FollowThePathNet: UAVs Use Neural Networks to Follow Paths in GPS-Denied Environments

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU151734" target="_blank" >RIV/00216305:26220/24:PU151734 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10557044/keywords#keywords" target="_blank" >https://ieeexplore.ieee.org/document/10557044/keywords#keywords</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICUAS60882.2024.10557044" target="_blank" >10.1109/ICUAS60882.2024.10557044</a>

Result language
angličtina
Original language name
FollowThePathNet: UAVs Use Neural Networks to Follow Paths in GPS-Denied Environments
Original language description
Navigating complex pathways autonomously poses a significant challenge for Unmanned Aerial Vehicles (UAVs). To address this issue, we developed a robust convolutional neural network (CNN) enabling UAVs to follow specific paths, such as trail, rural, and cycling ones, using real-time camera data. Our CNN model interprets the visual data to estimate the UAV's position relatively to the path, enabling path following without human intervention. This article details the methodology employed in training our neural network, including the data collection, architecture of the model, and parameters. Additionally, we describe integrating the hardware and software components used in the implementation. We conducted real-world tests to evaluate the effectivity of our approach. These tests confirmed the UAVs' capability to follow the designated paths, demonstrating the practical applicability and reliability of the system. The results and their implications are discussed thoroughly.
Czech name
—
Czech description
—

Project
<a href="/en/project/VJ02010036" target="_blank" >VJ02010036: An Artificial Intelligence-Controlled Robotic System for Intelligence and Reconnaissance Operations</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2024 International Conference on Unmanned Aircraft Systems
ISBN
979-8-3503-5788-2
ISSN
2575-7296
e-ISSN
—
Number of pages
7
Pages from-to
92-98
Publisher name
IEEE
Place of publication
New York
Event location
Chania
Event date
Jun 4, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001259354800141

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