The Autonomous Path Planning Algorithm for the USV Under Complex Battlefield Situations

Abstract

The autonomous path planning problem of the USV in the complex battlefield situation environment is studied, an autonomous path planning algorithm for the USV based on polar coordinate modeling and crowd search algorithm is proposed. First, the basic principles of the crowd search algorithm are studied, and the self-interest behavior, altruistic behavior, pre-action behavior and uncertainty reasoning behavior of the crowd search algorithm are analyzed in detail. Then the search step, search direction and individual position of the crowd search algorithm update the rules and algorithm flow. Next, the algorithm in this paper, the traditional differential evolution algorithm, and the artificial bee colony optimization algorithm are used for typical function optimization problems. Simulation experiments show that the algorithm proposed in this paper is significantly better than other algorithms. Finally, based on polar coordinate modeling, the USV path code is placed in two-dimensional polar coordinates, and then the distance relationship between the current path point, the next path point and the obstacle point of the USV are used to determine whether to plan the path. When obstacles are encountered, the shortest and absolutely safe navigation route is obtained by calling the crowd search algorithm for path planning. Simulation experiments verify the effectiveness of the algorithm proposed in this paper.