| 基于改进量子粒子群算法的AUV路径规划研究 |
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| 引用本文： | 张瀚彬,史先鹏,刘喜梅.基于改进量子粒子群算法的AUV路径规划研究[J].海洋工程,2023,41(2):86-92. |
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| 作者姓名： | 张瀚彬 史先鹏 刘喜梅 |
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| 作者单位： | 1.青岛科技大学 自动化与电子工程学院，山东 青岛 266100
2.国家深海基地管理中心，山东 青岛 266237 |
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| 摘 要： | 针对海洋环境下自主水下机器人（AUV）的路径规划问题，提出了一种基于框架四叉树的改进量子粒子群算法（QPSO），首先使用框架四叉树的方法对障碍物建模，该方法提高了建模的精度且对后续算法的效率也有极大的改进，之后设计改进的量子粒子群算法，并且结合水下环境的特殊性设计适应度函数，综合考虑航线路径长度、偏转角度以及海流影响，使得算法可以在水下环境中寻得能耗最短的解路径。最后通过仿真试验验证，相比于传统的栅格法和粒子群算法，改进量子粒子群算法的运算时间更短，收敛速度更快，其独特的适应度函数可以使AUV能更好适应水下多变的环境，且能利用海流设计能耗更小的路径，具有很大的实用价值。
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| 关 键 词： | 自主水下机器人 量子粒子群算法 路径规划 海流 |
| 收稿时间： | 2022/2/22 0:00:00 |
An AUV path planning method based on improved quantum particle swarm optimization |
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| ZHANG Hanbin,SHI Xianpeng,LIU Ximei.An AUV path planning method based on improved quantum particle swarm optimization[J].Ocean Engineering,2023,41(2):86-92. |
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| Authors: | ZHANG Hanbin SHI Xianpeng LIU Ximei |
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| Abstract: | Aiming at the path planning problem of autonomous underwater vehicle in marine environment, an improved quantum particle swarm optimization algorithm (QPSO) based on frame quadtree is proposed. Firstly, the method of frame quadtree is used to model the obstacle, which improves the accuracy of modeling and greatly improves the efficiency of subsequent algorithms. Then, the improved quantum particle swarm optimization algorithm is designed, and the fitness function is designed according to the particularity of underwater environment, Considering the influence of route length, deflection angle and ocean current, the algorithm can find the solution path with the shortest energy consumption in underwater environment. Finally, the simulation results show that compared with the traditional grid method and particle swarm optimization algorithm, the method used in this paper has shorter operation time and faster convergence speed. Its unique fitness function can make the AUV better adapt to the changeable underwater environment, and can use the ocean current to design the path with less energy consumption, which has practical application value. |
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| Keywords: | autonomous underwater vehicle quantum particle swarm optimization path planning current |
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