Digital model of automated unmanned aerial vehicle with edge computing application for railway reconnaissance

Aigul Tulembayeva; Shayakhmet Ashirov; Evgenij Makarov; Zhexen Seitbattalov

doi:10.14311/AP.2024.64.0059

Authors

Aigul Tulembayeva Ministry of Defense of the Republic of Kazakhstan, 14 Dostyk Ave., 010000 Astana, Kazakhstan https://orcid.org/0000-0001-5360-6581
Shayakhmet Ashirov General Staff of the Armed Forces of the Republic of Kazakhstan, Department of Geoinformation Support, 14 Dostyk Ave., 010000 Astana, Kazakhstan https://orcid.org/0009-0005-2995-4291
Evgenij Makarov National Defence University named after the First President of the Republic of Kazakhstan – Elbasi, Department of Scientific Information and Innovation, Esilsky District, 72 Turan Ave., 010000 Astana, Kazakhstan https://orcid.org/0000-0002-2793-9101
Zhexen Seitbattalov L. N. Gumilyov Eurasian National University, Department of Computer and Software Engineering, 2 Satbaev St., 010000 Astana, Kazakhstan https://orcid.org/0000-0003-2607-4908

DOI:

https://doi.org/10.14311/AP.2024.64.0059

Keywords:

drone, military intelligence, image processing, tracking of the railroad, robotics simulator

Abstract

In the last decade, unmanned aerial vehicles have been widely used in various civil and military field operations. Most modern drones are manually controlled by an operator and require decision making, which requires experience gained through training, which is time consuming and expensive, and even with experienced operators, the human factor can never be excluded. In this paper, an algorithm for an automatic flight mode of unmanned aerial vehicles without an operator’s participation has been developed. The proposed algorithm has been considered in the military reconnaissance of railways using edge computing and computer vision approaches to recognise railroads and moving trains for simulated and real cases as well as send report data to the web platform. As a result, the head of the operation received information about the number of carriages and Global Positioning System coordinates of the recognised train to make the necessary decisions.

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