Reasonable and efficient path is the key for the automated guided vehicle (AGV) to complete autonomous navigation transportation. Aiming at the existing problems of the conventional A* path planning methods, such as the unsmooth path planning,vulnerability to local optimal, poor safety and stability.To address these issues,this research presents a novel path planning algorithm based on the fusion of enhanced A* algorithm and enhanced DWA.By introducing weight factors through an evaluation function and using the cubic B-spline curves, the search speed and path smoothness of A* algorithm are enhanced.Using the DWA algorithm to provide accurate directional guidance for automatic guided transport vehicles in local path planning,and enhancing the AGV obstacle avoidance ability in dynamic environments by enhancing its evaluation function and weight fuzzy control.Integrate the enhanced algorithm to achieve the optimal global path and real-time obstacle avoidance function.Simulation experiment outcomes indicate that the enhanced A* algorithm achieves an average reduction of approximately 1.82% in path length and 61.24% in execution time.For instance,when tested on a 30×30 map, this method achieves a 4.45% reduction in path length, a 6.12% decrease in execution time, a 70.3% reduction in the count of turning points, and a 74.93% minimization of cumulative turning angles.Subsequently,the algorithm's practicality and validity are further confirmed through real vehicle testing.