Application of Deep Reinforcement Learning (DRL) in the ROS Platform in Autonomous Navigation Decision Making of Unmanned Vehicles

This paper proposes a solution based on Deep Reinforcement Learning (DRL) for the path planning problem of unmanned vehicles, and discusses its deep integration and efficient deployment with Robot Operating System (ROS) in detail. First, we deeply analyze the basic theory and the main algorithm of DRL and, combined with the characteristics of the unmanned vehicle path planning task, demonstrate the applicability and advantages of DRL model. Then, we innovatively design a DRL algorithm improvement strategy for the unmanned vehicle path planning task, and a training and deployment framework closely integrated with ROS platform. The experimental results in a variety of complex environments show that the proposed DRL path planning method performs well in path quality, decision efficiency, safety, and robustness, and is significantly superior to traditional methods and unoptimized DRL models. This study not only provides advanced theoretical guidance and practical tools for the planning of unmanned vehicle paths, but also lays a solid foundation for the deep learning application of intelligent transportation systems in the future.