Fault Detection and Emergency Path Planning for Fixed Wing UAVs

Abstract

The implementation of Uncrewed Aerial Vehicles (UAVs) for civilian and military purposes requires safety protocols. Control surface failures are among the most common issues in fixed-wing UAVs. This study presents two heuristic-based algorithms developed to detect such faults. The Mahalanobis Distance Fault Detection Algorithm (MDFDA) employs the Mahalanobis distance to identify anomalies in UAV states. On the other hand, the Fuzzy Logic Fault Detection Algorithm (FLFDA) uses fuzzy logic to identify jammed control surfaces. In the event of a fault, an emergency path planning algorithm is initiated. This algorithm leverages terrain and population data to pinpoint safe landing zones. However, the type of fault the system encounters will impact the aircraft’s ability to reach these safe zones. In this study, unreachable zones are delineated based on the specific control surface fault detected. These zones are areas where the aircraft cannot land or traverse due to the fault. By employing the proposed protocol, the aircraft can detect a fault during mid-flight, select a safe landing zone within its reachable range, and mitigate the effects of the control surface fault. This approach enhances the chances of preserving the aircraft and ensures the safety of the surrounding population.