## 5 Conclusions  
This paper introduces an AGV navigation and positioning system that integrates navigation and precise positioning. The AMCL particle positioning algorithm is used to perform triangular matching, establish a library of QR code templates, and quickly identify the current position of the QR code for precise positioning. The accuracy of the proposed algorithm is verified through a large number of comparative experiments and simulation data, and the positioning and navigation tasks can be completed in a real experimental environment. It solves the problems of inaccurate positioning and low precision, which are common in the market at present, and provides reference for the accurate positioning of AGV. The focus of this research is to develop hybrid navigation AGVs based on the market scarcity to serve the public and shift from industry to service. The main contributions of this paper are as follows:  
1. This article provides an in-depth analysis and research on the development status of AGV's critical domestic and international technologies and researches robot navigation, positioning, and path planning technology. The advantages and disadvantages of various methods are compared, and the overall navigation scheme and system navigation method are designed in detail. Finally, the feasibility and benefits of this choice are verified through experiments. 2. An ROS operating system was used to build the simulation environment of the AGV, and the real positioning system platform was established to prepare for the research of the AGV positioning system. The localization system proposed in this paper uses SLAM global mapping to obtain the absolute coordinates of ground punctuation. It uses the improved AMCL algorithm to combine QR codes, which improves the positioning time and accuracy in the navigation process. 3. Improving the generation of the initial particle swarm can improve the convergence speed and accuracy of the algorithm, and improve the resampling method to effectively reduce or avoid the kidnapping problem. By building the simulation model and testing the simulation using MATLAB software, the algorithm can be made to converge faster and more accurately to the AGV position, as well as improving the real-time and responsiveness of the system, and greatly reducing the time required for navigation. 4. When the particles generated by the AMCL algorithm encounter the kidnapping situation, i.e., the AGV generates too much offset and the particle state does not change with the movement of the AGV. Through comparison, it can be learnt that the improved AMCL algorithm can quickly adjust the attitude and correct the offset distance, so that it can quickly return to the original travelling route, and the resumption of the adjustment time has been improved by 68.73% compared with the unimproved algorithm. 5. During AGV navigation, the time required for navigation was reduced by 42.81% compared to the unimproved algorithm. The navigation time is greatly reduced, which speeds up the time to process the goods and improves the turnaround speed and capacity of the goods. The positioning accuracy is an important criterion to measure the accuracy of the proposed algorithm. In this paper, by comparing and contrasting, it is concluded that the positioning accuracy is improved by 64.27% compared to the previous algorithm. In the following research, we will pay more attention to the accuracy and practicality of AGVs in positioning and navigation. For example, we will study the practical application of visual navigation in AGVs and further combine visual information with or instead of QR code data to improve the ability of AGVs to sense their surroundings. In addition, we aim to make a major breakthrough in visual autonomous navigation in AGVs, i.e., to achieve more accurate positioning and no interference throughout the autonomous navigation process, and to apply it to more complex operational tasks and environments. Conceptualization, B.Z. and J.Q. ; methodology, B.Z., J.Q. and S.L. ; software, B.Z. and G.Y.