In static grid maps, an improved algorithm that combines Jump Point Search and Bi-directional Parallel Ant Colony Search is proposed to address the problem of slow convergence and easy trapping in local optima of traditional ant colony algorithms for AGV path planning. Firstly, the actual research environment is modeled by gridization, and the improved Jump Point Search algorithm is used to generate the initial suboptimal path for bi-directional search, providing a reference for the initial search direction of bi-directional ant colony search. Secondly, an improved transition probability heuristic function is used in the bi-directional parallel ant colony search process, which considers the avoidance of collision between AGV and obstacles when determining the next transition node. Meanwhile, by designing an information sharing mechanism and combining two fusion models of improved information increment and concentration, the global information concentration is shared and updated to better explore and optimize the path and ensure the connection of bi-directional paths. Finally, experimental results are compared with traditional ant colony algorithms to verify the improved algorithm"s global search capability, efficiency and safety.