Real-time 3D wind field prediction onboard UAVs for safe flight in complex terrain

Due to a lack of environment awareness, today's low-altitude fixed-wing Unmanned Aerial Vehicles (UAVs) are limited to primitively follow user-defined waypoints. All high-level decision making is still performed by a human user. Fully-autonomous remote missions in complex environments however require true environment awareness both with respect to terrain and wind. While terrain-aware navigation is covered in existing literature, the real-time estimation and consideration of complex wind patterns onboard UAVs is not. This paper therefore presents the literature's first-ever local 3D wind field prediction method which can run in real time onboard a UAV. The selected method is a simple downscaling approach which retrieves low-resolution data from global weather models and then adjusts the wind field via potential flow theory such that terrain boundaries, mass conservation, and the atmospheric stratification are observed. Typical 3D wind fields of 1 km³ volume are calculated in below 10 seconds. Synthetic test cases such as the flow around a semi-cylinder, through a valley and over a ramp yield good results. A comparison with 3D LIDAR wind data collected over 10 days in the Swiss Alps shows an overall wind error reduction of 23% with respect to the zero-wind assumption that is mostly used for UAV path planning today. Overall, our initial research demonstrates the feasibility of real-time 3D wind field prediction onboard a UAV. However, the focus on low computation time means that the vertical wind prediction lacks accuracy. The paper therefore ends with a research outlook into real-time 3D wind field prediction through the fusion of machine learning techniques with Computational Fluid Dynamics (CFD) methods.