On-Board Deep-Learning-Based Unmanned Aerial Vehicle Fault Cause Detection and Classification via FPGAs

With the increase in the 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 postincident 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 work, we propose novel architectures based on deep convolutional and long short-term memory neural networks to detect (via autoencoder) and classify drone misoperations based on real-time sensor data. The proposed architectures are able to learn high-level features automatically from the raw sensor data. Empirical results show that our solution is able to detect (with over 90% accuracy) and classify various types of drone misoperations with about 99% accuracy (simulation data) and up to 85% accuracy (experimental data). Furthermore, with the help of field programmable gate array-based hardware acceleration, we achieved a speedup of 40x (\!2. 6 ms) for detection, while consuming half the amount of power compared with onboard GPU devices, such as NVIDIA Jetson TX2.