关键词: 全局路径规划, 人工鱼群算法, 蚁群算法, 初始信息素分布, 拥挤度因子

Abstract: To solve the problem of global path planning of underwater unmanned vehicles (UUVs) in a three-dimensional environment, this study examines a fusion algorithm for fish swarm and ant colony that optimizes the initial pheromone distribution and transfer probability of UUVs. The fusion algorithm improves the state expression and moving step of the artificial fish swarm algorithm. The heuristic value and pheromone of the ant colony algorithm are also optimized. Using the congestion factor, the transfer probability of traditional ant colony algorithms is improved, and the new algorithm is capable of global optimization. Based on grid modeling of the actual marine environment data, we take the path length as the measurement index to simulate and verify the algorithm through MATLAB. The experimental results indicate that the initial convergence speed of the fusion algorithm is faster, the optimal fitness value is higher, and the executed time is shortened, verifying the effectiveness of the algorithm.

Key words: globalpathplanning, artificialfishswarmalgorithm, antcolonyoptimization, initialpheromonedistribution, congestionfactor