Detail publikačního výsledku

Deep Neural Network for Precision Landing and Variable Flight Planning of Autonomous UAV

JANOUŠEK, J.; MARCOŇ, P.; KLOUDA, J.; POKORNÝ, J.; RAICHL, P.; ŠIRŮČKOVÁ, A.

Originální název

Deep Neural Network for Precision Landing and Variable Flight Planning of Autonomous UAV

Anglický název

Deep Neural Network for Precision Landing and Variable Flight Planning of Autonomous UAV

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

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.

Anglický abstrakt

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.

Klíčová slova

Neural networks, Autonomous aerial vehicles, Real-time systems, Planning, Object recognition, Telemetry, Reliability

Klíčová slova v angličtině

Neural networks, Autonomous aerial vehicles, Real-time systems, Planning, Object recognition, Telemetry, Reliability

Autoři

JANOUŠEK, J.; MARCOŇ, P.; KLOUDA, J.; POKORNÝ, J.; RAICHL, P.; ŠIRŮČKOVÁ, A.

Rok RIV

2023

Vydáno

25.11.2021

Nakladatel

IEEE

Místo

NEW YORK

ISBN

978-1-7281-7247-7

Kniha

2021 Photonics & Electromagnetics Research Symposium (PIERS)

ISSN

1559-9450

Periodikum

Progress In Electromagnetics

Stát

Spojené státy americké

Strany od

2243

Strany do

2247

Strany počet

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"
}