New approaches in training for in situ visualization of ionizing radiation measurements

Abstract

Surveying an environment for sources of ionizing radiation requires the use of measurement tools and skills developed from training in order to understand and analyze measurements. Recent advances in technology allow for new approaches to be made in the radiation survey methodology generally used with the incorporation of augmented reality (AR) technology to improve real-time awareness in situ and virtual reality (VR) technology to better develop the skill set of the surveyor in realistic virtual environments beforehand. This thesis investigates and develops a novel process to display real-time measured radiation monitoring data in AR to support a radiation surveyor during a search of an environment for hazardous sources of radiation. This AR process is then modified to show how it can be used with virtual radiation sources to allow a radiation surveyor to practice with a digital twin of a radiation field using a virtual source. This process is then modified further and shown how it can be adapted and used to develop VR training scenarios to teach the skill sets needed to assess potential hazards (radiological and non-radiological) and identify sources of radiation. Finally, an approach using reinforcement learning methods is developed and applied to demonstrate a strategy to localize a single radiation source leveraging the real-time measurement data taken in AR.