Design of an Intelligent Navigation System Integrating Reinforcement Learning and Computer Vision Algorithms

Traditional static guidance systems have problems such as poor interactivity and low path planning efficiency. This paper designs an intelligent guidance system to achieve efficient, accurate and personalized navigation services. This paper constructs an intelligent guidance system that integrates reinforcement learning and computer vision algorithms, and adopts a multi-level architecture: the perception layer collects environmental data, the data processing layer uses YOLO and semantic segmentation to extract features, the decision layer uses deep Q network (DQN) to plan and optimize the path, and the interaction layer provides intuitive navigation and user feedback mechanism. The system effectively solves the limitations of traditional guidance systems in complex environments and improves navigation efficiency and user experience. In terms of path planning efficiency, the average path planning time of the intelligent guidance system is shorter than that of the traditional system; in terms of path navigation accuracy, the average accuracy of the intelligent guidance system reaches 99.1%, which is much higher than the 95.2% of the traditional system. These data fully prove the effectiveness of the intelligent guidance system proposed in this paper in improving the quality of navigation services and user experience.