Drone Navigation Using DRL

This research explores the implementation of Deep Reinforcement Learning (DRL) to facilitate autonomous drone navigation within complex and unpredictable environments. Traditional navigation systems often rely on rigid, pre-programmed trajectories that struggle with real-time obstacles or environmental shifts. To overcome these limitations, the proposed framework utilizes a trial-and-error learning mechanism, allowing the unmanned aerial vehicle (UAV) to autonomously discover optimal flight paths and obstacle-avoidance strategies through continuous interaction with its surroundings. .By integrating high-frequency environmental sensing with adaptive learning algorithms, the system enhances its navigational precision and safety across diverse settings, including urban landscapes, rural terrains, and confined indoor spaces. A core component of the framework is the integration of proactive collision prediction and avoidance strategies, which significantly bolster operational reliability. The architecture is designed with scalability in mind, providing a foundation for multi-drone coordination and collaborative mission execution in high-density scenarios. This DRL-driven approach represents a shift toward truly intelligent, self-evolving aerial robotics capable of maintaining high mission success rates in dynamic, "in-the-wild" conditions.