Complete Coverage Path Planning of an Unmanned Surface Vehicle Based on a Complete Coverage Neural Network Algorithm

In practical applications, an unmanned surface vehicle (USV) generally employs a task of complete coverage path planning for exploration in a target area of interest. The biological inspired neural network (BINN) algorithm has been extensively employed in path planning of mobile robots, recently. In...

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Autores principales:	Peng-Fei Xu, Yan-Xu Ding, Jia-Cheng Luo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	unmanned surface vehicle complete coverage path planning biological inspired neural network algorithm A* algorithm Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581
Acceso en línea:	https://doaj.org/article/06fc35b707b64e99b4c4128f673af0d4
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id	oai:doaj.org-article:06fc35b707b64e99b4c4128f673af0d4
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spelling	oai:doaj.org-article:06fc35b707b64e99b4c4128f673af0d42021-11-25T18:03:47ZComplete Coverage Path Planning of an Unmanned Surface Vehicle Based on a Complete Coverage Neural Network Algorithm10.3390/jmse91111632077-1312https://doaj.org/article/06fc35b707b64e99b4c4128f673af0d42021-10-01T00:00:00Zhttps://www.mdpi.com/2077-1312/9/11/1163https://doaj.org/toc/2077-1312In practical applications, an unmanned surface vehicle (USV) generally employs a task of complete coverage path planning for exploration in a target area of interest. The biological inspired neural network (BINN) algorithm has been extensively employed in path planning of mobile robots, recently. In this paper, a complete coverage neural network (CCNN) algorithm for the path planning of a USV is proposed for the first time. By simplifying the calculation process of the neural activity, the CCNN algorithm can significantly reduce calculation time. To improve coverage efficiency and make the path more regular, the optimal next position decision formula combined with the covering direction term is established. The CCNN algorithm has increased moving directions of the path in grid maps, which in turn has further reduced turning-angles and makes the path smoother. Besides, an improved A* algorithm that can effectively decrease path turns is presented to escape the deadlock. Simulations are carried out in different environments in this work. The results show that the coverage path generated by the CCNN algorithm has less turning-angle accumulation, deadlocks, and calculation time. In addition, the CCNN algorithm is capable to maintain the covering direction and adapt to complex environments, while effectively escapes deadlocks. It is applicable for USVs to perform multiple engineering missions.Peng-Fei XuYan-Xu DingJia-Cheng LuoMDPI AGarticleunmanned surface vehiclecomplete coverage path planningbiological inspired neural network algorithmA* algorithmNaval architecture. Shipbuilding. Marine engineeringVM1-989OceanographyGC1-1581ENJournal of Marine Science and Engineering, Vol 9, Iss 1163, p 1163 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	unmanned surface vehicle complete coverage path planning biological inspired neural network algorithm A* algorithm Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581
spellingShingle	unmanned surface vehicle complete coverage path planning biological inspired neural network algorithm A* algorithm Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 Peng-Fei Xu Yan-Xu Ding Jia-Cheng Luo Complete Coverage Path Planning of an Unmanned Surface Vehicle Based on a Complete Coverage Neural Network Algorithm
description	In practical applications, an unmanned surface vehicle (USV) generally employs a task of complete coverage path planning for exploration in a target area of interest. The biological inspired neural network (BINN) algorithm has been extensively employed in path planning of mobile robots, recently. In this paper, a complete coverage neural network (CCNN) algorithm for the path planning of a USV is proposed for the first time. By simplifying the calculation process of the neural activity, the CCNN algorithm can significantly reduce calculation time. To improve coverage efficiency and make the path more regular, the optimal next position decision formula combined with the covering direction term is established. The CCNN algorithm has increased moving directions of the path in grid maps, which in turn has further reduced turning-angles and makes the path smoother. Besides, an improved A* algorithm that can effectively decrease path turns is presented to escape the deadlock. Simulations are carried out in different environments in this work. The results show that the coverage path generated by the CCNN algorithm has less turning-angle accumulation, deadlocks, and calculation time. In addition, the CCNN algorithm is capable to maintain the covering direction and adapt to complex environments, while effectively escapes deadlocks. It is applicable for USVs to perform multiple engineering missions.
format	article
author	Peng-Fei Xu Yan-Xu Ding Jia-Cheng Luo
author_facet	Peng-Fei Xu Yan-Xu Ding Jia-Cheng Luo
author_sort	Peng-Fei Xu
title	Complete Coverage Path Planning of an Unmanned Surface Vehicle Based on a Complete Coverage Neural Network Algorithm
title_short	Complete Coverage Path Planning of an Unmanned Surface Vehicle Based on a Complete Coverage Neural Network Algorithm
title_full	Complete Coverage Path Planning of an Unmanned Surface Vehicle Based on a Complete Coverage Neural Network Algorithm
title_fullStr	Complete Coverage Path Planning of an Unmanned Surface Vehicle Based on a Complete Coverage Neural Network Algorithm
title_full_unstemmed	Complete Coverage Path Planning of an Unmanned Surface Vehicle Based on a Complete Coverage Neural Network Algorithm
title_sort	complete coverage path planning of an unmanned surface vehicle based on a complete coverage neural network algorithm
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/06fc35b707b64e99b4c4128f673af0d4
work_keys_str_mv	AT pengfeixu completecoveragepathplanningofanunmannedsurfacevehiclebasedonacompletecoverageneuralnetworkalgorithm AT yanxuding completecoveragepathplanningofanunmannedsurfacevehiclebasedonacompletecoverageneuralnetworkalgorithm AT jiachengluo completecoveragepathplanningofanunmannedsurfacevehiclebasedonacompletecoverageneuralnetworkalgorithm
_version_	1718411660574588928

Complete Coverage Path Planning of an Unmanned Surface Vehicle Based on a Complete Coverage Neural Network Algorithm

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