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Today, low-altitude fixed-wing Unmanned Aerial Vehicles (UAVs) are largelylimited to primitively follow user-defined waypoints. To allow fully-autonomousremote missions in complex environments, real-time environment-aware navigationis required both with respect to terrain and strong wind drafts. This paperpresents two relevant initial contributions: First, the literature's first-ever3D wind field prediction method which can run in real time onboard a UAV ispresented. The approach retrieves low-resolution global weather data, and usespotential flow theory to adjust the wind field such that terrain boundaries,mass conservation, and the atmospheric stratification are observed. Acomparison with 1D LIDAR data shows an overall wind error reduction of 23% withrespect to the zero-wind assumption that is mostly used for UAV path planningtoday. However, given that the vertical winds are not resolved accuratelyenough further research is required and identified. Second, a sampling-basedpath planner that considers the aircraft dynamics in non-uniform winditeratively via Dubins airplane paths is presented. Performance optimizations,e.g. obstacle-aware sampling and fast 2.5D-map collision checks, render theplanner 50% faster than the Open Motion Planning Library (OMPL) implementation.Test cases in Alpine terrain show that the wind-aware planning performs up to50x less iterations than shortest-path planning and is thus slower in lowwinds, but that it tends to deliver lower-cost paths in stronger winds. Moreimportantly, in contrast to the shortest-path planner, it always deliverscollision-free paths. Overall, our initial research demonstrates thefeasibility of 3D wind field prediction from a UAV and the advantages ofwind-aware planning. This paves the way for follow-up research onfully-autonomous environment-aware navigation of UAVs in real-life missions andcomplex terrain.

Towards Fully Environment-Aware UAVs: Real-Time Path Planning with Online 3D Wind Field Prediction in Complex Terrain