Deep learning for spacecraft guidance, navigation, and control

Khoroshylov, S.V.; Redka, M.O.

doi:10.15407/knit2021.06.038

Cited by 5 publications

(3 citation statements)

References 41 publications

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“…Research [14] follows the modern tendency to use artificial intelligence methods in space appli-cations [15] and determine the ion beam force utilizing deep learning techniques. According to the results of this paper, the designed model can provide admissible accuracy but requires good estimates of the position and orientation of the SDO.…”

Section: Research and Engineering Innovation Projects Of The National...mentioning

confidence: 99%

Convolutional Neural Networks for Determining the Ion Beam Impact on a Space Debris Object

REDKA,

KHOROSHYLOV

2023

Sci. innov.

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Introduction. Space debris is a serious problem that significantly complicates space activity. This problem can be mitigated by active space debris removal. The ion beam shepherd (IBS) concept assumes the contactless removal of a space debris object (SDO) by the plume of an ion thruster (IT). Techniques for determining the force impact from the IT to the SDO are of crucial importance for implementing the IBS concept.Problem Statement. A launcher’s upper stage, approximated by a cylinder, is considered an SDO deorbited by the plume of the IT. The SDO can change its orientation and position relative to the shepherd satellite. The shepherd satellite shall be able to determine the force transmitted to the SDO by the IT, using only SDO’s images as the input information.Purpose. The study aims to develop a neural net model that can map an SDO image to the force transmitted by an IT plume to this object and estimate the accuracy of such models.Material and Methods. Plasma physics methods are used to obtain ground truth values of the ion beam force. The deep learning methodology is applied to create neural net models.Results. Three different approaches for end-to-end ion force determination have been investigated. The first model uses a single convolutional neural net (CNN). The second model is an ensemble network consisting of four sub-models, and a classifier is used to pick the correct sub-model. The last model is similar to the first one but is trained on all images used for the second model. After training, all three models’ accuracy and computational complexity are estimated. These estimates demonstrate the acceptable performance of CNN-based models.Conclusions. This paper demonstrates that CNNs can be used to determine the force impact without knowledge about the SDO position and orientation and significantly faster than the previous methods.

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Section: Research and Engineering Innovation Projects Of The National...mentioning

confidence: 99%

Convolutional Neural Networks for Determining the Ion Beam Impact on a Space Debris Object

REDKA,

KHOROSHYLOV

2023

Sci. innov.

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“…In recent years, with the continuous advancement of deep learning, neural networks have emerged as the predominant approach in the field of object detection [6]. The application in the aerospace domain holds the potential to expedite the intelligent evolution of space situational awareness [7][8][9]. However, the formidable cost and complexity associated with space data collection have led to the generation of most space target datasets through model simulations, overlooking the intricacies of real-space imaging [10].…”

Section: Introductionmentioning

confidence: 99%

TYCOS: A Specialized Dataset for Typical Components of Satellites

Bian,

Cao,

Zhang

et al. 2024

Applied Sciences

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The successful detection of key components within satellites is a crucial prerequisite for executing on-orbit capture missions. Due to the inherent data-driven functionality, deep learning-based component detection algorithms rely heavily on the scale and quality of the dataset for their accuracy and robustness. Nevertheless, existing satellite image datasets exhibit several deficiencies, such as the lack of satellite motion states, extreme illuminations, or occlusion of critical components, which severely hinder the performance of detection algorithms. In this work, we bridge the gap via the release of a novel dataset tailored for the detection of key components of satellites. Unlike the conventional datasets composed of synthetic images, the proposed Typical Components of Satellites (TYCOS) dataset comprises authentic photos captured in a simulated space environment. It encompasses three types of satellite, three types of key components, three types of illumination, and three types of motion state. Meanwhile, scenarios with occlusion in front of the satellite are also taken into consideration. On the basis of TYCOS, several state-of-the-art detection methods are employed in rigorous experiments followed by a comprehensive analysis, which further enhances the development of space scene perception and satellite safety.

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“…At this time, artificial intelligence methods attract a great interest of researches and practitioners all over the world, which is largely because of the impressive results obtained using deep learning (DL) techniques. DL has rapidly evolved and showed promising results in solving complex tasks, finding non-trivial solutions of existing problems [1].…”

mentioning

confidence: 99%

Deep Learning for Space Debris Removal

Serhii,

Mikhailo

2024

ITMM

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The advances in deep learning have revolutionized the field of artificial intelligence. These advances, as well as new tasks and requirements in space exploration, have led to an increased interest in these deep learning methods among space scientists and practitioners. The problems of controlling the attitude and relative motion of spacecraft are considered for both traditional and new missions, such as contactless space debris removal. Both supervised and reinforcement learning is used to solve such problems based on various architectures of artificial neural networks, including convolutional ones. The possibility of using deep learning together with methods of control theory is analyzed to solve the considered problems more efficiently. The difficulties that limit the application of these methods for space applications are highlighted. The necessary research directions for solving these problems are indicated.

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Deep learning for spacecraft guidance, navigation, and control

Cited by 5 publications

References 41 publications

Convolutional Neural Networks for Determining the Ion Beam Impact on a Space Debris Object

Convolutional Neural Networks for Determining the Ion Beam Impact on a Space Debris Object

TYCOS: A Specialized Dataset for Typical Components of Satellites

Deep Learning for Space Debris Removal

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