A Path Planning Algorithm based on Artificial Potential Field Method and Ant Colony Algorithm

In the UUV searching task, a key step is the path planning process, in the known search environment to plan a satisfactory path from the beginning to the end, directly determines the final success of the search task. There are many path planning algorithms and environment modeling methods for the purpose of searching a target. Artificial potential field method has the advantages of small computation, strong real-time performance and easy implementation, but it also has the problem of local minimum point, so that UUV can't move forward in the vicinity of the target point. Aiming at the above problems, in this paper, a new method based on artificial potential field method and ant colony algorithm is proposed. The repulsive force field in the artificial potential field method is combined with the pheromone in the ant colony algorithm. Firstly, the initial target path point set is assigned to the UUV, and then the target path point of the next iteration is selected from the path point set, and the trajectory points of the next iteration are selected according to the path points. During the iteration, the UUV gets closer and closer to the target, and the path point set is updated. The results show that the improved algorithm solves the path planning problem of UUV search in complex environment, has certain adaptability to different search environment, and is more in line with the actual application environment, and has better search performance.