###### Abstract  
For the local oscillation phenomenon of the APF algorithm in the face of static U-shaped obstacles, the path cusp phenomenon caused by the vehicle corner and path curvature constraints is not taken into account, as well as the low path safety caused by ignoring the vehicle volume constraints. Therefore, an APF-Dijkstra path planning fusion algorithm based on steering model and volume constraints is proposed to improve it. First, perform an expansion treatment on the obstacles in the map, optimize the search direction of the Dijkstra algorithm and its planned global path, ensuring that the distance between the path and the expanded grid is no less than 1 m, and use the path points as temporary target points for the APF algorithm. Secondly, a Gaussian function is introduced to optimize the potential energy function of the APF algorithm, and the U-shaped obstacle is ellipticized, and a virtual target point is used to provide the gravitational force. Again, the three-point arc method based on the steering model is used to determine the location of the predicted points and to smooth the paths in real time while constraining the steering angle. Finally, a 4.5 m \(\times\) 2.5 m vehicle rectangle is used instead of the traditional mass points to make the algorithm volumetrically constrained. Meanwhile, a model for detecting vehicle collisions is established to cover the rectangle boundary with 14 envelope circles, and the combined force of the computed mass points is transformed into the combined force of the computed envelope circles to further improve path safety. The algorithm is validated by simulation experiments, and the results show that the fusion algorithm can avoid static U-shaped obstacles and dynamic obstacles well; the curvature change rate of the obstacle avoidance path is 0.248, 0.162, and 0.169, and the curvature standard deviation is 0.16, which verifies the smoothness of the fusion algorithm. Meanwhile, the distances between the obstacles and the center of the rear axle of the vehicle are all higher than 1.60 m, which verifies the safety of the fusion algorithm. p 2025 1  
point by constructing a potential field [7], but it is easy to fall into local minima, and the path is not smooth enough, so it has been studied by a wide range of scholars. [PERSON] et al. [8] improved the APF method and overcame the tendency to fall into local minima. However, this method relies on the pre-drawn map and does not consider environmental changes, such as the addition of new obstacles. [PERSON] et al. [9] integrated the simulated annealing algorithm with APF to optimize the local minimal problem, which can achieve obstacle avoidance. [PERSON] et al. [10] propose the concepts of virtual pendant points and temporary forces to provide a path deviation-based attraction for USVs and to break the local minima dilemma, but there are limitations in the generalization of the algorithms, which do not take into account factors such as the size of the USVs. [PERSON] et al. [11] proposed an optimization algorithm combining deep reinforcement learning and APF, which improved the path planning ability in complex environments. However, in the experiment, obstacles of different shapes were not considered, especially irregular obstacles such as U-shaped obstacles, and the generalization ability of the algorithm was not fully verified. [PERSON] et al. [12] propose a two-way A*-APF fusion algorithm to improve the repulsive field function for the local optimal solution and oscillation problem of the artificial potential field method. However, the algorithm generalization is limited, and the volume factor of the algorithm carrier is not considered. [PERSON] et al. [13] found that combining the simulated annealing algorithm and adding angle and distance constraints can effectively avoid dynamic obstacles. However, it does not consider how to avoid U-shaped obstacles when they are randomly generated, nor does it consider the volume factor of the carrier. [PERSON] et al.