Optimal search path planning for unmanned surface vehicle based on an improved genetic algorithm

被引：51

作者：

Guo, Hui ^{[1
]}

Mao, Zhaoyong ^{[1
]}

Ding, Wenjun ^{[1
]}

Liu, Peiliang ^{[1
]}

机构：

[1] Northwestern Polytech Univ, Sch Marine Sci & Technol, Key Lab Unmanned Underwater Vehicle, Minist Ind & Informat Technol, Xian 710072, Shaanxi, Peoples R China

来源：

COMPUTERS & ELECTRICAL ENGINEERING | 2019年 / 79卷

基金：

中国国家自然科学基金; 中国博士后科学基金;

关键词：

Unmanned surface vehicle (USV); Cumulative detection probability; Improved genetic algorithm; Adaptive mutation; SYSTEM;

D O I：

10.1016/j.compeleceng.2019.106467

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

A planning model for simultaneously optimizing the unmanned surface vehicle's (USV's) direction and speed is established for searching submarines. The USV detection model is achieved through the underwater sonar search principle. An improved genetic algorithm is employed for maximizing cumulative detection probability (CDP), which uses three control factors to control the direction and amplitude of mutation adaptively and improve the convergence speed. In the simulation, the escaping target is assumed unknown direction, and many reasonable and efficient search paths are obtained. The analysis results of the evolutionary curve show that the proposed algorithm has the advantages of strong stability and fast convergence and is suitable for USV search problem. (C) 2019 Elsevier Ltd. All rights reserved.

引用

页数：10

共 19 条

[1]

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