Abstract
This paper addresses anomaly detection and monitoring for swarm drone flights. While the current practice of swarm flight typically relies on the operator’s naked eyes to monitor health of the multiple vehicles, this work proposes a machine learning-based framework to enable detection of abnormal behavior of a large number of flying drones on the fly. The method works in two steps: a sequence of two unsupervised learning procedures reduces the dimensionality of the real flight test data and labels them as normal and abnormal cases; then, a deep neural network classifier with one-dimensional convolution layers followed by fully connected multi-layer perceptron extracts the associated features and distinguishes the anomaly from normal conditions. The proposed anomaly detection scheme is validated on the real flight test data, highlighting its capability of online implementation.
Highlights
Coordination of multiple drones has been intensively studied in the field of military surveillance, smart agriculture, logistics, disaster response, and artistic drone shows [1,2]
The network layout of 1D-Convolutional neural networks (CNN) is similar to the 2D counterpart; it consists of a stack of one-dimensional convolution and max-pooling layers at the end of which is connected a global-pooling layer or a flattened layer
The distribution of the data points in the reduced space defined by the first and the second principal components are shown; the left plot is based on the real-time kinematic global positioning system (RTK-GPS) data and the right plot is based on the inertial navigation system (INS) data
Summary
Coordination of multiple drones has been intensively studied in the field of military surveillance, smart agriculture, logistics, disaster response, and artistic drone shows [1,2]. Traditional model-based methods have been intensively studied in the context of fault detection and isolation (FDI) for safety-critical aerospace systems [25,26] This has recently been extended to multi-unmanned aerial vehicle (UAV) systems from the perspective of safe operations [27,28,29] and for resilience against cyberattacks [30,31,32,33]. In a multi-UAV system, the design and experimental verification methods and strategies have been studied to perform fault detection, identification, and recovery (FDIR) by constructing a cooperative virtual sensor (CVS) system for each UAV through on-board sensor signals [34] These model-based schemes work well when normal behavior of the system is well-understood and predicted using (physics-based) models and when the potential fault modes are well-identified a priori so that a finite number of hypotheses on the faultiness can be posed. Anomaly detection for swarm flights, and (b) to validate the method against real flight test data
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