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Smart robots poised to boost green energy efficiency with wave prediction
Schematic representation of the proposed end-to-end protocol - the ROV receives predictive information relating to the oncoming wave from a measuring device upstream which the onboard control can exploit to compensate for future hydrodynamic disturbances. This information can be transmitted wireless to a base station and relayed to the ROV via a tether (communication option 1), or if operating untethered transmitted wireless directly underwater (communication option 2).
Smart robots poised to boost green energy efficiency with wave prediction
by Sophie Jenkins
Edinburgh, UK (SPX) Nov 15, 2024

Underwater robots capable of predicting real-time wave movements could significantly cut the costs of offshore renewable energy production, according to a new study.

Engineers from the University of Edinburgh have developed innovative computational and experimental methods that allow autonomous robots to function stably in turbulent seas. This advancement could make the maintenance of offshore wind farms and tidal turbines more efficient and safer, eliminating current challenges posed by erratic wave conditions that hinder machine stability and precision.

The study highlights how positioning autonomous robots offshore for routine maintenance tasks could reduce the typically higher costs of renewable energy compared to fossil fuel sources. Additionally, it could streamline operations that often rely on ships, helicopters, or complex lifting processes and minimize human exposure to hazardous offshore environments.

At the heart of this development are tools that help robots maintain stability even in unpredictable conditions. Tests were conducted in the University of Edinburgh's FloWave tank, utilizing wave data from the North Sea to simulate real-world conditions. This system includes wave-detecting instruments tethered to the seafloor that relay information about wave direction and height directly to nearby robots. These robots can then predict and counteract disturbances, maintaining a steady position and increasing operational reliability.

The findings show that this system works effectively for robots near the water's surface as well as at deeper levels, where wave impacts can still be significant. The researchers noted that the new technology marks an improvement over traditional control systems, which tend to respond too slowly to the rapid shifts in oceanic conditions, resulting in less dependable performance.

Dr. Kyle Walker, who spearheaded the development as part of his doctoral research, stated: "A major limitation at present is robots' ability to perceive and counteract environmental disturbances effectively, which fundamentally restricts the current use of small subsea vehicles. By forming a prediction of future wave disturbances and integrating this within the control system, we're able to expand this range with little to no change to the robot hardware. In terms of translating this technology into the field, this is a huge benefit and makes our system applicable to most vehicles currently available on the market."

Future efforts will focus on enhancing the precision of robots in performing complex tasks like rust detection or electrical repairs while maintaining a stable position. This research was supported by the Engineering and Physical Sciences Research Council and builds on the outcomes of the ORCA Hub project, a green energy initiative by Heriot-Watt University and the University of Edinburgh.

Dr. Francesco Giorgio-Serchi, the study leader from the University of Edinburgh's School of Engineering, emphasized: "Increasing the use of autonomous robots to help maintain offshore renewable installations could have a transformative effect on cutting the cost of producing clean energy. Advancing this technology further could help bring about a step change in the adoption of unmanned robots at sea and drastically increase the degree of automation in the offshore sector."

Research Report:Nonlinear model predictive dynamic positioning of a remotely operated vehicle with wave disturbance preview

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