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

West, A. and Tsitsimpelis, I. and Licata, M. and Jazbec, A. and Snoj, L. and Joyce, M.J. and Lennox, B. (2021) Use of Gaussian process regression for radiation mapping of a nuclear reactor with a mobile robot. Scientific Reports, 11 (1). ISSN 2045-2322

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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.

Item Type:
Journal Article
Journal or Publication Title:
Scientific Reports
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1000
Subjects:
ID Code:
157576
Deposited By:
Deposited On:
28 Jul 2021 11:35
Refereed?:
Yes
Published?:
Published
Last Modified:
20 Oct 2021 06:09