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

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.

辐射观测数据的收集和插值对于支持全球核领域的常规操作以及在危机应对期间完成勘测至关重要。为了减少人类工作人员在这类勘测中可能受到的电离辐射暴露，人们强烈希望使用机器人系统。以前从核设施获取测量值进行插值以重建环境辐射图的方法无法准确应用于机器人勘测所收集的数据，因为它们无法很好地处理不规则间隔、有噪声且计数低的数据。在这项工作中，提出了一种对机器人收集的辐射测量值进行插值的新方法，该方法克服了与稀疏和有噪声测量相关的问题。所提出的方法将一个合适的核（以辐射传输代码MCNP6为基准）集成到高斯过程回归技术中。通过将该技术应用于从一个定制的机器人系统收集的数据，证明了所提技术的适用性，该机器人系统用于在稳态运行期间对约瑟夫·斯特凡研究所的TRIGA Mark II核反应堆进行勘测，结果表明它成功地重建了反应堆大厅中的伽马剂量估计值，并有助于识别电离辐射源。