Learning-Based Path Planning Algorithm in Ocean Currents for Multi-Glider

In practical application studies of glider formations, ocean currents are a major influencing factor in their path planning. The purpose of this paper is to solve the path planning problem of glider formations in time-varying ocean currents and establish gliders, glider formation, and ocean current...

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Main Authors: Wei Lan, Xiang Jin, Xin Chang, Tianlin Wang, Han Zhou
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2022/1715226
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author Wei Lan
Xiang Jin
Xin Chang
Tianlin Wang
Han Zhou
author_facet Wei Lan
Xiang Jin
Xin Chang
Tianlin Wang
Han Zhou
author_sort Wei Lan
collection DOAJ
description In practical application studies of glider formations, ocean currents are a major influencing factor in their path planning. The purpose of this paper is to solve the path planning problem of glider formations in time-varying ocean currents and establish gliders, glider formation, and ocean current models based on existing data. The Doc-CNN architecture is tailored to conform to the operation and environment characteristics of gliders in practical application. After experiments, the algorithm of the improved architecture can be used in the path planning task of glider formation. The algorithm architecture is compared and tested on two datasets, grid maps and ocean maps. Doc-CNN has an advantage over architecture without being tailored to glider characteristics and makes full use of known global information and local information collected by gliders themselves. The results show that the path planning problem of glider formation in ocean currents can be solved by using Doc-CNN.
format Article
id doaj-art-8847f04667584a8792c19f14003ef8d2
institution Kabale University
issn 1099-0526
language English
publishDate 2022-01-01
publisher Wiley
record_format Article
series Complexity
spelling doaj-art-8847f04667584a8792c19f14003ef8d22025-02-03T01:11:57ZengWileyComplexity1099-05262022-01-01202210.1155/2022/1715226Learning-Based Path Planning Algorithm in Ocean Currents for Multi-GliderWei Lan0Xiang Jin1Xin Chang2Tianlin Wang3Han Zhou4Naval Architecture and Ocean Engineering CollegeNaval Architecture and Ocean Engineering CollegeNaval Architecture and Ocean Engineering CollegeSchool of Marine SciencesThe 714 Institute of CSSCIn practical application studies of glider formations, ocean currents are a major influencing factor in their path planning. The purpose of this paper is to solve the path planning problem of glider formations in time-varying ocean currents and establish gliders, glider formation, and ocean current models based on existing data. The Doc-CNN architecture is tailored to conform to the operation and environment characteristics of gliders in practical application. After experiments, the algorithm of the improved architecture can be used in the path planning task of glider formation. The algorithm architecture is compared and tested on two datasets, grid maps and ocean maps. Doc-CNN has an advantage over architecture without being tailored to glider characteristics and makes full use of known global information and local information collected by gliders themselves. The results show that the path planning problem of glider formation in ocean currents can be solved by using Doc-CNN.http://dx.doi.org/10.1155/2022/1715226
spellingShingle Wei Lan
Xiang Jin
Xin Chang
Tianlin Wang
Han Zhou
Learning-Based Path Planning Algorithm in Ocean Currents for Multi-Glider
Complexity
title Learning-Based Path Planning Algorithm in Ocean Currents for Multi-Glider
title_full Learning-Based Path Planning Algorithm in Ocean Currents for Multi-Glider
title_fullStr Learning-Based Path Planning Algorithm in Ocean Currents for Multi-Glider
title_full_unstemmed Learning-Based Path Planning Algorithm in Ocean Currents for Multi-Glider
title_short Learning-Based Path Planning Algorithm in Ocean Currents for Multi-Glider
title_sort learning based path planning algorithm in ocean currents for multi glider
url http://dx.doi.org/10.1155/2022/1715226
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AT xiangjin learningbasedpathplanningalgorithminoceancurrentsformultiglider
AT xinchang learningbasedpathplanningalgorithminoceancurrentsformultiglider
AT tianlinwang learningbasedpathplanningalgorithminoceancurrentsformultiglider
AT hanzhou learningbasedpathplanningalgorithminoceancurrentsformultiglider