关键词: 改进蚁群算法, 粒子群算法, 三维栅格模型, 自主式水下机器人, 三维路径规划

Abstract:

The traditional ant colony optimization has some problems, such as weak initial path finding ability and slow algorithm convergence speed in solving the three-dimensional path planning problem of autonomous underwater vehicles. In this paper, a new method which combines Particle Swarm Optimization with improved Ant Colony Optimization（PSO-ACO） is proposed. The three-dimensional grid model is used to model the marine environment based on the idea of spatial stratification, and the path evaluation model is established by considering the length, ruggedness and danger of the path. Particle swarm optimization is firstly used for path pre-search to optimize the initial pheromone, and then the ant colony optimization is used to realize the global path planning, which improves the state transition rule, pheromone update method and adds the reward and punishment mechanism. The experiments show that the algorithm can effectively improve the ability of initial path finding and global searching, reduce the number of convergence iterations and shorten the search time.

Key words: improved ant colony optimization, particle swarm optimization, three-dimensional grid model, autonomous underwater vehicle, three-dimensional path planning