Application of artificial intelligence and machine learning in autonomous navigation for drones

This paper provides a comprehensive review of the applications of Artificial Intelligence (AI) and Machine Learning (ML) in Unmanned Aerial Vehicles (UAVs), focusing on key areas such as machine learning, expert systems, autonomous planning, and distributed artificial intelligence. It also examines best practices for testing and validation in diverse environments to ensure UAV adaptability and robustness. The integration of AI/ML into UAV software has shown significant improvements in target detection, environmental perception, path planning, and real-time decision-making capabilities. Deep learning techniques, in particular, enhance target recognition under complex conditions, while reinforcement learning and distributed AI improve adaptive path planning in dynamic settings. This study highlights the importance of simulation and Hardware-in-the-Loop (HIL) testing to identify potential issues before field deployment, along with user feedback and continuous data analysis to optimize performance. Ultimately, this paper underscores how AI-driven UAV technology is advancing for various applications, establishing a foundation for increased operational efficiency and safety across military, civil, and commercial sectors.