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Aiming at the problems of Ant Colony Optimization, such as slow convergence, easy to fall into local optimum and easy to deadlock, a dual-population ant colony algorithm for path planning of Automatic Guided Vehicle (AGV) is proposed. The algorithm introduces different pheromone initial values, modifies the heuristic function and rewards and punishes the best and worst paths when pheromone is updated; Based on the improved strategy, the adaptive step-size search strategy is introduced, and the optimization ability and search efficiency of the algorithm are strengthened by the cooperation of two populations with different step sizes; To solve the deadlock problem, a strategy of "filling traps" is proposed, which regards qualified cells as obstacles. Simulation experiments and field experiments are carried out respectively. The results show that the algorithm can plan a safe and comprehensive path for AGV, and provides a feasible scheme for AGV path planning.