SO(2)-Equivariant Downwash Models for Close Proximity Flight

Henry Smith, Ajay Shankar, Jennifer Gielis, Jan Blumenkamp, Amanda Prorok

Abstract: Multirotors flying in close proximity induce aerodynamic wake effects on each other through propeller downwash. Conventional methods have fallen short of providing adequate 3D force-based models that can be incorporated into robust control paradigms for deploying dense formations. Thus, learning a model for these downwash patterns presents an attractive solution. In this paper, we present a novel learning-based approach for modelling the downwash forces that exploits the latent geometries (i.e. symmetries) present in the problem. We demonstrate that when trained with only $5$ minutes of real-world flight data, our geometry-aware model outperforms state-of-the-art baseline models trained with more than $15$ minutes of data. In dense real-world flights with two vehicles, deploying our model online improves 3D trajectory tracking by nearly $36\%$ on average (and vertical tracking by $56\%$ ).