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Image-based attitude determination of co-orbiting satellites using deep learning technologies

Image-based attitude determination of co-orbiting satellites using deep learning technologies
Image-based attitude determination of co-orbiting satellites using deep learning technologies
Active debris removal missions pose demanding guidance, navigation and control requirements. We present a novel approach which adopts deep learning technologies to the problem of attitude determination of an uncooperative debris satellite of an a-priori unknown geometry. A siamese convolutional neural network is developed, which detects and tracks inherently useful landmarks from sensor data, after training upon synthetic datasets of visual, LiDAR or RGB-D data. The method is capable of real-time performance while improving upon conventional computer vision-based approaches, and generalises well to previously unseen object geometries, enabling this approach to be a feasible solution for safely performing guidance and navigation in active debris removal, satellite servicing and other close proximity operations. The performance of the algorithm, its sensitivity to model parameters and its robustness to illumination and shadowing conditions, are analysed via numerical simulation.
Active debris removal, Deep learning, Image processing, Spacecraft attitude determination
1270-9638
Guthrie, Benjamin, Felix
03652589-afe9-4d34-a4fd-831cb1562c73
Kim, Minkwan
18ed9a6f-484f-4a7c-bf24-b630938c1acc
Urrutxua, Hodei
b419b029-53d7-41aa-bd77-117ec584a972
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Guthrie, Benjamin, Felix
03652589-afe9-4d34-a4fd-831cb1562c73
Kim, Minkwan
18ed9a6f-484f-4a7c-bf24-b630938c1acc
Urrutxua, Hodei
b419b029-53d7-41aa-bd77-117ec584a972
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9

Guthrie, Benjamin, Felix, Kim, Minkwan, Urrutxua, Hodei and Hare, Jonathon (2022) Image-based attitude determination of co-orbiting satellites using deep learning technologies. Aerospace Science and Technology, 120, [107232]. (doi:10.1016/j.ast.2021.107232).

Record type: Article

Abstract

Active debris removal missions pose demanding guidance, navigation and control requirements. We present a novel approach which adopts deep learning technologies to the problem of attitude determination of an uncooperative debris satellite of an a-priori unknown geometry. A siamese convolutional neural network is developed, which detects and tracks inherently useful landmarks from sensor data, after training upon synthetic datasets of visual, LiDAR or RGB-D data. The method is capable of real-time performance while improving upon conventional computer vision-based approaches, and generalises well to previously unseen object geometries, enabling this approach to be a feasible solution for safely performing guidance and navigation in active debris removal, satellite servicing and other close proximity operations. The performance of the algorithm, its sensitivity to model parameters and its robustness to illumination and shadowing conditions, are analysed via numerical simulation.

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Submitted date: 23 June 2021
Accepted/In Press date: 16 November 2021
e-pub ahead of print date: 19 November 2021
Published date: January 2022
Additional Information: Funding Information: This work was supported by the EPSRC Centre for Doctoral Training in Next Generation Computational Modelling Grant No. EP/L015382/1 . The authors acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work. Hodei Urrutxua wishes to acknowledge funding from grant PID2020-112576GB-C22 of the Spanish State Research Agency and the European Regional Development Fund , as well as from grant F663-AAGNCS by the “Dirección General de Investigación e Innovación Tecnológica, Consejería de Ciencia, Universidades e Innovación, Comunidad de Madrid” and “Universidad Rey Juan Carlos” . Funding Information: This work was supported by the EPSRC Centre for Doctoral Training in Next Generation Computational Modelling Grant No. EP/L015382/1. The authors acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work. Hodei Urrutxua wishes to acknowledge funding from grant PID2020-112576GB-C22 of the Spanish State Research Agency and the European Regional Development Fund, as well as from grant F663-AAGNCS by the ?Direcci?n General de Investigaci?n e Innovaci?n Tecnol?gica, Consejer?a de Ciencia, Universidades e Innovaci?n, Comunidad de Madrid? and ?Universidad Rey Juan Carlos?. Publisher Copyright: © 2021 Elsevier Masson SAS
Keywords: Active debris removal, Deep learning, Image processing, Spacecraft attitude determination

Identifiers

Local EPrints ID: 451306
URI: http://eprints.soton.ac.uk/id/eprint/451306
ISSN: 1270-9638
PURE UUID: a618ba18-6254-406b-8f3c-0962f6dfc7b8
ORCID for Benjamin, Felix Guthrie: ORCID iD orcid.org/0000-0001-7513-1521
ORCID for Minkwan Kim: ORCID iD orcid.org/0000-0002-6192-312X
ORCID for Jonathon Hare: ORCID iD orcid.org/0000-0003-2921-4283

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Date deposited: 20 Sep 2021 16:32
Last modified: 17 Mar 2024 06:46

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Contributors

Author: Benjamin, Felix Guthrie ORCID iD
Author: Minkwan Kim ORCID iD
Author: Hodei Urrutxua
Author: Jonathon Hare ORCID iD

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