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Game Theory for Unmanned Vehicle Path Planning in the Marine Domain: State of the Art and New Possibilities
Article

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

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Dipartimento di Ingegneria dell’Informazione, Università di Pisa, 56122 Pisa, Italy
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Interuniversity Center of Integrated Systems for the Marine Environment (ISME), 16145 Genova, Italy
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Centro di Ricerca “E. Piaggio”, Università di Pisa, 56122 Pisa, Italy
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Systems Robotics and Vision Group, University of the Balearic Islands (UIB), 07122 Palma de Mallorca, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Marco Cococcioni and Pierre Lermusiaux
J. Mar. Sci. Eng. 2021, 9(11), 1183; https://doi.org/10.3390/jmse9111183
Received: 30 September 2021 / Revised: 21 October 2021 / Accepted: 23 October 2021 / Published: 27 October 2021
(This article belongs to the Special Issue Machine Learning and Remote Sensing in Ocean Science and Engineering)
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. View Full-Text
Keywords: genetic algorithm; path planning; Gaussian Process; AUVs; optimal sampling; Posidonia Oceanica; maritime monitoring and inspection task genetic algorithm; path planning; Gaussian Process; AUVs; optimal sampling; Posidonia Oceanica; maritime monitoring and inspection task
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MDPI and ACS Style

Bresciani, M.; Ruscio, F.; Tani, S.; Peralta, G.; Timperi, A.; Guerrero-Font, E.; Bonin-Font, F.; Caiti, A.; Costanzi, R. Path Planning for Underwater Information Gathering Based on Genetic Algorithms and Data Stochastic Models. J. Mar. Sci. Eng. 2021, 9, 1183. https://doi.org/10.3390/jmse9111183

AMA Style

Bresciani M, Ruscio F, Tani S, Peralta G, Timperi A, Guerrero-Font E, Bonin-Font F, Caiti A, Costanzi R. Path Planning for Underwater Information Gathering Based on Genetic Algorithms and Data Stochastic Models. Journal of Marine Science and Engineering. 2021; 9(11):1183. https://doi.org/10.3390/jmse9111183

Chicago/Turabian Style

Bresciani, Matteo, Francesco Ruscio, Simone Tani, Giovanni Peralta, Andrea Timperi, Eric Guerrero-Font, Francisco Bonin-Font, Andrea Caiti, and Riccardo Costanzi. 2021. "Path Planning for Underwater Information Gathering Based on Genetic Algorithms and Data Stochastic Models" Journal of Marine Science and Engineering 9, no. 11: 1183. https://doi.org/10.3390/jmse9111183

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