基于改进鲸鱼优化和流体扰动算法的AUV多目标点路径规划

马裕鸿; 庞文; 朱大奇

doi:10.11993/j.issn.2096-3920.2025-0054

基于改进鲸鱼优化和流体扰动算法的AUV多目标点路径规划

doi: 10.11993/j.issn.2096-3920.2025-0054

马裕鸿^{1, 2},
庞文^{1, 2,},
朱大奇^{1, 2}

1.
上海理工大学机械工程学院, 上海, 200093
2.
水下机器人与智能系统研究院, 上海, 200093

基金项目: 国家自然科学基金重点项目(62033009, 24510712400); 国家自然科学基金青年项目(52301378); 国家资助博士后研究人员计划(GZC20231677); 中国博士后科学基金(2023M742372).

详细信息

作者简介:
马裕鸿(1998-), 男, 硕士, 主要研究方向为AUV路径规划

通讯作者:
庞　文(1988-), 男, 博士, 助理研究员, 主要研究方向为多水下航行器协同控制与运动规划.

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- 文章访问数: 17
- HTML全文浏览量: 9
- PDF下载量: 2
- 被引次数: 0
出版历程
- 网络出版日期: 2025-06-13

Multi-objective point path planning for AUVs based on improved whale optimisation and fluid perturbation algorithms

MA Yuhong^{1, 2},
PANG Wen^{1, 2
,},
ZHU Daqi^{1, 2}

1.
School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
2.
Underwater Vehicle and Intelligent System Research Institute, Shanghai 200093, China

摘要

摘要: 针对在深海环境下, 自主水下航行器(AUV)在多目标点路径规划中效率低、传统鲸鱼优化算法(WOA)易陷入局部最优且难以适应三维避障需求的问题, 提出一种融合流体扰动算法与改进鲸鱼优化算法(IWOA)的协同规划策略。通过混沌映射生成高覆盖率初始解, 结合贪心算法构造局部最优序列, 设计混合种群初始化方法, 解决传统WOA随机初始化导致的解质量差问题。针对旅行商问题(TSP)的离散特性, 提出基于随机插入与局部翻转的离散化位置更新策略, 增强算法逃离局部最优能力。引入精英保留机制, 通过最优个体定向替换最差个体的迭代优化模式, 保障算法全局收敛性。在路径生成阶段, 建立三维流体扰动场模型, 通过障碍物扰动矩阵修正原始流场方向, 实现复杂障碍物环境下的连续避障。仿真结果表明: 文中所提IWOA算法较传统遗传算法和粒子群算法的平均路径长度分别减少15.4%和7.5%, 计算效率提升45.5%和16.8%。
- AUV /
- 多目标点 /
- 路径规划 /
- 改进鲸鱼优化 /
- 旅行商问题 /
- 流体扰动算法
Abstract: To address the challenges of low path planning efficiency for Autonomous Underwater Vehicles (AUVs) in multi-objective path planning in deep-sea environment, as well as the limitations of the traditional Whale Optimization Algorithm (WOA) in terms of susceptibility to local optima and inadequate adaptability to three-dimensional obstacle avoidance requirements, this study proposes a collaborative planning strategy that integrates an interfered fluid dynamic algorithm with an enhanced whale optimization approach. A hybrid population initialization method is developed by combining chaotic mapping to generate high-coverage initial solutions and a greedy algorithm to construct locally optimal sequences, effectively addressing the issue of poor solution quality caused by random initialization in traditional WOA. For the discrete characteristics of the Traveling Salesman Problem (TSP), a discrete position update strategy based on random insertion and local inversion is proposed, significantly enhancing the algorithm’s capability to escape local optima. An elite retention mechanism is introduced to ensure the global convergence of the algorithm through an iterative optimization framework that replaces the worst individuals with the optimal ones. During the path generation phase, a three-dimensional interfered fluid dynamic field model is established, where obstacle perturbation matrices adjust the original flow field direction to achieve continuous obstacle avoidance in complex environments. Simulation results demonstrate that the proposed algorithm reduces the average path length by 15.4% and 7.5% compared to traditional Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), respectively, while improving computational efficiency by 45.5% and 16.8%.
- AUV /
- multi-target /
- path planning /
- WOA /
- travelling salesman problem /
- interfered fluid

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图 1 原始流场

Figure 1. Original fluid field

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图 2 扰动流场

Figure 2. Interfered fluid field

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图 3 多目标路径规划流程图

Figure 3. Flow chart of multi-objective path planning

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图 4 WOA算法流程图

Figure 4. Flow chart of the WOA

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图 5 IWOA算法伪代码

Figure 5. Pseudocode of the IWOA

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图 6 障碍物环境建模与目标点设置

Figure 6. Obstacle environment modeling and target points setting

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图 7 规划路径对比图

Figure 7. Comparison map of planned paths

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图 8 IWOA规划结果

Figure 8. Planning results by IWOA

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图 9 PSO算法规划结果

Figure 9. Planning results by PSO algorithm

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图 10 GA规划结果

Figure 10. Planning results by GA

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图 11 3种算法平均转弯角度对比

Figure 11. Comparison of average turning angles of three algorithms

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图 12 3种算法性能对比

Figure 12. Performance comparison of three algorithms

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图 13 3种算法迭代曲线对比

Figure 13. Comparison of iteration curves of three algorithms

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图 14 复杂障碍物环境建模与目标点设置

Figure 14. Obstacle complex environment modeling and target points setting

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图 15 复杂环境下IWOA规划结果

Figure 15. Planning results by IWOA in complex environment

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图 16 复杂环境下PSO算法规划结果

Figure 16. Planning results by PSO algorithm in complex environment

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图 17 复杂环境下GA规划结果

Figure 17. planning results by GA in complex environment

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图 18 复杂环境下3种算法迭代曲线对比

Figure 18. Comparison of iteration curves of three algorithms in complex environment

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图 19 复杂洋流干扰与动态障碍物

Figure 19. Current disturbances and dynamic obstacles

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表 1 目标点中心坐标

Table 1. Coordinates of the centre of target points

编号	中心坐标	编号	中心坐标
1	[40, −90, 20]	5	[100, −135, 20]
2	[230, −80, 30]	6	[100, 20, 20]
3	[150, 100, 20]	7	[150, −75, 20]
4	[20, 100, 10]	8	[200, 25, 20]

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表 2 参数设置

Table 2. parameter setting

参数名称	数值
种群数量	10
迭代次数	50
C/(m/s)	10
$ {\rho _0} $	1.5
$ {\sigma _0} $	0.1

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表 3 3种算法访问顺序对比

Table 3. Comparison of access orders of three algorithms

算法	访问顺序
PSO	[6, 8, 3, 4, 1, 5, 7, 2]
GA	[1, 5, 7, 8, 2, 6, 3, 4]
IWOA	[4, 6, 3, 8, 2, 7, 5, 1]

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表 4 复杂环境下3种算法访问顺序对比

Table 4. Comparison of access order of three algorithms in complex environment

算法	访问顺序
PSO	[2 13 7 14 4 8 9 6 3 12 10 11 5 1]
GA	[11 1 5 7 13 2 6 8 3 9 12 14 4 10]
IWOA	[14 4 10 12 9 3 8 6 11 1 5 7 2 13]

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