基于改进蚁群算法的水下无人机路径规划研究

为实现水下无人机在水域中自主作业的功能,对其设计一套合理的路径规划方案是非常有必要的。蚁群算法针对水下无人机路径规划方面有着非常好的效果,拥有不错的鲁棒性,但是传统的蚁群算法在解决路径规划问题时很容易出现局部最优解的问题。以传统蚁群遗传算法理论为根据,对其进行添加目标引导素、构建精英蚂蚁体系、更新信息素浓度这三方面的改进,使用栅格法构建水下环境分析模型,并以最短的路径为目的,规划一条从初始状态到目标状态的无碰安全途径,运用仿真的办法展开验证。结果显示：相较于传统算法,改进后的算法在求解速度和全局求解能力上有较大的优势。

RESEARCH ON PATH PLANNING OF UNDERWATER UAV BASED ON IMPROVED ANT COLONY ALGORITHM

In order to realize the autonomous operation of underwater drones in waters, it is necessary to design a reasonable path planning scheme for them. The ant colony algorithm has very good effects on the path planning of underwater drones, and has good robustness, but the traditional ant colony algorithm is prone to the problem of local optimal solution when solving the path planning problem. Based on the traditional ant colony genetic algorithm theory, it is improved by adding target guides, constructing an elite ant system, and updating the pheromone concentration. The grid method is used to build an underwater environment analysis model. The purpose is to plan a collision-free safety path from the initial state to the target state and use simulation to carry out verification. The results show that compared with traditional algorithms, the improved algorithm has greater advantages in solving speed and global solving ability.