Use of Gaussian process regression for radiation mapping of a nuclear reactor with a mobile robot

Abstract Collection and interpolation of radiation observations is of vital importance to support routine operations in the nuclear sector globally, as well as for completing surveys during crisis response. To reduce exposure to ionizing radiation that human workers can be subjected to during such s...

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Autores principales:	Andrew West, Ioannis Tsitsimpelis, Mauro Licata, Anz̆e Jazbec, Luka Snoj, Malcolm J. Joyce, Barry Lennox
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/93f1a43364e2407ca248a46714b4bbbc
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spelling	oai:doaj.org-article:93f1a43364e2407ca248a46714b4bbbc2021-12-02T16:14:46ZUse of Gaussian process regression for radiation mapping of a nuclear reactor with a mobile robot10.1038/s41598-021-93474-42045-2322https://doaj.org/article/93f1a43364e2407ca248a46714b4bbbc2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93474-4https://doaj.org/toc/2045-2322Abstract Collection and interpolation of radiation observations is of vital importance to support routine operations in the nuclear sector globally, as well as for completing surveys during crisis response. To reduce exposure to ionizing radiation that human workers can be subjected to during such surveys, there is a strong desire to utilise robotic systems. Previous approaches to interpolate measurements taken from nuclear facilities to reconstruct radiological maps of an environment cannot be applied accurately to data collected from a robotic survey as they are unable to cope well with irregularly spaced, noisy, low count data. In this work, a novel approach to interpolating radiation measurements collected from a robot is proposed that overcomes the problems associated with sparse and noisy measurements. The proposed method integrates an appropriate kernel, benchmarked against the radiation transport code MCNP6, into the Gaussian Process Regression technique. The suitability of the proposed technique is demonstrated through its application to data collected from a bespoke robotic system used to conduct a survey of the Joz̆ef Stefan Institute TRIGA Mark II nuclear reactor during steady state operation, where it is shown to successfully reconstruct gamma dosimetry estimates in the reactor hall and aid in identifying sources of ionizing radiation.Andrew WestIoannis TsitsimpelisMauro LicataAnz̆e JazbecLuka SnojMalcolm J. JoyceBarry LennoxNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Andrew West Ioannis Tsitsimpelis Mauro Licata Anz̆e Jazbec Luka Snoj Malcolm J. Joyce Barry Lennox Use of Gaussian process regression for radiation mapping of a nuclear reactor with a mobile robot
description	Abstract Collection and interpolation of radiation observations is of vital importance to support routine operations in the nuclear sector globally, as well as for completing surveys during crisis response. To reduce exposure to ionizing radiation that human workers can be subjected to during such surveys, there is a strong desire to utilise robotic systems. Previous approaches to interpolate measurements taken from nuclear facilities to reconstruct radiological maps of an environment cannot be applied accurately to data collected from a robotic survey as they are unable to cope well with irregularly spaced, noisy, low count data. In this work, a novel approach to interpolating radiation measurements collected from a robot is proposed that overcomes the problems associated with sparse and noisy measurements. The proposed method integrates an appropriate kernel, benchmarked against the radiation transport code MCNP6, into the Gaussian Process Regression technique. The suitability of the proposed technique is demonstrated through its application to data collected from a bespoke robotic system used to conduct a survey of the Joz̆ef Stefan Institute TRIGA Mark II nuclear reactor during steady state operation, where it is shown to successfully reconstruct gamma dosimetry estimates in the reactor hall and aid in identifying sources of ionizing radiation.
format	article
author	Andrew West Ioannis Tsitsimpelis Mauro Licata Anz̆e Jazbec Luka Snoj Malcolm J. Joyce Barry Lennox
author_facet	Andrew West Ioannis Tsitsimpelis Mauro Licata Anz̆e Jazbec Luka Snoj Malcolm J. Joyce Barry Lennox
author_sort	Andrew West
title	Use of Gaussian process regression for radiation mapping of a nuclear reactor with a mobile robot
title_short	Use of Gaussian process regression for radiation mapping of a nuclear reactor with a mobile robot
title_full	Use of Gaussian process regression for radiation mapping of a nuclear reactor with a mobile robot
title_fullStr	Use of Gaussian process regression for radiation mapping of a nuclear reactor with a mobile robot
title_full_unstemmed	Use of Gaussian process regression for radiation mapping of a nuclear reactor with a mobile robot
title_sort	use of gaussian process regression for radiation mapping of a nuclear reactor with a mobile robot
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/93f1a43364e2407ca248a46714b4bbbc
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Use of Gaussian process regression for radiation mapping of a nuclear reactor with a mobile robot

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