Wind and the City: Utilizing UAV-Based In-Situ Measurements for Estimating Urban Wind Fields

Jay Patrikar, Brady G. Moon, and Sebastian Scherer
Published In the proceedings of 2020 International Conference on Intelligent Robots and Systems International Conference on Unmanned Aircraft Systems (IROS), 2020



A high-quality estimate of wind fields can potentially improve the safety and performance of Unmanned Aerial Vehicles (UAVs) operating in dense urban areas. Computational Fluid Dynamics (CFD) simulations can help provide a wind field estimate, but their accuracy depends on the knowledge of the distribution of the inlet boundary conditions. This paper provides a real-time methodology using a Particle Filter (PF) that utilizes wind measurements from a UAV to solve the inverse problem of predicting the inlet conditions as the UAV traverses the flow field. A Gaussian Process Regression (GPR) approach is used as a surrogate function to maintain the real-time nature of the proposed methodology. Real-world experiments with a UAV at an urban test-site prove the efficacy of the proposed method. The flight test shows that the 95% confidence interval for the difference between the mean estimated inlet conditions and mean ground truth measurements closely bound zero, with the difference in mean angles being between -3.680 degrees and 1.250 degrees and the difference in mean magnitudes being between -0:206 m/s and 0:020 m/s.