AUTONOMUS DRONE NAVIGATION USING REINFORCEMENT LEARNING

Abstract

The Autonomous Drone Navigation System is a cutting-edge solution designed to empower drones with the ability to navigate and operate independently in diverse and dynamic environments. This system integrates advanced technologies such as computer vision, sensor fusion, and machine learning to enhance the drone’s ability to perceive its surroundings, make decisions, and execute complex tasks without human intervention. Vision-based navigation plays a crucial role in this system, employing cameras and image processing algorithms to detect obstacles, recognize landmarks, and map terrain. This capability is especially useful in environments where GPS signals are weak or unavailable, such as indoor spaces, dense forests, or urban canyons. The navigation system leverages sensor fusion by combining data from GPS, Inertial Measurement Units (IMUs), LiDAR, and ultrasonic sensors to provide accurate environmental mapping and localization. By processing inputs from multiple sensors, the system can operate reliably even in challenging scenarios, ensuring precise maneuverability and obstacle avoidance. Furthermore, the drone is equipped with advanced autonomous decision-making capabilities. Reinforcement learning algorithms enable the system to adapt dynamically to changes in the environment, such as moving obstacles or adverse weather conditions. Path-planning algorithms, including A* and Dijkstra, are used to calculate the most efficient and safe routes in real-time, while collision-avoidance mechanisms predict potential hazards and adjust the flight path accordingly.