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%).
@article{arxiv.2305.18983,
title = {SO(2)-Equivariant Downwash Models for Close Proximity Flight},
author = {H. Smith and A. Shankar and J. Gielis and J. Blumenkamp and A. Prorok},
journal= {arXiv preprint arXiv:2305.18983},
year = {2024}
}