Mobile Robotic Radiation Surveying With Recursive Bayesian Estimation and Attenuation Modeling

Both routine radiation surveys and incident response—as currently performed by human workers—are time-consuming and involve significant, potentially unanticipated radiation dose (especially for accident response where levels prohibit human presence entirely). Previous efforts addressed routine surveys using robotic systems, but they must also localize and characterize discrete sources when anomalies occur. To be effective, characterization must account for real-world complications, including multiple sources, environmental attenuation, spatial localization, and isotopic identification. Recursive Bayesian estimation (RBE) using grid-based estimators and particle filters has been previously investigated, but we eliminate previous open-space assumptions by including attenuation models and sensor position height in the sequential sampling strategy. The method also incorporates autonomous isotopic identification via gamma spectroscopy, which supports the attenuation modeling and improves the computational efficiency in multisource cases. In addition, survey points are autonomously optimized using the Fisher information. A radioactive decay model is implemented to generalize the method by addressing short-lived nuclides. The developed hardware is evaluated in multiple scenarios where no operator intervention is required, effectively eliminating operator dose uptake from localization/characterization in real-world scenarios. Two different mobile robots are used, demonstrating the portability of the data collection and analysis software. Also, the generalized complexity is documented to support planning in future scenarios. Note to Practitioners —This article was motivated by the desire to automate tasks in radiation work spaces of nuclear facilities, particularly laboratory facilities such as those at Los Alamos National Laboratory. While there is a great deal of prior literature dealing with the simple problem of collecting radiation readings in some regular spatial patterns, there remains the more advanced problem of inferring characteristics of the radiation sources. The localization problem has been studied in academia, but the developed solutions fall short of being practically deployable in real facilities. Our goal with this work is to enhance the sophistication of the radiation data analysis and modeling in order to shrink the gap between the state of the art and the demands of the target environments.