Control and motion planning of fixed-wing UAV through reinforcement learning

Autonomous systems, driven by advancements in artificial intelligence and machine learning, are increasingly integral to various domains, including aerial vehicle control. This paper explores the application of reinforcement learning (RL) in the Guidance, Navigation, and Control (GNC) systems of aerial vehicles, specifically focusing on motion planning for fixed-wing UAVs. We present two key applications: waypoint tracking and dynamic target interception. These findings underscore the potential of reinforcement learning to enhance the robustness, adaptability, and efficiency of aerial vehicle control systems, paving the way for more autonomous and intelligent flight operations.