SO(2)-Equivariant Downwash Models for Close Proximity Flight
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866929288203206656 |
|---|---|
| author | Smith, H. Shankar, A. Gielis, J. Blumenkamp, J. Prorok, A. |
| author_facet | Smith, H. Shankar, A. Gielis, J. Blumenkamp, J. Prorok, A. |
| contents | 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%). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_18983 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | SO(2)-Equivariant Downwash Models for Close Proximity Flight Smith, H. Shankar, A. Gielis, J. Blumenkamp, J. Prorok, A. Robotics Artificial Intelligence Machine Learning 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%). |
| title | SO(2)-Equivariant Downwash Models for Close Proximity Flight |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2305.18983 |