Towards an Embedded System for Failure Diagnosis in Drones Using AI and SAC-DM on FPGA

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

Abstract

We present a way of failure detection in real-time unmanned aerial vehicles (UAVs) by integrating Chaos Theory and AI techniques on an FPGA board. The Signal Analysis based on Chaos using the Density of Maxima (SAC-DM) validates the input of the Machine Learning (ML) model due to the relation between the density of maxima and autocorrelation length. While the accuracies achieved solely by SAC-DM are not remarkably high, the ML model demonstrates an accuracy of 92.46% when utilizing sac-dm results as inputs. The unprecedented integration of SAC-DM on FPGA board serves as a solution for high-speed onboard processing, parallel integrated data synchronization and fusion, and an enhanced low-power architecture.

Details

OriginalspracheEnglisch
Titel2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)
Herausgeber (Verlag)IEEE
Seiten1-2
Seitenumfang2
ISBN (elektronisch)978-3-9819263-8-5
ISBN (Print)979-8-3503-4860-6
PublikationsstatusVeröffentlicht - 10 Juni 2024
Peer-Review-StatusJa

Konferenz

Titel2024 Design, Automation and Test in Europe Conference and Exhibition
KurztitelDATE 2024
Veranstaltungsnummer27
Dauer25 - 27 März 2024
Webseite
OrtPalacio De Congresos De Valencia
StadtValencia
LandSpanien

Externe IDs

ORCID /0000-0003-2571-8441/work/176859793
ORCID /0000-0002-6311-3251/work/176862432
unpaywall 10.23919/date58400.2024.10546792
Mendeley ad9a325a-2e6a-3349-a0a1-73c07bcca7cc

Schlagworte

Schlagwörter

  • Chaos Theory, FPGA boards, Real-time, Failure detection, UAVs