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A review: Challenges and opportunities for artificial intelligence and robotics in the offshore wind sector
Open accessAbstract
The UK has set plans to increase offshore wind capacity from 22GW to 154GW by 2030. With such tremendous growth, the sector is now looking to Robotics and Artificial Intelligence (RAI) in order to tackle lifecycle service barriers as to support sustainable and profitable offshore wind energy production. Today, RAI applications are predominately being used to support short term objectives in operation and maintenance. However, moving forward, RAI has the potential to play a critical role throughout the full lifecycle of offshore wind infrastructure, from surveying, planning, design, logistics, operational support, training and decommissioning. This paper presents one of the first systematic reviews of RAI for the offshore renewable energy sector. The state-of-the-art in RAI is analyzed with respect to offshore energy requirements, from both industry and academia, in terms of current and future requirements. Our review also includes a detailed evaluation of investment, regulation and skills development required to support the adoption of RAI. The key trends identified through a detailed analysis of patent and academic publication databases provide insights to barriers such as certification of autonomous platforms for safety compliance and reliability, the need for digital architectures for scalability in autonomous fleets, adaptive mission planning for resilient resident operations and optimization of human machine interaction for trusted partnerships between people and autonomous assistants. Our study concludes with identification of technological priorities and outlines their integration into a new ‘symbiotic digital architecture’ to deliver the future of offshore wind farm lifecycle management.
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“Substantial upfront CAPEX for AI infrastructure, data systems, sensors, and specialized hardware/software is a primary hurdle ( [28,29].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [28]
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“Energy systems are safety-critical.AI failures can have catastrophic consequences [28,35].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [28]
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Metadata
- Authors
- Mitchell, D., Blanche, J., Harper, S., Lim, T., Gupta, R., Zaki, O., Tang, W., Robu, V., Watson, S., & Flynn, D.
- Affiliations
- Heriot-Watt University (GB)
- Centrum Wiskunde & Informatica (NL)
- University of Manchester (GB)
- Corresponding author
- Mitchell, Daniel
- ORCID
- Venue
- Energy and AI
- Publisher
- Elsevier BV
- Published
- 2022-05
- Volume
- 8
- Pages
- 100146
- ISSN
- 2666-5468 (ISSN-L)
- OpenAlex
- W4200630024
- Cited by
- 114
- References
- 56
- Citations by year
- 2026: 20 · 2025: 40 · 2024: 24 · 2023: 21 · 2022: 9
- FWCI
- 10.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Energy and AI (publishedVersion, cc-by)
- Research Explorer (The University of Manchester) (publishedVersion, cc-by)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
- arXiv (Cornell University) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Research Repository (Delft University of Technology) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- Offshore Robotics for Certification of Assets (EP/R026173/1)
- Engineering and Physical Sciences Research Council (EP/R026173/1, EP/P009743/1, EP/R026173)
- Topics
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Wind Energy Research and Development
- Physical Sciences › Engineering › Ocean Engineering › Wave and Wind Energy Systems
- Physical Sciences › Engineering › Ocean Engineering › Offshore Engineering and Technologies
- Keywords
- artificial intelligence, robotics, offshore wind energy, operation and maintenance, autonomous platforms, human-machine interaction, digital architecture, skill development
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