Abstract: With the shortage of land resources and the worsening of the environment, the development of marine resources has become an important way to alleviate the shortage of land resources. At this time, the wave glider based on the large-scale long-time series of ocean observation and the harsh environment come into being, and the path planning of the glider in the marine environment is also very important. The ant colony algorithm with good positive feedback mechanism and environment interaction is selected as the path planning algorithm for wave glider. Individuals are allowed to execute different schemes according to their location and environment at different periods. In the state transition formula, the directional memory factors and the marine environment factors that can simulate the actual environment are added. Finally, the best individuals are screened out by different individuals and the best and most efficient routes are planned. The results show that compared with the traditional ant colony algorithm, the improved ant colony algorithm presented in this paper is more efficient and time-saving in planning the route, and can adjust and make the most reasonable path planning according to the different marine environment.

Key words: wave glider, antcolony algorithm, marine environment, path planning, mode change

摘要： 随着陆地资源短缺，环境恶化等问题的日益严峻，海洋资源的开发成为缓解陆地资源匮乏的重要途径。这时基于海洋观测大尺度长时序以及恶劣环境等特点的波浪滑翔机应运而生，波浪滑翔机在海洋环境中的路径规划也至关重要。具有良好的正反馈机制和环境互动性的蚁群算法作为波浪滑翔机的路径规划算法，让个体在不同时期根据自身位置和环境的不同来执行不同的方案，并在状态转移公式中加入可以模拟实际环境的海洋环境因素及方向记忆因子，通过不同个体的自身情况来筛选出最佳个体并规划出高效合理的最佳航线。结果表明，与传统蚁群算法相比，给出的改进蚁群算法在规划路径方面效率更高、用时更短，而且可以根据海洋环境的不同及时调节并作出最合理的航线规划。

关键词: 波浪滑翔机, 蚁群算法, 海洋环境, 路径规划, 模式转变