Complete Coverage Autonomous Underwater Vehicles Path Planning Based on Glasius Bio-Inspired Neural Network Algorithm for Discrete and Centralized Programming

被引：125

作者：

Sun, Bing ^{[1
]}

Zhu, Daqi ^{[1
]}

Tian, Chen ^{[1
]}

Luo, Chaomin ^{[2
]}

机构：

[1] Shanghai Maritime Univ, Lab Underwater Vehicles & Intelligent Syst, Shanghai 201306, Peoples R China

[2] Univ Detroit Mercy, Dept Elect & Comp Engn, Detroit, MI 48221 USA

来源：

IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS | 2019年 / 11卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Autonomous underwater vehicles (AUVs); complete coverage; Glasius bio-inspired neural network (GBNN); path planning; MULTIROBOT COVERAGE; DYNAMICS; AUV;

D O I：

10.1109/TCDS.2018.2810235

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

For the complete coverage path planning of autonomous underwater vehicles (AUVs), a new strategy with Glasius bio-inspired neural network (GBNN) algorithm with discrete and centralized programming is proposed. The basic modeling for multi-AUVs complete coverage problem based on grid map and neural network is discussed first. Then, the design for single AUV complete coverage is introduced based on GBNN algorithm which is a new developed tool with small amount of calculation and high efficiency. In order to solve the difficulty of single AUV full coverage task of large water range, the multi-AUV full coverage discrete and centralized programming is proposed based on GBNN algorithm. The simulation experiment is conducted to confirm that through the proposed algorithm, multi-AUVs can plan reasonable and collision-free coverage path and reach full coverage on the same task area with division of labor and cooperation.

引用

页码：73 / 84

页数：12

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