Study on path planning of unmanned surface vessel based on data-driven genetic algorithm

The genetic algorithm (GA) is an effective method for the path planning system of unmanned surface vessel (USV),but it is easy to fall into local optimal precocity and converges slowly.For this,without increasing the complexity of the algorithm,a data-driven linear changing parameters genetic algori...

Full description

Saved in:
Bibliographic Details
Main Authors: Junfeng XIN, Yongbo ZHANG, Jiageng BO, Bowen ZHAO, Shiyuan FAN
Format: Article
Language:zho
Published: POSTS&TELECOM PRESS Co., LTD 2019-06-01
Series:智能科学与技术学报
Subjects:
Online Access:http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.201926
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1846171169722466304
author Junfeng XIN
Yongbo ZHANG
Jiageng BO
Bowen ZHAO
Shiyuan FAN
author_facet Junfeng XIN
Yongbo ZHANG
Jiageng BO
Bowen ZHAO
Shiyuan FAN
author_sort Junfeng XIN
collection DOAJ
description The genetic algorithm (GA) is an effective method for the path planning system of unmanned surface vessel (USV),but it is easy to fall into local optimal precocity and converges slowly.For this,without increasing the complexity of the algorithm,a data-driven linear changing parameters genetic algorithm (LCPGA) was proposed,which can adjust adaptively control parameters in the shortest time.Compared with the traditional genetic algorithm,the LCPGA increases the diversity of the population,avoids falling into local optimum more effectively,and improves the accuracy,robustness and convergence speed of path planning.Then simulation experiments and field tests verify the more excellent performance of the LCPGA.This algorithm can be helpful in path planning for unmanned surface vessel.
format Article
id doaj-art-1e30d69a935e4662a8910522aa116147
institution Kabale University
issn 2096-6652
language zho
publishDate 2019-06-01
publisher POSTS&TELECOM PRESS Co., LTD
record_format Article
series 智能科学与技术学报
spelling doaj-art-1e30d69a935e4662a8910522aa1161472024-11-11T06:51:10ZzhoPOSTS&TELECOM PRESS Co., LTD智能科学与技术学报2096-66522019-06-01117118059635158Study on path planning of unmanned surface vessel based on data-driven genetic algorithmJunfeng XINYongbo ZHANGJiageng BOBowen ZHAOShiyuan FANThe genetic algorithm (GA) is an effective method for the path planning system of unmanned surface vessel (USV),but it is easy to fall into local optimal precocity and converges slowly.For this,without increasing the complexity of the algorithm,a data-driven linear changing parameters genetic algorithm (LCPGA) was proposed,which can adjust adaptively control parameters in the shortest time.Compared with the traditional genetic algorithm,the LCPGA increases the diversity of the population,avoids falling into local optimum more effectively,and improves the accuracy,robustness and convergence speed of path planning.Then simulation experiments and field tests verify the more excellent performance of the LCPGA.This algorithm can be helpful in path planning for unmanned surface vessel.http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.201926path planning;improved genetic algorithm;unmanned surface vessel;self-adaption
spellingShingle Junfeng XIN
Yongbo ZHANG
Jiageng BO
Bowen ZHAO
Shiyuan FAN
Study on path planning of unmanned surface vessel based on data-driven genetic algorithm
智能科学与技术学报
path planning;improved genetic algorithm;unmanned surface vessel;self-adaption
title Study on path planning of unmanned surface vessel based on data-driven genetic algorithm
title_full Study on path planning of unmanned surface vessel based on data-driven genetic algorithm
title_fullStr Study on path planning of unmanned surface vessel based on data-driven genetic algorithm
title_full_unstemmed Study on path planning of unmanned surface vessel based on data-driven genetic algorithm
title_short Study on path planning of unmanned surface vessel based on data-driven genetic algorithm
title_sort study on path planning of unmanned surface vessel based on data driven genetic algorithm
topic path planning;improved genetic algorithm;unmanned surface vessel;self-adaption
url http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.201926
work_keys_str_mv AT junfengxin studyonpathplanningofunmannedsurfacevesselbasedondatadrivengeneticalgorithm
AT yongbozhang studyonpathplanningofunmannedsurfacevesselbasedondatadrivengeneticalgorithm
AT jiagengbo studyonpathplanningofunmannedsurfacevesselbasedondatadrivengeneticalgorithm
AT bowenzhao studyonpathplanningofunmannedsurfacevesselbasedondatadrivengeneticalgorithm
AT shiyuanfan studyonpathplanningofunmannedsurfacevesselbasedondatadrivengeneticalgorithm