Maintenance of offshore wind farms requires the transportation of tools and spare parts in close coordination with the deployment of technicians and the cost-intensive shutdown of the wind turbines. In addition to ships and helicopters, drones are envisioned to support the offshore transportation system in the future. For cost-efficient and scalable offshore drone operations, autonomy is key to minimize the required infrastructure and personnel. In this work, we present a system architecture that integrates the key onboard capabilities for autonomous offshore drone operations: onboard mission and contingency management, en-route trajectory planning, robust flight control, safe landing, communication management, and runtime monitoring. We also present technical solutions for each of these capabilities and discuss their integration and interaction within the autonomy architecture. Furthermore, remaining challenges and the feasibility of autonomous drone operations for offshore wind farm cargo delivery are addressed, contributing to the realization of this vision in the near future. The work presented here summarizes the results of autonomous cargo drone operations within the UDW research project, a joint project between the German Aerospace Center (DLR) and the energy supplier EnBW.

The operation of Unmanned Aircraft (UA) in the European Union Aviation Safety Agency (EASA) specific category requires the operator to put risk-minimizing measures in place, where an operation poses a risk to third parties. A geocaging functionality contains the UA operation inside a pre-determined flight geography and defines boundaries, at which mitigative actions are triggered to ensure that the operation is returned to control or terminated safely. This paper focuses on the development of a computationally efficient geocaging algorithm with superior performance characteristics over the state-of-the-art. While the outermost boundaries of the operational volume are maintained, the presented approach leverages performance heuristics to adapt the geocage’s mitigation boundaries to the current vehicle state and direction of flight such that the operational volume is maximized and significantly faster velocities are achieved in narrow passages. Simulation results for flights in convex and concave geocage geometries, as well as benchmarks against a polygon scaling method are presented.