A Spatiotemporal Downwash Modeling for Agile Close-Proximity Multirotor Flight

Abstract

Accurate aerodynamic interaction modeling in multi-drone tasks is crucial for enhancing system stability and efficiency, especially when facing major disturbances from downwash wake effects. Conventional data-driven and empirical models mainly address simplified cases where one drone hovers or all vehicles have low absolute and relative velocities (≤ 0.5 m/s), and rely merely on relative states. In this study, we use high-fidelity Computational Fluid Dynamics (CFD) simulations to explore quadrotor interactions at higher speeds (0.5-4.0 m/s). We find that as the absolute velocities of the UAVs rise, downwash effects change significantly. To account for these discrepancies, we present a data-driven model considering both the absolute and relative properties of the downwash problem. We propose a geometric deep neural network predictor and compare its performance with existing data-driven and empirical models. Validations on two quadrotor settings show that our model gives more reliable predictions in tough scenarios and performs better in training without rigorous fine-tuning. Finally, we combine our predictor with a nonlinear feedback controller to enhance flight control under downwash disturbances. However, we encounter limitations for our speed ranges during trajectory tracking such as delays and velocity loss. Despite these challenges, our encoding and prediction method shows to be a promising step to address the downwash effects at higher speeds.We release our dataset, method, and re-implementations at: https://github.com/pavelkharitenko/flare-dw.