Machine Learning for Aerospace Applications using the Blackbird Dataset

Abstract

There is currently much interest in using machine learning (ML) models for vision-based object detection and navigation tasks in autonomous vehicles. For unmanned aerial vehicles (UAVs), and particularly small multi-rotor vehicles such as quadcopters, these models are trained on either unpublished data or within simulated environments, which leads to two issues: the inability to reliably reproduce results, and behavioral discrepancies on physical deployments resulting from unmodeled dynamics in the simulation environment. To overcome these issues, this project uses the Blackbird Dataset to explore integration of ML models for UAV. The Blackbird Dataset is overviewed to illustrate features and issues before investigating possible ML applications. Unsupervised learning models are used to determine flight-test partitions for training supervised deep neural network (DNN) models for nonlinear dynamic inversion. The DNN models are used to determine appropriate model choices over several network parameters including network layer depth, activation functions, epochs for training, and neural network regularization.