AI-powered underwater vehicle transforms offshore wind inspections

Image of a Beam ship for AI-powered underwater inspections at offshore wind farms.


Beam has deployed the world’s first AI-driven autonomous underwater vehicle for offshore wind farm inspections. The technology has already proved its mettle by inspecting jacket structures at Scotland’s largest offshore wind farm, Seagreen—a joint venture between SSE Renewables, TotalEnergies, and PTTEP.

The AI-powered vehicle represents a significant leap forward in marine technology and underwater robotics. Capable of conducting complex underwater inspections without human intervention, it promises to dramatically enhance efficiency and slash costs associated with underwater surveys and inspections.

Traditionally, offshore wind site inspections have been manual, labour-intensive processes. Beam’s autonomous solution offers a radical departure from this approach, enabling data to be streamed directly back to shore. This shift allows offshore workers to concentrate on more intricate tasks while reducing inspection timelines by up to 50%, resulting in substantial operational cost savings.

Brian Allen, CEO of Beam, said: “We are very proud to have succeeded in deploying the world’s first autonomous underwater vehicle driven by AI. Automation can revolutionise how we carry out inspection and maintenance of offshore wind farms, helping to reduce both costs and timelines.”

Beyond improved efficiency, Beam’s technology elevates the quality of inspection data and facilitates the creation of 3D reconstructions of assets alongside visual data. This deployment marks a crucial step in Beam’s roadmap for autonomous technology, with plans to extend this AI-driven solution across its fleet of DP2 vessels, ROVs, and autonomous underwater vehicles (AUVs) throughout 2025 and 2026.

“Looking ahead to the future, the potential of this technology is huge for the industry, and success in these initial projects is vital for us to progress and realise this vision. This wouldn’t be possible without forward-thinking customers like SSE Renewables who are willing to go on the journey with us,” explained Allen.

The Seagreen wind farm, operational since October 2023, is the world’s deepest fixed-bottom offshore wind farm. Beam’s project at Seagreen has provided crucial insights into the potential of autonomous technology for large offshore wind superstructures. The data collected by the AI-driven vehicle will support ongoing operational reliability at the site, offering valuable information on areas such as marine growth and potential erosion at the foundations.

Matthew Henderson, Technical Asset Manager – Substructure and Asset Lifecycle at SSE Renewables, commented: “At SSE, we have a mantra that ‘if it’s not safe, we don’t do it.’ Beam’s technology demonstrates that autonomous inspections can reduce the personnel we need to send offshore for planned inspections, while speeding up planned works and collecting rich data-sets to inform asset integrity planning.

“As we move further offshore, and into deeper waters, the ability to collect high-quality inspection data in a low-risk manner is imperative to us delivering our Net Zero Acceleration Programme.”

As Beam prepares to roll out its AI-driven inspection technology across its fleet in 2025 and 2026, this deployment aligns with the company’s mission to revolutionise offshore wind operations by making them more efficient and cost-effective—further supporting the global energy transition.

The success of this AI-powered underwater vehicle at Seagreen wind farm not only demonstrates the potential of autonomous technology in offshore wind inspections but also sets a new standard for safety, efficiency, and data quality in the industry. Such innovations will play a crucial role in ensuring the sustainability and cost-effectiveness of offshore wind energy.

See also: Hugging Face is launching an open robotics project

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Tags: ai, artificial intelligence, autonomous vehicle, beam, energy, environment, offshore wind, robotics, seagreen, sustainability, wind farm



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