Trajectory Planning for Offshore Wind Farm Logistics with Unmanned Aircraft

Currently, offshore wind farms are serviced by vessels and manned helicopters. In the future, unmanned helicopters are expected to enhance logistics by providing timely and costefficient delivery of necessary spare parts and tools. In order to efficiently scale up unmanned air traffic to offshore wind farms, unmanned aircraft should be capable of autonomous flight including the capability to plan and replan trajectories. In this work, the trajectory planning problem for unmanned helicopters in offshore logistics operations is examined in detail. Based on this analysis, efficient planning algorithms are proposed to tackle the key challenges: planning with respect to spatiotemporally varying winds and wind forecast data, respecting arrival time and round-trip fuel constraints, and runtime efficiency for onboard execution of the planning algorithm. The algorithmic approach is based on Probabilistic Roadmap Method (PRM) and variants of the A * graph search algorithm. A novel method for constraints prioritization is proposed for the A * algorithm. It allows for enforcing satisfaction of constraints if possible and intentionally violating constraints if required to calculate a feasible solution. The proposed algorithm is benchmarked in a realistic scenario for offshore wind farm logicists in the North Sea. Our results demonstrate the practicality and scalability of the proposed algorithm. Also, its advantages and disadvantages in comparison to alternative algorithms are discussed qualitatively. This work contributes towards the development and deployment of intelligent and autonomous unmanned helicopters for offshore wind farm logistics.