Autonomous Drone Navigation Using Reinforcement Learning Algorithms

Abstract

The rapid evolution of unmanned aerial vehicles (UAVs), commonly referred to as drones, has transformed several industries, including defense, agriculture, disaster management, logistics, and surveillance. One of the most critical challenges in drone operations is autonomous navigation in dynamic and uncertain environments. Traditional rule-based or model-driven navigation systems are limited in adaptability and scalability, particularly in environments characterized by obstacles, unpredictable wind currents, or GPS-denied zones. In recent years, reinforcement learning (RL) has emerged as a powerful paradigm for developing autonomous navigation systems capable of learning optimal policies through interaction with their environment.