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

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.