Three Dimensional Path Planning of Unmanned Underwater Vehicle Based on Improved Manta Ray Foraging Optimization Algorithm

HUANG He; LI Xiaolei; YANG Lan; WANG Huifeng; RU Feng

doi:10.7652/xjtuxb202207002

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Three Dimensional Path Planning of Unmanned Underwater Vehicle Based on Improved Manta Ray Foraging Optimization Algorithm

更新时间：2025-07-09

- Three Dimensional Path Planning of Unmanned Underwater Vehicle Based on Improved Manta Ray Foraging Optimization Algorithm
- Vol. 56, Issue 7, Pages: 9-18(2022)
- 作者机构：
  
  1. 长安大学电子与控制工程学院,西安,710064
  2. 长安大学西安市智慧高速公路信息融合与控制重点实验室,西安,710064
  3. 长安大学信息工程学院,西安,710064
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202207002
  CLC： TP181
- Online First：10 July 2022，
  
  Published：2022
- 稿件说明：
移动端阅览
HUANG He, LI Xiaolei, YANG Lan, et al. Three Dimensional Path Planning of Unmanned Underwater Vehicle Based on Improved Manta Ray Foraging Optimization Algorithm[J]. 2022, 56(7): 9-18.
DOI：

HUANG He, LI Xiaolei, YANG Lan, et al. Three Dimensional Path Planning of Unmanned Underwater Vehicle Based on Improved Manta Ray Foraging Optimization Algorithm[J]. 2022, 56(7): 9-18. DOI： 10.7652/xjtuxb202207002.

摘要

针对复杂环境下传统群体智能优化算法在求解水下无人航行器(UUV)路径规划的过程中存在路径搜索能力不足、易陷入局部最优等问题

提出了一种引入改进蝠鲼觅食优化算法的UUV三维路径规划方法。首先

根据UUV在水下航行时的实际环境

建立相关地形模型和威胁源模型; 其次

对传统的蝠鲼觅食优化算法进行改进

相关改进包括在初始化过程中加入局部反向学习机制优化种群的位置

提高了种群的多样性; 根据每次迭代后种群个体适应度的不同

改进蝠鲼翻滚觅食的翻滚因子S

由此实现一种自适应翻滚

有利于跳出局部最优; 同时

在蝠鲼螺旋觅食过程中融合莱维飞行-柯西变异策略

扩大了搜索路径和种群搜索范围

提升了算法寻找全局最优的能力; 最后

将改进的蝠鲼觅食优化算法引入到UUV的路径规划中

进行相应的实验模拟。实验结果表明:在地形1中采用改进的蝠鲼觅食优化算法所规划的路径相比于灰狼算法和蝠鲼觅食优化算法分别降低了32.49 km和23.88 km

航迹代价分别降低了9.68和4.04; 在地形2中采用改进的蝠鲼觅食优化算法所规划的路径相较于灰狼算法和蝠鲼觅食优化算法分别降低了20.83 km和29.95 km

航迹代价分别降低了10.14和3.18; 同时

所提路径规划方法能够使UUV有效地避开障碍物、威胁物等

较大地降低了风险成本

安全性更高。

Abstract

As the traditional swarm intelligence optimization algorithm is not capable enough in path search and is easy to fall into local optimum in complex environment

the improved manta ray foraging optimization(IMRFO)algorithm was proposed for three dimensional path planning of an unmanned underwater vehicle(UUV). First

terrain and threat source models were established based on real underwater environment. Second

the traditional MRFO algorithm was improved

including the application of local reverse learning mechanism during initialization to optimize location of population and improve the population diversity. The individual fitness of the population after each iteration was based on to improve the tumbling factor S of the manta ray rolling foraging and thus realize an adaptive rolling which was conducive to getting out of local optimum. At the same time

integrating Levy flight-Cauchy mutation strategy in the manta ray spiral foraging process expanded the search path and population search range and improved the algorithm's ability to find the global optimum. Finally

the improved algorithm was applied to path planning of the UUV for experimental simulation. The results show that in terrain 1

the path planned with the improved algorithm reduces by 32.49 km and 23.88 km respectively and the track cost reduces by 9.68 and 4.04 respectively compared with the Wolf algorithm and the traditional MRFO algorithm and that in terrain 2

these reductions were 20.83 km and 29.95 km respectively and 10.14 and 3.18 respectively. Moreover

the proposed algorithm can enable the UUV to avoid obstacles

threats

etc.

thus reducing risk cost and improving safety.

关键词

Keywords

references

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