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RAICHL, P. JANOUŠEK, J. MARCOŇ, P.
Original Title
FollowThePathNet: UAVs Use Neural Networks to Follow Paths in GPS-Denied Environments
Type
conference paper
Language
English
Original Abstract
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.
Keywords
Training, Visualization, Navigation, Neural networks, Reliability engineering, Data models, Software
Authors
RAICHL, P.; JANOUŠEK, J.; MARCOŇ, P.
Released
19. 6. 2024
Publisher
IEEE
ISBN
979-8-3503-5788-2
Book
2024 International Conference on Unmanned Aircraft Systems
2575-7296
Periodical
2024 International Conference on Unmanned Aircraft Systems (ICUAS)
State
Hellenic Republic
Pages from
92
Pages to
98
Pages count
7
URL
https://ieeexplore.ieee.org/document/10557044/keywords#keywords
BibTex
@inproceedings{BUT189115, author="Petr {Raichl} and Jiří {Janoušek} and Petr {Marcoň}", title="FollowThePathNet: UAVs Use Neural Networks to Follow Paths in GPS-Denied Environments", booktitle="2024 International Conference on Unmanned Aircraft Systems", year="2024", journal="2024 International Conference on Unmanned Aircraft Systems (ICUAS)", pages="92--98", publisher="IEEE", isbn="979-8-3503-5788-2", issn="2575-7296", url="https://ieeexplore.ieee.org/document/10557044/keywords#keywords" }