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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

<a href="/cs/project/VJ02010036" target="_blank" >VJ02010036: Robotický systém řízený algoritmy umělé inteligence pro zpravodajské a průzkumné účely</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2024 International Conference on Unmanned Aircraft Systems

ISBN

979-8-3503-5788-2

ISSN

2575-7296

e-ISSN

—

Počet stran výsledku

7

Strana od-do

92-98

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Chania

Datum konání akce

4. 6. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001259354800141