Review of Autonomous UAV Methods in GNSS-Challenging Environments

Abstract

Interest in autonomous UAVs has been growing due to the need in many different industries to seek a robust and efficient system that can work even in remote areas without any other intervention. This paper provides a comprehensive review of recent advancements in autonomous UAV methodologies, with a particular focus on three key areas: planning, navigation, and AI-driven algorithms. The review examines the strengths and limitations of traditional approaches, such as Kalman filters and SLAM-based methods, while also exploring the potential of AI-driven techniques, particularly deep reinforcement learning (DRL), in enhancing UAV autonomy. Although recent developments show promising results, challenges remain in scalability, computational efficiency, and adaptability to complex environments. The findings suggest future research directions toward hybrid methodologies that integrate classical and AIbased techniques to improve UAV performance in real world scenarios.