In view of the shortcoming of traditional ant colony algorithm in path planning of AGV, such as a large number of inflection points and high operating energy consumption, an improvement adaptive ant colony algorithm is proposed in this paper. Firstly, the adaptive parameter adjustment method to continuously adjust the relative importance of pheromone concentration and heuristic information in the iterative process to enhance the direction of ant search; Secondly, the multiobjective path performance evaluation index is introduced. Based on the single index of path length, the path risk index and the steering times are introduced to achieve the global comprehensive optimization of AGV path planning; Then a reward and punishment mechanism is proposed to update the pheromone increment, which provides different pheromone update rules for the paths of different evaluation indicators to avoid the algorithm falling into premature; Finally, the quasi uniform cubic Bspline smoothing strategy is introduced to further optimize the optimal solution. At 20×20 and 30×30 Simulation experiments are carried out in different complexity environments. Compared with the traditional ACO, the steering times of improved ACO is reduced by 11.3%~38.2%, and the path optimization speed is increased by 79.8%~87.9%, which verifies the effectiveness, feasibility and superiority of the improved ACO.