Learning Local Urban Wind Flow Fields From Range Sensing

Obtaining accurate and timely predictions of the wind through an urban environment is a challenging task, but has wide-ranging implications for the safety and efficiency of autonomous aerial vehicles in future urban airspaces. Prior work relies strongly on global information about the environment, such as a precise map of the city and in-situ wind measurements at various locations, to run expensive computational fluid dynamics solvers to predict the entire wind flow field. In contrast, this letter introduces a new method to estimate the wind flow field in a region around the robot in real time, utilizing on-board range measurements to sense nearby buildings and sparse wind measurements to infer windspeed and direction. We propose that this information sufficiently characterizes the structure of the wind flow field in the local region of interest. To that end, we introduce a deep learning-based approach to predict local flow fields from range measurements. Our results indicate that a neural network trained on numerous simulated winds through small randomized maps is capable of reconstructing local wind flows while generalizing to larger environments with over 200 buildings. This contribution empowers computationally-constrained aerial robots to reason about the structure of local wind flow fields, thereby enabling new planning, control, and estimation strategies in windy urban environments without
a priori
knowledge of the map.