###### Abstract
[14] proposed an adaptive step size adjustment method, and the step size can be adjusted according to the distance between the robot and the obstacle as well as the number of iterations. However, the planned paths have cusps, are not smoothed, do not consider the dynamic obstacles, and do not take the volume of the carrier into account. [PERSON] et al. [15] achieved the optimization of the local optimal problem by measuring the distance to the obstacles to reduce unnecessary walking. However, this simulation experiment is relatively simple, and there are no randomly occurring obstacles. [PERSON] et al. [16] combine the APF algorithm with an improved PSO algorithm. However, the planned path is too close to the obstacles, and there is even a risk of diagonal penetration of the obstacle. Also, moving obstacles are not considered. [PERSON] et al. [17], irregular obstacles are wrapped with rectangular regions to avoid the robot searching for invalid rasters. However, this method is only applicable to static obstacles with known maps. [PERSON] et al. [18], virtual walls and virtual obstacles are created to guide the robot around the local minimum region. However, the curvature of the planned path is large, and no smoothing is performed. [PERSON] et al. [19] use a fifth-degree polynomial to fit discrete path points. However, this smoothing treatment is post-smoothing, and the post-smoothing treatment will be detached from the dynamic variability of the real situation. Based on the common problems of APF algorithms in the above studies, this article is aimed at (1) Localized oscillatory phenomena due to random generation of U-shaped obstacles; (2) When the target point is reached, the combined potential energy of the potential field is not zero, and the total potential field changes by a large amount; (3) The vehicle obstacle avoidance process produces the phenomenon of path cusps as well as the planned path does not conform to the vehicle steering constraints; (4) No real-time smoothing, not based on real-time dynamic feedback; (5) The volume influence of the algorithm carrier was not taken into account, and other defects to carry out the following work, the flowchart of the fusion algorithm in this paper is shown in Figure 1.  
1. When a U-shaped obstacle appears randomly and causes a local oscillation phenomenon, a virtual target point is generated near the U-shaped obstacle by ellipticizing the U-shaped obstacle to provide temporary gravitational force for the vehicle. 2. Introducing the Gaussian function and target distance factor in the combined potential energy function, when away from the target point, the repulsive potential energy will gradually equal the original potential energy, and the magnitude of the change of the total potential energy field will be smoother, and located in the vicinity of the target point, the value of the Gaussian function tends to be close to 0, which can ensure that the target point is the lowest point of the total potential energy of the whole field. 3. For the problems of path cusp phenomenon, more path folds, and planned paths not conforming to the vehicle steering constraints, the three-point arc method based on the steering model is used to determine the prediction points, and real-time smoothing is performed according to the vehicle model parameters. 4. The vehicle carrier is equated to a rectangle of 4.5 m \(\times\) 2.5 m, and 14 envelope circles are covered on the rectangle to increase the volume effect, and at the same time, the vehicle collision detection model is established to calculate the force of the envelope circles, which can be more accurate to determine whether a collision occurs.