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

• URL: http://arxiv.org/abs/2305.18983v3
• Date: Mon, 25 Mar 2024 20:21:25 GMT
• Title: SO(2)-Equivariant Downwash Models for Close Proximity Flight
• Authors: H. Smith, A. Shankar, J. Gielis, J. Blumenkamp, A. Prorok,
• Abstract summary: 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.
• Score: 6.297269227845377
• License: http://creativecommons.org/licenses/by/4.0/
• 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%).

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