Deep learning-based anomaly detection for individual drone vehicles performing swarm missions

This study explores methods for the detection and identification of anomalous instances in individual drone vehicles when performing missions in a swarm formation. Conventional anomaly detection (AD) in unmanned aerial vehicle (UAV) clusters typically involves manual inspection, which is time and resource-inefficient, followed by machine learning techniques. In this study, a novel machine-learning-based framework was proposed for the automatic detection of anomalous individual drones within a swarm and the rapid identification of faulty channels. Considering the imbalance between normal and abnormal states when using real flight data, semisupervised models were selected. Four models with one-dimensional (1D) convolutions were trained on normal data. These models were based on a variational autoencoder and three popular AD-specific models (AnoGAN, GANomaly, and Skip-GANomaly), and their performances considering several metrics were compared. Data preprocessing was performed according to various scaling methods, and the hyperparameters that affect the training process were determined through Bayesian optimization. After training, AD was performed using a twostep process. First, detection through binary classification was performed by generating a reconstruction of the testing data and thresholding the reconstruction error. After detection, if the data were determined to be abnormal, each of the 16 channels was ranked in terms of its probability of being the source of the anomaly. The proposed scheme for detecting anomalies was tested and verified using real-world flight data, and analysis of the results revealed the major types of faults and identified specific abnormal channels. This has implications on future research of implementing necessary responses to abnormal readings to maintain UAV autonomy.