Principal Investigator Nicholas Roy
With unmanned aerial vehicles (UAVs) becoming more prolific and capable, and regulations evolving, their eventual operation in urban environments seems all but certain. As UAVs begin to fly in these environments, they will be presented with a host of unique challenges. One of these challenges will be the complex wind fields generated by urban structures and terrain. Although much effort has been directed towards developing planning and estimation strategies for wind fields at high altitudes or in large open spaces, these approaches contain an implicit assumption that the wind field evolves over relatively large temporal and spatial scales. Given this simplification, a history of local measurements can be used to estimate the global wind field with sufficient accuracy. However, urban wind fields are highly variant in both space and time and are therefore resistant to this estimation method and require an approach that models the complex interaction between the flow and surrounding environment.
The approach is to use prevailing wind estimates from local weather stations and a 3D model of the surrounding environment as inputs to a computational fluid dynamics solver to obtain both steady and unsteady wind field estimates. Unlike many approaches, these wind field estimates account for the strong coupling between the wind flow and nearby structures. Once obtained, these wind field estimates can be used to find minimum-energy trajectories between points of interest. Further work hopes to leverage a library of precomputed wind fields to find a wind field covariance estimate within a region. This uncertainty estimate could be used to infer a global wind field from local measurements, or predict future wind conditions.