Deep Neural Network for Precision Landing and Variable Flight Planning of Autonomous UAV
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU146056" target="_blank" >RIV/00216305:26220/21:PU146056 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9694683" target="_blank" >https://ieeexplore.ieee.org/document/9694683</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/PIERS53385.2021.9694683" target="_blank" >10.1109/PIERS53385.2021.9694683</a>
Alternative languages
Result language
angličtina
Original language name
Deep Neural Network for Precision Landing and Variable Flight Planning of Autonomous UAV
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2021 Photonics & Electromagnetics Research Symposium (PIERS)
ISBN
978-1-7281-7247-7
ISSN
1559-9450
e-ISSN
—
Number of pages
5
Pages from-to
2243-2247
Publisher name
IEEE
Place of publication
NEW YORK
Event location
Hangzhou, China
Event date
Nov 21, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000795902300370