AI-Driven Real-Time UAV Autonomous Trajectory Optimization Using Deep Reinforcement Learning in Dynamic and Partially Observable Environments

Abstract

Autonomous Unmanned Aerial Vehicle (UAV) navigation in dynamic and partially observable environments poses significant challenges, including real-time decision-making and robust obstacle avoidance. Traditional methods often struggle with adaptability, necessitating more advanced approaches. In this work, we propose a Deep Reinforcement Learning (DRL) framework for trajectory optimization, leveraging the Advantage Actor–Critic (A2C) algorithm. We further enhance stability, learning speed, and generalization by employing automatic hyperparameter tuning with Optuna. The proposed system is validated in a Software-in-the-Loop (SITL) simulation using AirSim, ensuring realistic flight dynamics and sensor feedback. Multi-modal observations — combining depth images, GPS, and target localization — improve situational awareness in partially observable conditions. Experimental results show that A2C tuned with Optuna boosts trajectory efficiency by 35.7%, reduces the collision rate to 0.97% and achieves a 74% success rate, while cutting training time by 42%. These findings confirm the effectiveness of using automated hyperparameter tuning for UAV motion planning and pave the way for real-world deployment of DRL-based UAV control systems. Furthermore, our study provides an in-depth comparison of training efficiency, convergence properties, and robustness across different algorithms, establishing a strong foundation for autonomous UAV navigation in challenging environments.