Determination of the Force Impact of an Ion Thruster Plume on an Orbital Object via Deep Learning

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

doi:10.15407/knit2022.05.015

Cited by 5 publications

(3 citation statements)

References 14 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%

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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%

“…The ground truth forces are calculated based on the methodology used for the similar purpose in [14]. According to this methodology the IT plume is a stream of heavy propellant ions (for example, xenon), accelerated to an energy level of several kilo electron-volts.…”

Section: Ground Truth Forcementioning

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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“…The conventional approach for this task results in computationally complex algorithms since they are based on integration of the elementary forces over the SDO surface. This work demonstrates that these tasks can be addressed using supervised [3] and reinforcement learning (RL) [4,5] techniques. Determination of the ion beam impact on a space debris object using convolutional neural networks.…”

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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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Determination of the force exerted by an ion beam on a space debris object from the edges of its images using deep learning

Redka¹

2023

Teh. Meh.

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The goal of this article is to develop an effective image preprocessing algorithm and a neural network model for determining the force to be transmitted to a space debris object (SDO) for its non-contact deorbit. In the development and study of the algorithm, use was made of methods of theoretical mechanics, machine learning, computer vision, and computer simulation. The force is determined using a photo taken by an onboard camera. To increase the efficiency of the neural network, an algorithm was developed for feature recognition by the SDO edge in the photo. The algorithm, on the one hand, selects a sufficient number of features to describe the properties of the figure and, on the other hand, significantly reduces the amount of data at the neural network input. A dataset with the features and corresponding reference force values was created for model training. A neural network model was developed to determine the force to be exerted on a SDO from the SDO features. The model was tested using a set of eighteen calculated cases to determine the effectiveness, accuracy, and speed of the algorithm. The proposed algorithm was compared with two existing ones: the method of central projections onto an auxiliary plane and the multilayered neural network model that calculates the force using the SDO orientation parameters. The comparison was performed using the root mean square error, the maximum absolute error, and the maximum relative error. The test results are presented as tables and graphs. The proposed approach makes it possible to develop a system of SDO non-contact removal that does not need to determine the exact relative position and orientation with respect to the active spacecraft. Instead, the algorithm uses camera-taken photos, from which the features necessary for calculation are extracted. This makes it possible to reduce the requirements for its computing elements, to abandon sensors for determining the relative position and orientation, and to reduce the cost of the system.

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Determination of the Force Impact of an Ion Thruster Plume on an Orbital Object via Deep Learning

Cited by 5 publications

References 14 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

Deep Learning for Space Debris Removal

Determination of the force exerted by an ion beam on a space debris object from the edges of its images using deep learning

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