ЗГОРТКОВІ НЕЙРОННІ МЕРЕЖІ ДЛЯ ВИЗНАЧЕННЯ ВПЛИВУ ІОННОГО ПРОМЕНЯ НА ОБ’ЄКТ КОСМІЧНОГО СМІТТЯ

• Date: 2023
• Main Authors: REDKA, M., KHOROSHYLOV, C.
• Format: Article
• Language: English
• Published: PH “Akademperiodyka” 2023
• Subjects: видалення космічного сміття; глибинне навчання; силовий вплив електро-реактивного двигуна; згорткові нейронні мережі.
• Online Access: https://scinn-eng.org.ua/ojs/index.php/ni/article/view/491
• Journal Title: Science and Innovation

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.