A Machine Learning driven Fault Tolerance Mechanism for UAVs’ Flight Controller

Unmanned Aerial Vehicles (UAVs) are susceptible to various hazards (e.g., software or hardware failures, communication failures, or security attacks) that may hinder mission completion or compromise safety by violating the separation minima (i.e., the minimum distance that must be maintained between UAVs in order to ensure safe and efficient operations). To address this issue, this paper proposes a new machine learning-based fault-tolerant mechanism for UAV flight controllers that tolerates GPS-related faults. These faults are of paramount importance (i.e., accidental faults and/or security attacks that eventually cause failures in the GPS function/data), as accurate positioning and tracking are essential to assure safe operation in UAVs. The proposed machine learning models were built using 884,410 data records from 1,985 flight logs collected from the PX4 public repository. The trained models are used to predict the expected position of the UAV during a mission, and separation minima are used as a threshold to detect the GPS hazards by comparing it with the distance between two consecutive position values. When a hazard is detected (i.e., the distance is higher than separation minima), the predicted values by machine learning models are fed into the flight controller’s position estimator (i.e., an Extended Kalman Filter (EKF)). To evaluate the effectiveness of this approach, validation experiments were conducted on several realistically defined missions while being exposed to different types of failure conditions (e.g., GPS signal loss or GPS Spoofing), both with and without using the proposed fault-tolerant mechanism. The results show a remarkable reduction in safety violations (the number of separation minima violations was reduced from 94 to 1). Additionally, the proposed mechanism demonstrated a notable improvement in the distance traveled by UAV and the duration of the flight mission in failure conditions, showing its ability to mitigate faults effectively. These findings support the effectiveness of the proposed fault tolerance mechanism in enhancing UAV safety in the presence of issues caused by GPS.