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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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