Improving autonomous guidance using machine learning technologies
Improving autonomous guidance using machine learning technologies
Active debris removal missions pose demanding requirements on the visual guidance system. We investigate the potential applications of machine learning technologies to solve some of the remaining challenges for these missions. A novel method of attitude determination of an unknown and uncooperative debris satellite is presented, which adopts machine learning technologies to detect and track inherently useful image landmarks. We then apply image segmentation and object detection approaches to this domain and demonstrate their advantages. The performance of the algorithms are analysed via numerical simulation and compared with conventional approaches. In order to facilitate further research into the applications of machine learning for visual guidance in space, we make available a simulation framework which is capable of generating realistic image data simulating the relative motion between co-orbiting satellites.
Guthrie, Ben
03652589-afe9-4d34-a4fd-831cb1562c73
Guthrie, Ben
03652589-afe9-4d34-a4fd-831cb1562c73
Guthrie, Ben
(2021)
Improving autonomous guidance using machine learning technologies.
Stardust-R – Second Global Virtual Workshop, Online.
13 - 17 Sep 2021.
4 pp
.
(Submitted)
Record type:
Conference or Workshop Item
(Other)
Abstract
Active debris removal missions pose demanding requirements on the visual guidance system. We investigate the potential applications of machine learning technologies to solve some of the remaining challenges for these missions. A novel method of attitude determination of an unknown and uncooperative debris satellite is presented, which adopts machine learning technologies to detect and track inherently useful image landmarks. We then apply image segmentation and object detection approaches to this domain and demonstrate their advantages. The performance of the algorithms are analysed via numerical simulation and compared with conventional approaches. In order to facilitate further research into the applications of machine learning for visual guidance in space, we make available a simulation framework which is capable of generating realistic image data simulating the relative motion between co-orbiting satellites.
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Submitted date: 13 September 2021
Venue - Dates:
Stardust-R – Second Global Virtual Workshop, Online, 2021-09-13 - 2021-09-17
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Local EPrints ID: 483588
URI: http://eprints.soton.ac.uk/id/eprint/483588
PURE UUID: a736ab22-370d-4158-bd23-43f04bd8a301
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Date deposited: 02 Nov 2023 04:25
Last modified: 16 Mar 2024 14:51
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Author:
Ben Guthrie
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