Design and Control of an Autonomous Drone Navigation System Using Embedded AI

Abstract

This research study the design and the development of an autonomous drone navigation system utilizing embedded artificial intelligence (AI) capable of real-time decision making, obstacle avoidance, and path planning. The focus of this research is the application of neural networks and reinforcement learning techniques integrated with the drone’s onboard computer and the fully autonomous navigation of the drone in sophisticated and rapidly changing environments. The stability, precision, and flexible response of the drone’s navigation system within the complex environmental frameworks is attained by the combination of classical control methods (PID, LQR) and the control frameworks provided by artificial intelligence. Other than the developments in the optimization of path planning algorithms (A*, RRT) the paper discusses the incorporation of AI for dynamic obstacle avoidance which enables real-time handling of rapid environmental changes. A balanced coverage of sensor fusion methods (EKF, SLAM) which provides accurate localization in environments where GPS is not available outlines the importance of localization in autonomous drones. The research is developed and demonstrated in multiple environments, and the system’s performance is evaluated based on speed, accuracy, and reliability in comparison with classical systems. This research has significance in demonstrating the operationalization of embedded AI with real-time decision making in improving drone autonomy for applications in delivery, surveillance and, search-and-rescue missions.

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