TY - JOUR
T1 - A Review: Challenges and Opportunities for Artificial Intelligence and Robotics in the Offshore Wind Sector
AU - Mitchell, Daniel
AU - Blanche, Jamie
AU - Harper, Sam
AU - Lim, Theodore
AU - Gupta, Ranjeetkumar
AU - Zaki, Osama
AU - Tang, Wenshuo
AU - Robu, Valentin
AU - Watson, Simon
AU - Flynn, David
N1 - Funding Information:
This work was supported in part by the Offshore Robotics for Certification of Assets (ORCA) Hub under EPSRC Project EP/R026173/1 and EPSRC Holistic Operation and Maintenance for Energy (HOME) for offshore wind farms.
Publisher Copyright:
© 2022
PY - 2022/5
Y1 - 2022/5
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Autonomous systems
KW - Digitalization
KW - Offshore renewable energy
KW - Offshore wind farms
KW - Robotics
UR - http://www.scopus.com/inward/record.url?scp=85124974008&partnerID=8YFLogxK
U2 - 10.1016/j.egyai.2022.100146
DO - 10.1016/j.egyai.2022.100146
M3 - Article
SN - 2666-5468
VL - 8
JO - Energy and AI
JF - Energy and AI
M1 - 100146
ER -
