he real-world application of Micro Air Vehicles (MAVs) is often constrained by their limited flight range and endurance, primarily due to battery limitations. One way to overcome this challenge is by leveraging naturally occurring vertical air currents, a technique inspired by soaring birds. This paper introduces Osprey Simulator, a framework designed to simulate and test energy-efficient soaring flight strategies for fixed-wing drones. Built on Isaac Sim, Osprey Simulator enables the creation of both randomly generated urban environments and real-world environments, such as the TU Delft campus. These environments are paired with accurate wind field simulations using OpenFOAM, providing a robust platform for studying aerodynamic interactions in diverse settings. This functionality allows for the generation of scalable synthetic datasets, including depth images and wind field information, enabling comprehensive exploration of soaring potential across various structural geometries and wind conditions. Using generated synthetic data, a neural network was trained to predict optimal soaring regions by analyzing depth images and wind field information. The network demonstrates the ability to identify updraft and downdraft regions, enabling more efficient path planning for drones in urban environments. By integrating realistic simulations and advanced predictive models, Osprey Simulator serves as a powerful tool for advancing autonomous soaring and extending the operational range of fixed-wing MAVs.

The demand for renewable energy has increased significantly over the past years. During the last decades it became clear that the main source of renewable energy will be wind energy. The wind turbines, still growing in size every year, are almost all located in either remote rural or offshore areas. However, there lies a huge opportunity in imple-menting wind harvest systems in urban areas.