A Deep Learning-based Fault Recovery System for Safe Flight of UAV in the Position Sensor Freezing Situation

Abstract

As the use of robots such as unmanned aerial vehicles (UAVs), unmanned ground vehicles, and robot arms in industry and leisure continues to grow, it becomes increasingly important to maintain these robots in a stable condition to prevent potential danger, including actuator, sensor, and system faults. Consequently, researchers have developed various algorithms to address these faults. In this study, we propose a deep learning-based fault recovery system designed to ensure the safe flight of UAVs in situations where position sensors freeze. When a position sensor freezing event is detected, this fault recovery system rectifies the issue by enabling the UAV to utilize values from a long short-term memory-based position prediction model, thus replacing the frozen sensor data. We tested our fault recovery system with a UAV in a Gazebo simulation and validated its effectiveness by comparing it with an inertial measurement unit kinematic model-based fault recovery system. The proposed deep learning-based fault recovery system demonstrated superior performance.