In order to improve the autonomy and safety of the transport robot in navigation, it is necessary to plan the robot path reasonably to achieve the goal of optimal path and shortest time. For the problems of the classic A* algorithm in the process of path planning, such as long search time, redundant paths, many inflection points and unsmooth paths, and insufficient ability to avoid dynamic obstacles, an improved AFD (A* Fuzzy-DWA) fusion algorithm was proposed. Aiming at the problems of long search time and many traversal nodes in the planning path of the classical A* algorithm, the obstacle rate evaluation index was proposed to optimize the evaluation function. By calculating the proportion of obstacle grid in the global map grid, it was used as the weight of the evaluation function to reduce the number of nodes in the algorithm. Aiming at the problem of high redundancy of the path planned by the classical A* algorithm, the Smooth Floyd method was proposed according to the Floyd algorithm idea. Through the three times optimization of the initial path, the runnable path with less inflection points and small turning angles was obtained. Aiming at the problem of unsmooth path planning, the inner tangent smoothing strategy was used to optimize the path, and the turning angle in the path was optimized into an arc, which avoided the security threat caused by excessive turning and made the robot run more smoothly. In order to improve the efficiency of local path planning, the fuzzy reasoning method of evaluation function weight was proposed. By calculating the distance between the robot and the target point and the safety point, the evaluation function weight was dynamically adjusted according to the distance, so as to ensure that the robot could reach the predetermined point safely and timely. Experiments showed that compared with the comparison algorithm, the global and local path length of AFD algorithm in the static simulation environment was reduced by 6.6%, 6.9% and 3.3%, 2.7%, and the running time was shortened by 62.1%, 42.1% and 29.7%, 21.1%, respectively. In the dynamic simulation environment, the global and local path lengths were reduced by 11.4%, 7.8% and 8.3%, 4%, and the running time was shortened by 53.1%, 37.5% and 58.4%, 32.6%, respectively. The actual scenario verification further confirmed the effectiveness of the algorithm in improving the autonomous navigation ability and safety of transportation robots.