quarter of the lifetime costs of an offshore wind project. The UK has the largest installed capacity of offshore wind generating around 7.2GW capacity. Therefore, reducing the cost of electricity production by offshore wind is extremely high on the Government's agenda.

In the research and innovation area the main objectives of the program are to (1) limit human intervention for blade inspections preventing human exposure to dangerous risks, reducing the cost of the intervention and speeding up the process; (2) minimize the amount of time needed to inspect an offshore asset to reduce the inactivity period of the installation; (3) provide an accurate analysis of the conditions to reduce the cost of the maintenance process, and, (4) provide an alternative method for risk assessment which would reduce the costs incurred by the renewable energy projects for risk management services.

The program exploits the cutting-edge facilities offered by the University of Plymouth, the Levenmouth Demonstration Turbine of ORE Catapult platform at the Robotics and Autonomous Systems (RAS) testing and validation facilities of ORE Catapult and the National Nuclear User Facility for Hot Robotics in Bristol. The team also envisages a collaboration with Thales and Applied Automation Ltd (Plymouth).