A Path Planning Method for Intelligent Warehouse Robots Based on Improved A-star Algorithm

In the context of escalating demand for heightened logistical efficiency, the significance of intelligent warehousing becomes increasingly pronounced. Within the realm of intelligent warehousing, the prevalent issue of numerous turning points in existing algorithmically planned routes necessitates a reduction in robotic velocity during these maneuvers, thereby impeding overall operational efficiency. Addressing this challenge, this article posits an enhanced A-star method based on directional search and path reprocessing. Primarily, it aims to curtail computational load by minimizing the quantity of search nodes. Additionally, it endeavors to streamline paths by eliminating non-essential nodes, consequently reducing the number of turning points and shortening path lengths. Simulation outcomes indicate that the enhanced A-star algorithm attains an optimal overall performance across maps with varying obstacle ratios.