AGV Path Planning based on Improved Ant Colony Algorithm

With the widespread application of Automated Guided Vehicles (AGV) in industrial production and warehouse logistics, the challenges they face during operation are becoming increasingly apparent. Currently, the path planning problem of AGV has become one of the hot topics in academic research. This paper provides an in-depth analysis of the performance of AGV in real-world scenarios and utilizes grid-based methods to construct a map environment model. Subsequently, a detailed analysis of the movement of AGV in the environment is conducted, and an improved pheromone method is proposed based on the traditional ant colony algorithm, aiming to optimize the navigation path of AGV to determine the optimal travel route.