Optimal Route Search Based on Multi-objective Genetic Algorithm for Maritime Navigation Vessels
Optimal Route Search Based on Multi-objective Genetic Algorithm for Maritime Navigation Vessels
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DOI：
10.1007/978-3-030-50017-7_38
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
R. Saga;Zhipeng Liang;N. Hara;Y. Nihei
中科院分区：
文献类型：
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作者：
R. Saga;Zhipeng Liang;N. Hara;Y. Nihei
海洋研究需要定期收集海洋数据，其中通常使用自主机器人船。然而，与陆地数据的收集相比，海平面数据的收集面临以下问题。首先，机器人船受到海面风、波浪和潮汐的影响，其强度和方向不断变化。其次，当多艘船同时工作时，必须防止船体碰撞。第三，由于自主帆船电力的限制，在广阔海域进行采集时，必须考虑机器人船的电力消耗。第四，必须避开固定障碍物，例如海面上的岛屿。考虑到这些问题，目前还没有有效的导航路线搜索系统。本文根据实际情况，设计了海面复杂情况下的航路系统。基于机器人船舶数量，采用聚类方法按距离对采集点进行分类，并采用多目标遗传算法确定每种分类的最优路径。
Ocean research requires regular collection of ocean data, wherein an autonomous robotic ship is usually used. However, in contrast to collecting land-based data, collecting sea level data face the following problems. First, robot ships are affected by sea surface winds, waves, and tides, with constantly changing strength and direction. Second, hull collisions must be prevented when multiple ships are working simultaneously. Third, given the limitation of the electric power of the autonomous sailing ship, the electric power consumption of the robot ship must be considered when collecting over a wide sea. Fourth, fixed obstacles, such as an island on the sea surface, must be avoided. Given such issues, no effective navigation route search system is currently available. In this work, a navigation route system for complex situations on the sea surface was designed on the basis of the actual situation. Clustering method was used to classify collection points according to distance based on the number of robot ships, and a multi-objective genetic algorithm was used to determine the optimal path for each classification.