Deep Reinforcement Learning for Autonomous Drone Navigation

Abstract

The rapid advancement of artificial intelligence, sensor technology, and robotics has significantly enhanced the autonomous capabilities of unmanned aerial vehicles (UAVs), yet achieving fully autonomous navigation in complex, dynamic, and unstructured environments remains a persistent challenge. Traditional rule-based and classical control algorithms often fail to adapt to unpredictable conditions, motivating the integration of Deep Reinforcement Learning (DRL) for intelligent, experience-driven navigation. This study proposes a DRL-based autonomous drone navigation framework that combines deep neural perception with reinforcement-driven decision-making, enabling UAVs to learn optimal flight policies through interaction with both simulated and real environments. Multiple DRL algorithms, including DQN, PPO, DDPG, SAC, and A3C, were implemented and evaluated using high-fidelity simulators such as AirSim and Gazebo, supported by sensor fusion from LiDAR, RGB-D cameras, IMU, and GPS. A structured methodology was adopted involving environment modelling, state–action space design, reward engineering, actor–critic network optimisation, and Sim2Real transfer techniques. Experimental results demonstrate that DRL-based models significantly outperform traditional navigation approaches in obstacle avoidance, trajectory optimisation, and generalisation to unseen scenarios. PPO achieved a 92% collision-free success rate, while SAC excelled in continuous control and environmental uncertainty. Further analysis confirms the importance of reward shaping, hybrid sensor inputs, and curriculum learning for robust convergence. Although training time and sim-to-real discrepancies pose challenges, the findings establish DRL as a powerful paradigm for next-generation UAV autonomy. The proposed system offers substantial potential for applications in disaster response, surveillance, environmental monitoring, and multi-drone coordination, contributing to more adaptive, intelligent, and safe aerial navigation systems.