On-board Deep-learning-based Unmanned Aerial Vehicle Fault Cause Detection and Identification
Vidyasagar Sadhu,Saman Zonouz,Dario Pompili,Vidyasagar Sadhu,Saman Zonouz,Dario Pompili
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...
