@Article{PenhaNetoCampShig:2019:UAAuNa,
author = "Penha Neto, Gerson da and Campos Velho, Haroldo Fraga de and
Shiguemori, Elcio Hieiti",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)}",
title = "UAV autonomous navigation by data fusion and FPGA",
journal = "Mec{\'a}nica Computacional",
year = "2019",
volume = "37",
number = "16",
pages = "609--618",
month = "05-07 nov.",
keywords = "Unmanned aerial vehicles, FPGA: Field Programmable Gate Array,
autonomous navigation, self-configuring neural network.",
abstract = "Currently, the use of unmanned aerial vehicles (UAV), also known
as drones, is increasing. The applications are in several areas
such as engineering projects, agriculture, livestock, monitoring,
and rescue. One of the main reasons to use UAV is its lower cost
when compared to manned aircraft. The flight of a UAV can be done
remotely or autonomously. For the autonomous navigation, a Global
Navigation Satellite System (GNSS) is usually applied. However, a
GNSS system can suffer natural or human interference, becoming the
research for alternatives strategies a hot topic in this field. An
approach to carry out the autonomous navigation without use of
GNSS signal is to estimate the UAV position by using data fusion
combining different sensors. A solution for autonomous navigation
is presented applying inertial sensor and image processing, both
are employed to estimate the drone position. The data fusion
process is carried out by a computational intelligence procedure.
Two self-configuring ANNs are employed here: for image edge
extraction, and an operator for data fusion. A hybrid computer
architecture is employed to implement the solution with standard
CPU and FPGA (Field Programmable Gate Array).",
language = "en",
targetfile = "penha_uav.pdf",
urlaccessdate = "11 maio 2024"
}