Path Planning for Underwater Information Gathering Based on Genetic Algorithms and Data Stochastic Models

Recent technological developments have paved the way to the employment of Autonomous Underwater Vehicles (AUVs) for monitoring and exploration activities of marine environments. Traditionally, in information gathering scenarios for monitoring purposes, AUVs follow predefined paths that are not effic...

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Autores principales:	Matteo Bresciani, Francesco Ruscio, Simone Tani, Giovanni Peralta, Andrea Timperi, Eric Guerrero-Font, Francisco Bonin-Font, Andrea Caiti, Riccardo Costanzi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	genetic algorithm path planning Gaussian Process AUVs optimal sampling Posidonia Oceanica Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581
Acceso en línea:	https://doaj.org/article/7cb8ecfc1b78463db6da0d5b7d120e71
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id	oai:doaj.org-article:7cb8ecfc1b78463db6da0d5b7d120e71
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spelling	oai:doaj.org-article:7cb8ecfc1b78463db6da0d5b7d120e712021-11-25T18:03:59ZPath Planning for Underwater Information Gathering Based on Genetic Algorithms and Data Stochastic Models10.3390/jmse91111832077-1312https://doaj.org/article/7cb8ecfc1b78463db6da0d5b7d120e712021-10-01T00:00:00Zhttps://www.mdpi.com/2077-1312/9/11/1183https://doaj.org/toc/2077-1312Recent technological developments have paved the way to the employment of Autonomous Underwater Vehicles (AUVs) for monitoring and exploration activities of marine environments. Traditionally, in information gathering scenarios for monitoring purposes, AUVs follow predefined paths that are not efficient in terms of information content and energy consumption. Informative Path Planning (IPP) represents a valid alternative, defining the path that maximises the gathered information. This work proposes a Genetic Path Planner (GPP), which consists in an IPP strategy based on a Genetic Algorithm, with the aim of generating a path that simultaneously maximises the information gathered and the coverage of the inspected area. The proposed approach has been tested offline for monitoring and inspection applications of Posidonia Oceanica (PO) in three different geographical areas. The a priori knowledge about the presence of PO, in probabilistic terms, has been modelled utilising a Gaussian Process (GP), trained on real marine data. The GP estimate has then been exploited to retrieve an information content of each position in the areas of interest. A comparison with other two IPP approaches has been carried out to assess the performance of the proposed algorithm.Matteo BrescianiFrancesco RuscioSimone TaniGiovanni PeraltaAndrea TimperiEric Guerrero-FontFrancisco Bonin-FontAndrea CaitiRiccardo CostanziMDPI AGarticlegenetic algorithmpath planningGaussian ProcessAUVsoptimal samplingPosidonia OceanicaNaval architecture. Shipbuilding. Marine engineeringVM1-989OceanographyGC1-1581ENJournal of Marine Science and Engineering, Vol 9, Iss 1183, p 1183 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	genetic algorithm path planning Gaussian Process AUVs optimal sampling Posidonia Oceanica Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581
spellingShingle	genetic algorithm path planning Gaussian Process AUVs optimal sampling Posidonia Oceanica Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 Matteo Bresciani Francesco Ruscio Simone Tani Giovanni Peralta Andrea Timperi Eric Guerrero-Font Francisco Bonin-Font Andrea Caiti Riccardo Costanzi Path Planning for Underwater Information Gathering Based on Genetic Algorithms and Data Stochastic Models
description	Recent technological developments have paved the way to the employment of Autonomous Underwater Vehicles (AUVs) for monitoring and exploration activities of marine environments. Traditionally, in information gathering scenarios for monitoring purposes, AUVs follow predefined paths that are not efficient in terms of information content and energy consumption. Informative Path Planning (IPP) represents a valid alternative, defining the path that maximises the gathered information. This work proposes a Genetic Path Planner (GPP), which consists in an IPP strategy based on a Genetic Algorithm, with the aim of generating a path that simultaneously maximises the information gathered and the coverage of the inspected area. The proposed approach has been tested offline for monitoring and inspection applications of Posidonia Oceanica (PO) in three different geographical areas. The a priori knowledge about the presence of PO, in probabilistic terms, has been modelled utilising a Gaussian Process (GP), trained on real marine data. The GP estimate has then been exploited to retrieve an information content of each position in the areas of interest. A comparison with other two IPP approaches has been carried out to assess the performance of the proposed algorithm.
format	article
author	Matteo Bresciani Francesco Ruscio Simone Tani Giovanni Peralta Andrea Timperi Eric Guerrero-Font Francisco Bonin-Font Andrea Caiti Riccardo Costanzi
author_facet	Matteo Bresciani Francesco Ruscio Simone Tani Giovanni Peralta Andrea Timperi Eric Guerrero-Font Francisco Bonin-Font Andrea Caiti Riccardo Costanzi
author_sort	Matteo Bresciani
title	Path Planning for Underwater Information Gathering Based on Genetic Algorithms and Data Stochastic Models
title_short	Path Planning for Underwater Information Gathering Based on Genetic Algorithms and Data Stochastic Models
title_full	Path Planning for Underwater Information Gathering Based on Genetic Algorithms and Data Stochastic Models
title_fullStr	Path Planning for Underwater Information Gathering Based on Genetic Algorithms and Data Stochastic Models
title_full_unstemmed	Path Planning for Underwater Information Gathering Based on Genetic Algorithms and Data Stochastic Models
title_sort	path planning for underwater information gathering based on genetic algorithms and data stochastic models
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7cb8ecfc1b78463db6da0d5b7d120e71
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_version_	1718411688832663552

Path Planning for Underwater Information Gathering Based on Genetic Algorithms and Data Stochastic Models

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