Multi-Domain Informative Coverage Path Planning for a Hybrid Aerial Underwater Vehicle in Dynamic Environments

Hybrid aerial underwater vehicles (HAUV) are a new frontier for vehicles. They can operate both underwater and aerially, providing enormous potential for a wide range of scientific explorations. Informative path planning is essential to vehicle autonomy. However, covering an entire mission region is...

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Autores principales:	Xueyao Liang, Chunhu Liu, Zheng Zeng
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	underwater vehicle hybrid aerial underwater vehicle informative path planning Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/bbe2520059954f05b92068d8bcfa7a0b
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id	oai:doaj.org-article:bbe2520059954f05b92068d8bcfa7a0b
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spelling	oai:doaj.org-article:bbe2520059954f05b92068d8bcfa7a0b2021-11-25T18:12:14ZMulti-Domain Informative Coverage Path Planning for a Hybrid Aerial Underwater Vehicle in Dynamic Environments10.3390/machines91102782075-1702https://doaj.org/article/bbe2520059954f05b92068d8bcfa7a0b2021-11-01T00:00:00Zhttps://www.mdpi.com/2075-1702/9/11/278https://doaj.org/toc/2075-1702Hybrid aerial underwater vehicles (HAUV) are a new frontier for vehicles. They can operate both underwater and aerially, providing enormous potential for a wide range of scientific explorations. Informative path planning is essential to vehicle autonomy. However, covering an entire mission region is a challenge to HAUVs because of the possibility of a multidomain environment. This paper presents an informative trajectory planning framework for planning paths and generating trajectories for HAUVs performing multidomain missions in dynamic environments. We introduce the novel heuristic generalized extensive neighborhood search GLNS–k-means algorithm that uses k-means to cluster information into several sets; then through the heuristic GLNS algorithm, it searches the best path for visiting these points, subject to various constraints regarding path budgets and the motion capabilities of the HAUV. With this approach, the HAUV is capable of sampling and focusing on regions of interest. Our method provides a significantly more optimal trajectory (enabling collection of more information) than ant colony optimization (ACO) solutions. Moreover, we introduce an efficient online replanning scheme to adapt the trajectory according to the dynamic obstacles during the mission. The proposed replanning scheme based on KD tree enables significantly shorter computational times than the scapegoat tree methods.Xueyao LiangChunhu LiuZheng ZengMDPI AGarticleunderwater vehiclehybrid aerial underwater vehicleinformative path planningMechanical engineering and machineryTJ1-1570ENMachines, Vol 9, Iss 278, p 278 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	underwater vehicle hybrid aerial underwater vehicle informative path planning Mechanical engineering and machinery TJ1-1570
spellingShingle	underwater vehicle hybrid aerial underwater vehicle informative path planning Mechanical engineering and machinery TJ1-1570 Xueyao Liang Chunhu Liu Zheng Zeng Multi-Domain Informative Coverage Path Planning for a Hybrid Aerial Underwater Vehicle in Dynamic Environments
description	Hybrid aerial underwater vehicles (HAUV) are a new frontier for vehicles. They can operate both underwater and aerially, providing enormous potential for a wide range of scientific explorations. Informative path planning is essential to vehicle autonomy. However, covering an entire mission region is a challenge to HAUVs because of the possibility of a multidomain environment. This paper presents an informative trajectory planning framework for planning paths and generating trajectories for HAUVs performing multidomain missions in dynamic environments. We introduce the novel heuristic generalized extensive neighborhood search GLNS–k-means algorithm that uses k-means to cluster information into several sets; then through the heuristic GLNS algorithm, it searches the best path for visiting these points, subject to various constraints regarding path budgets and the motion capabilities of the HAUV. With this approach, the HAUV is capable of sampling and focusing on regions of interest. Our method provides a significantly more optimal trajectory (enabling collection of more information) than ant colony optimization (ACO) solutions. Moreover, we introduce an efficient online replanning scheme to adapt the trajectory according to the dynamic obstacles during the mission. The proposed replanning scheme based on KD tree enables significantly shorter computational times than the scapegoat tree methods.
format	article
author	Xueyao Liang Chunhu Liu Zheng Zeng
author_facet	Xueyao Liang Chunhu Liu Zheng Zeng
author_sort	Xueyao Liang
title	Multi-Domain Informative Coverage Path Planning for a Hybrid Aerial Underwater Vehicle in Dynamic Environments
title_short	Multi-Domain Informative Coverage Path Planning for a Hybrid Aerial Underwater Vehicle in Dynamic Environments
title_full	Multi-Domain Informative Coverage Path Planning for a Hybrid Aerial Underwater Vehicle in Dynamic Environments
title_fullStr	Multi-Domain Informative Coverage Path Planning for a Hybrid Aerial Underwater Vehicle in Dynamic Environments
title_full_unstemmed	Multi-Domain Informative Coverage Path Planning for a Hybrid Aerial Underwater Vehicle in Dynamic Environments
title_sort	multi-domain informative coverage path planning for a hybrid aerial underwater vehicle in dynamic environments
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/bbe2520059954f05b92068d8bcfa7a0b
work_keys_str_mv	AT xueyaoliang multidomaininformativecoveragepathplanningforahybridaerialunderwatervehicleindynamicenvironments AT chunhuliu multidomaininformativecoveragepathplanningforahybridaerialunderwatervehicleindynamicenvironments AT zhengzeng multidomaininformativecoveragepathplanningforahybridaerialunderwatervehicleindynamicenvironments
_version_	1718411520873857024

Multi-Domain Informative Coverage Path Planning for a Hybrid Aerial Underwater Vehicle in Dynamic Environments

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