Analysis of autonomous UAV navigation methods in GPS dead zones

The article presents a systematic review and comparative analysis of modern methods of autonomous navigation of unmanned aerial vehicles (UAVs) in the absence of a GPS signal. The main methods of autonomous navigation are considered: visual (optical flow, visual odometry, visual SLAM), inertial (IMU...

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Main Authors: A.R. Kravchuk, A.H. Tkachuk, O.O. Dobrzhanskyi, Yu.V. Bogoyavlenska, D.Yu. Tkachuk
Format: Article
Language:English
Published: Zhytomyr Polytechnic State University 2025-07-01
Series:Технічна інженерія
Subjects:
Online Access:https://ten.ztu.edu.ua/article/view/334764
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author A.R. Kravchuk
A.H. Tkachuk
O.O. Dobrzhanskyi
Yu.V. Bogoyavlenska
D.Yu. Tkachuk
author_facet A.R. Kravchuk
A.H. Tkachuk
O.O. Dobrzhanskyi
Yu.V. Bogoyavlenska
D.Yu. Tkachuk
author_sort A.R. Kravchuk
collection DOAJ
description The article presents a systematic review and comparative analysis of modern methods of autonomous navigation of unmanned aerial vehicles (UAVs) in the absence of a GPS signal. The main methods of autonomous navigation are considered: visual (optical flow, visual odometry, visual SLAM), inertial (IMU-based), lidar (LiDAR SLAM), radar, radio frequency (UWB, Wi-Fi, BLE), as well as hybrid multi-sensor systems. Particular attention is paid to the characteristics of navigation systems: positioning accuracy, processing latency, requirements for computing resources, reliability in different conditions and energy consumption level. Various application options for each method are considered, and the possibilities of use are also determined taking into account the technical limitations of the UAV. The results of a comparative analysis are presented, which allows systematizing the advantages and disadvantages of individual solutions depending on the type of environment, computing budget and target problem. Separately, modern trends are analyzed, namely the growing role of adaptive systems with sensor fusion and the involvement of machine learning methods. The main challenges that slow down the widespread implementation of the analyzed systems in practical applications are identified, namely the lack of unified testing methods, energy consumption of complex systems, lack of realistic datasets, the problem of integrating algorithms into various control systems. Promising directions for further research are identified, which include the development of energy-efficient navigation solutions, the integration of deep learning, and the creation of specialized hardware. The results obtained are of practical importance for engineers, developers, and researchers working to increase the autonomy of UAVs in the absence of GPS.
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spelling doaj-art-9a0cf53b0dac488287ea585c3121d8342025-08-20T03:17:59ZengZhytomyr Polytechnic State UniversityТехнічна інженерія2706-58472707-96192025-07-01951235242doi.org/10.26642/ten-2025-1(95)-235-242Analysis of autonomous UAV navigation methods in GPS dead zonesA.R. Kravchuk0https://orcid.org/0000-0002-8305-2492A.H. Tkachuk1https://orcid.org/0000-0003-2466-6299O.O. Dobrzhanskyi2https://orcid.org/0000-0002-4330-0287Yu.V. Bogoyavlenska3https://orcid.org/0000-0003-4101-7127D.Yu. Tkachuk4https://orcid.org/0009-0003-2583-6445Zhytomyr Polytechnic State University, UkraineZhytomyr Polytechnic State University, UkraineZhytomyr Polytechnic State University, UkraineZhytomyr Polytechnic State University, UkraineZhytomyr Polytechnic State University, UkraineThe article presents a systematic review and comparative analysis of modern methods of autonomous navigation of unmanned aerial vehicles (UAVs) in the absence of a GPS signal. The main methods of autonomous navigation are considered: visual (optical flow, visual odometry, visual SLAM), inertial (IMU-based), lidar (LiDAR SLAM), radar, radio frequency (UWB, Wi-Fi, BLE), as well as hybrid multi-sensor systems. Particular attention is paid to the characteristics of navigation systems: positioning accuracy, processing latency, requirements for computing resources, reliability in different conditions and energy consumption level. Various application options for each method are considered, and the possibilities of use are also determined taking into account the technical limitations of the UAV. The results of a comparative analysis are presented, which allows systematizing the advantages and disadvantages of individual solutions depending on the type of environment, computing budget and target problem. Separately, modern trends are analyzed, namely the growing role of adaptive systems with sensor fusion and the involvement of machine learning methods. The main challenges that slow down the widespread implementation of the analyzed systems in practical applications are identified, namely the lack of unified testing methods, energy consumption of complex systems, lack of realistic datasets, the problem of integrating algorithms into various control systems. Promising directions for further research are identified, which include the development of energy-efficient navigation solutions, the integration of deep learning, and the creation of specialized hardware. The results obtained are of practical importance for engineers, developers, and researchers working to increase the autonomy of UAVs in the absence of GPS.https://ten.ztu.edu.ua/article/view/334764unmanned aerial vehiclesautonomous navigationautomated control systems
spellingShingle A.R. Kravchuk
A.H. Tkachuk
O.O. Dobrzhanskyi
Yu.V. Bogoyavlenska
D.Yu. Tkachuk
Analysis of autonomous UAV navigation methods in GPS dead zones
Технічна інженерія
unmanned aerial vehicles
autonomous navigation
automated control systems
title Analysis of autonomous UAV navigation methods in GPS dead zones
title_full Analysis of autonomous UAV navigation methods in GPS dead zones
title_fullStr Analysis of autonomous UAV navigation methods in GPS dead zones
title_full_unstemmed Analysis of autonomous UAV navigation methods in GPS dead zones
title_short Analysis of autonomous UAV navigation methods in GPS dead zones
title_sort analysis of autonomous uav navigation methods in gps dead zones
topic unmanned aerial vehicles
autonomous navigation
automated control systems
url https://ten.ztu.edu.ua/article/view/334764
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AT yuvbogoyavlenska analysisofautonomousuavnavigationmethodsingpsdeadzones
AT dyutkachuk analysisofautonomousuavnavigationmethodsingpsdeadzones