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JANOUŠEK, J. MARCOŇ, P. KLOUDA, J. POKORNÝ, J. RAICHL, P. ŠIRŮČKOVÁ, A.
Original Title
Deep Neural Network for Precision Landing and Variable Flight Planning of Autonomous UAV
Type
conference paper
Language
English
Original Abstract
The article is focused on autonomous unmanned aerial vehicle control for precise guidance to the ground landing target with variable creation of another flight plan. Object recognition is performed in real-time by a neural network using a camera located on Unmanned Aerial Vehicle (UAV). Object recognition is performed in the ground station with which the aircraft maintains a communication channel. The ground station computer evaluates the relative position of the aircraft with the position of the monitored landing field in the field of view of the image and after successful detection sends back flight instructions to the aircraft control unit. The neural network is pre-trained on landing patterns carrying additionally encoded information with flight instructions about the next waypoints of the flight plan according to which the drone performs an autonomous flight. The created neural network thus serves not only for precise landing, but also for finding the following points of the flight plan for a given aircraft.
Keywords
Neural networks, Autonomous aerial vehicles, Real-time systems, Planning, Object recognition, Telemetry, Reliability
Authors
JANOUŠEK, J.; MARCOŇ, P.; KLOUDA, J.; POKORNÝ, J.; RAICHL, P.; ŠIRŮČKOVÁ, A.
Released
25. 11. 2021
Publisher
IEEE
Location
NEW YORK
ISBN
978-1-7281-7247-7
Book
2021 Photonics & Electromagnetics Research Symposium (PIERS)
1559-9450
Periodical
Progress In Electromagnetics
State
United States of America
Pages from
2243
Pages to
2247
Pages count
5
URL
https://ieeexplore.ieee.org/document/9694683
BibTex
@inproceedings{BUT179767, author="Jiří {Janoušek} and Petr {Marcoň} and Jan {Klouda} and Josef {Pokorný} and Petr {Raichl} and Anna {Širůčková}", title="Deep Neural Network for Precision Landing and Variable Flight Planning of Autonomous UAV", booktitle="2021 Photonics & Electromagnetics Research Symposium (PIERS)", year="2021", journal="Progress In Electromagnetics", pages="2243--2247", publisher="IEEE", address="NEW YORK", doi="10.1109/PIERS53385.2021.9694683", isbn="978-1-7281-7247-7", issn="1559-9450", url="https://ieeexplore.ieee.org/document/9694683" }