UNITE (Underwater Intervention for Offshore Renewable Energies)
The offshore environment is widely recognised as one of the most hazardous and challenging places for humans to work. Harsh weather, unpredictable sea conditions, and limited accessibility all contribute to a high-risk operating context. In the UK alone, there are currently more than 11,000 offshore wind assets, with thousands more planned by 2050 to support the transition to clean energy and meet legally binding net zero targets. As the scale of offshore infrastructure continues to grow, so too does the need for efficient, safe, and sustainable methods of inspection and maintenance.
To meet regulatory requirements, offshore energy companies are obligated to inspect and maintain these assets at least three times a year. Traditionally, this work is carried out by trained human crews deployed on large support vessels. These teams are responsible for visually assessing, repairing, and servicing complex structures such as wind turbines, subsea cables, and oil and gas platforms.
Operating in such extreme environments does not only pose significant risks to human safety, but it also comes at a substantial financial and environmental cost. Maintenance activities can account for as much as 30% of total offshore wind farm operating expenses, largely due to vessel fuel consumption, staffing requirements, and logistical complexities.
To address these challenges, researchers at The National Robotarium partnered with Fugro, a global leader in geo-data solutions, to deliver the UNITE (Underwater Intervention for Offshore Renewable Energies) project. Supported by £1.4 million in funding from the Engineering and Physical Sciences Research Council (EPSRC), the project set out to develop advanced autonomous systems capable of performing inspection, maintenance, and repair tasks underwater. Central to this initiative is the development of electric remotely operated vehicles (eROVs), designed to provide a safer and more environmentally friendly alternative to traditional methods.