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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2021 Photonics & Electromagnetics Research Symposium (PIERS)

ISBN

978-1-7281-7247-7

ISSN

1559-9450

e-ISSN

—

Počet stran výsledku

5

Strana od-do

2243-2247

Název nakladatele

IEEE

Místo vydání

NEW YORK

Místo konání akce

Hangzhou, China

Datum konání akce

21. 11. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000795902300370