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

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Use of Gaussian process regression for radiation mapping of a nuclear reactor with a mobile robot. / West, A.; Tsitsimpelis, I.; Licata, M. et al.
In: Scientific Reports, Vol. 11, No. 1, 13975, 07.07.2021.

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West A, Tsitsimpelis I, Licata M, Jazbec A, Snoj L, Joyce MJ et al. Use of Gaussian process regression for radiation mapping of a nuclear reactor with a mobile robot. Scientific Reports. 2021 Jul 7;11(1):13975. doi: 10.1038/s41598-021-93474-4

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@article{5b7527edd2af4d4f9ec33b6f85ce7244,
title = "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 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 Jo{\u z}ef 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. ",
author = "A. West and I. Tsitsimpelis and M. Licata and A. Jazbec and L. Snoj and M.J. Joyce and B. Lennox",
year = "2021",
month = jul,
day = "7",
doi = "10.1038/s41598-021-93474-4",
language = "English",
volume = "11",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "1",

}

RIS

TY - JOUR

T1 - Use of Gaussian process regression for radiation mapping of a nuclear reactor with a mobile robot

AU - West, A.

AU - Tsitsimpelis, I.

AU - Licata, M.

AU - Jazbec, A.

AU - Snoj, L.

AU - Joyce, M.J.

AU - Lennox, B.

PY - 2021/7/7

Y1 - 2021/7/7

N2 - 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.

AB - 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.

U2 - 10.1038/s41598-021-93474-4

DO - 10.1038/s41598-021-93474-4

M3 - Journal article

VL - 11

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 1

M1 - 13975

ER -