Publication detail

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

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

ISBN

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"
}