Abstract
With the fast development of offshore wind farms as renewable energy sources, maintaining them efficiently and safely becomes necessary. The high costs of operation and maintenance (O&M) are due to the length of turbine downtime and the logistics for human technician transfer. To reduce such costs, we propose a comprehensive multi-robot system that includes unmanned aerial vehicles (UAV), autonomous surface vessels (ASV), and inspection-and-repair robots (IRR). Our system, which is capable of co-managing the farms with human operators located onshore, brings down costs and significantly reduces the Health and Safety (H&S) risks of O&M by assisting human operators in performing dangerous tasks. In this paper, we focus on using AI temporal planning to coordinate the actions of the different autonomous robots that form the multi-robot system. We devise a new, adaptive planning approach that reduces failures and replanning by performing data-driven goal and domain refinement. Our experiments in both simulated and real-world scenarios prove the effectiveness and robustness of our technique. The success of our system marks the first-step towards a large-scale, multirobot solution for wind farm O&M.
Original language | English |
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Pages (from-to) | 15782-15788 |
Number of pages | 7 |
Journal | Proceedings of the AAAI Conference on Artificial Intelligence |
Volume | 37 |
Issue number | 13 |
Early online date | 26 Jun 2023 |
DOIs | |
Publication status | Published - 26 Jun 2023 |
Event | AAAI-23: The 37th AAAI Conference on Artificial Intelligence - Walter E. Washington Convention Center, Washington, United States Duration: 7 Feb 2023 → 14 Feb 2023 Conference number: 37th https://aaai.org/Conferences/AAAI-23/ |
Bibliographical note
This study has received funding from UKRI through the Innovate UK Grant Agreement No. 104821 and EPSRC Grant EP/R026084/1.Keywords
- PDDL
- adaptive planning
- multi-agent planning
- extreme environments