On-board Deep-learning-based Unmanned Aerial Vehicle Fault Cause Detection and Identification

• URL: http://arxiv.org/abs/2005.00336v2
• Date: Wed, 6 May 2020 18:55:28 GMT
• Title: On-board Deep-learning-based Unmanned Aerial Vehicle Fault Cause Detection and Identification
• Authors: Vidyasagar Sadhu, Saman Zonouz, Dario Pompili
• Abstract summary: We propose novel architectures to detect and classify drone mis-operations based on sensor data. We validate the proposed deep-learning architectures via simulations and experiments on a real drone. Our solution is able to detect with over 90% accuracy and classify various types of drone mis-operations.
• Score: 6.585891825257162
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the increase in use of Unmanned Aerial Vehicles (UAVs)/drones, it is important to detect and identify causes of failure in real time for proper recovery from a potential crash-like scenario or post incident forensics analysis. The cause of crash could be either a fault in the sensor/actuator system, a physical damage/attack, or a cyber attack on the drone's software. In this paper, we propose novel architectures based on deep Convolutional and Long Short-Term Memory Neural Networks (CNNs and LSTMs) to detect (via Autoencoder) and classify drone mis-operations based on sensor data. The proposed architectures are able to learn high-level features automatically from the raw sensor data and learn the spatial and temporal dynamics in the sensor data. We validate the proposed deep-learning architectures via simulations and experiments on a real drone. Empirical results show that our solution is able to detect with over 90% accuracy and classify various types of drone mis-operations (with about 99% accuracy (simulation data) and upto 88% accuracy (experimental data)).

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