Application of AGV (automated guided vehicle) in intelligent storage for electric power metering

With the development of intelligent storage systems, AGVs (Automated Guided Vehicles) have been widely applied in smart storage for electric power metering, where the issue of path planning becomes crucial for enhancing storage efficiency and safety. This study aims to enhance the dynamic adaptability and path optimization capabilities of the algorithm by introducing and improving the A* algorithm. Experimental results indicate that in static environments, the number of node expansions for the ATI-A* algorithm is significantly lower than that of traditional A* and Dijkstra algorithms. In dynamic environments, the path length of the ATI-A* algorithm consistently remains at a low level, achieving a path length of 460 after 800 iterations. The ATI-A* algorithm demonstrates remarkable performance in path planning accuracy, reaching 97% accuracy after 800 iterations, significantly outperforming the 91% accuracy of the A* algorithm and the 83% accuracy of the Dijkstra algorithm. In summary, the ATI-A* algorithm exhibits excellent path planning capabilities in complex storage environments, effectively improving the working efficiency and path optimization of AGVs and holds broad application prospects.