Dipartimento di Ingegneria informatica, automatica e gestionale

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, multi-robot solution for wind farm O&M.