Zhang Songbai, Liu Qi, Chen Jie, Cao Yujin, Wang Guoqing
School of Physics and Electronic Engineering, Sichuan University of Science & Engineering, Zigong 643000, China.
Intelligent Perception and Control Key Laboratory of Sichuan Province, Sichuan University of Science & Engineering, Zigong 643000, China.
Sensors (Basel). 2025 Jul 31;25(15):4736. doi: 10.3390/s25154736.
Structural collapses caused by accidents or disasters could create unexpected radiation shielding, resulting in sharp gradients within the radiation field. Traditional radiation mapping methods often fail to accurately capture these complex variations, making the rapid and precise localization of radiation sources a significant challenge in emergency response scenarios. To address this issue, based on standard Gaussian process regression (GPR) models that primarily utilize a single Gaussian kernel to reflect the inverse-square law in free space, a novel multi-kernel Gaussian process regression (MK-GPR) model is proposed for high-fidelity radiation mapping in environments with physical obstructions. MK-GPR integrates two additional kernel functions with adaptive weighting: one models the attenuation characteristics of intervening materials, and the other captures the energy-dependent penetration behavior of radiation. To validate the model, gamma-ray distributions in complex, shielded environments were simulated using GEometry ANd Tracking 4 (Geant4). Compared with conventional methods, including linear interpolation, nearest-neighbor interpolation, and standard GPR, MK-GPR demonstrated substantial improvements in key evaluation metrics, such as , , and . Notably, the coefficient of determination () increased to 0.937. For practical deployment, the optimized MK-GPR model was deployed to an RK-3588 edge computing platform and integrated into a mobile robot equipped with a NaI(Tl) detector. Field experiments confirmed the system's ability to accurately map radiation fields and localize gamma sources. When combined with SLAM, the system achieved localization errors of 10 cm for single sources and 15 cm for dual sources. These results highlight the potential of the proposed approach as an effective and deployable solution for radiation source investigation in post-disaster environments.
事故或灾难导致的结构坍塌可能会产生意想不到的辐射屏蔽，从而在辐射场内形成急剧的梯度变化。传统的辐射映射方法往往无法准确捕捉这些复杂的变化，这使得在应急响应场景中快速、精确地定位辐射源成为一项重大挑战。为了解决这个问题，基于主要利用单个高斯核来反映自由空间中平方反比定律的标准高斯过程回归（GPR）模型，提出了一种新颖的多核高斯过程回归（MK-GPR）模型，用于在存在物理障碍物的环境中进行高保真辐射映射。MK-GPR集成了两个带有自适应权重的附加核函数：一个对中间材料的衰减特性进行建模，另一个捕捉辐射的能量依赖穿透行为。为了验证该模型，使用GEometry ANd Tracking 4（Geant4）模拟了复杂屏蔽环境中的伽马射线分布。与传统方法（包括线性插值、最近邻插值和标准GPR）相比，MK-GPR在关键评估指标（如 、 和 ）方面有了显著改进。值得注意的是，决定系数（ ）提高到了0.937。为了实际部署，将优化后的MK-GPR模型部署到RK-3588边缘计算平台，并集成到配备NaI(Tl)探测器的移动机器人中。现场实验证实了该系统准确映射辐射场和定位伽马源的能力。与同时定位与地图构建（SLAM）相结合时，该系统对于单源的定位误差为10厘米，对于双源的定位误差为15厘米。这些结果突出了所提出方法作为灾后环境中辐射源调查的有效且可部署解决方案的潜力。
