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

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
$56\%$
).