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The Inventory Verification through Detectors on Robotic Inspection Platforms project is developing methodsand technologies to enable robotic platforms to autonomously perform Safeguards-relevant inspection tasks.The two inspection tasks we are working to address are automated design verification and radiation-based nu-clear material accountancy inspections. Our approach to development focuses on integrating our Scene DataFusion (SDF)-based radiation detection systems, with robotic platforms and developing autonomy algorithmswithin the SDF software ecosystem. The SDF technology, developed at Lawrence Berkeley National Laboratory(LBNL), is approximately ten years old and couples radiation imaging techniques with computer vision tech-nologies to create context-informed 3D maps of radioactivity. It has been most widely applied to contaminationmapping and radiation search applications, but has also been applied to object inspection in materials accoun-tancy problems in the past. This project represents LBNL’s first effort at integrating SDF with autonomoussystems in a safeguards context. For the design verification task, we focus on indoor applications, where we areintegrating our detector systems with a Boston Dynamics Spot robot which is given navigation instructions bythe SDF computer. The Spot/SDF system will autonomously create a 3D map of a facility, concurrently noteareas that present heightened radiation hazards, and automatically locate clutter objects, as well as pipes andducts. For accountancy inspection tasks, we plan to leverage previously-developed computer vision technolo-gies to identify and count nuclear material containers, either using the Spot system indoors or a similar smallunmanned aerial vehicle (UAV)-based system for outdoor inspections. Once the nuclear material containersare identified, we are developing autonomy algorithms that automatically navigate to the containers and con-duct SDF-based radiation measurements to assess whether the containers’ contents match declarations withinpre-specified confidence levels (e.g., empty vs. full). When the number of containers is too high for each tobe individually surveyed, we will implement randomized sampling strategies in order to gain various levels ofstatistical precision on a broad set of items, yet randomly select a subset for additional scrutiny. This project,funded by Defense Nuclear Nonproliferation Research and Development (DNN-R&D), is in its second year ofa three-year project.

A handheld system developed to digitize a contextual understanding of the scene at a chemical, biological, radiological, nuclear and/or explosives (CBRNE) events is described. The system uses LiDAR and cameras to create a colorized 3D model of the environment, which helps domain experts that are supporting responders in the field. To generate the digitized model, a responder scans any suspicious objects and the surroundings by carrying the system through the scene. The scanning system provides a real-time user interface to inform the user about scanning progress and to indicate any areas that may have been missed either by the LiDAR sensors or the cameras. Currently, the collected data are post-processed on a different device, building a colorized triangular mesh of the encountered scene, with the intention of moving this pipeline to the scanner at a later point. The mesh is sufficiently compressed to be sent over a reduced bandwidth connection to a remote analyst. Furthermore, the system tracks fiducial markers attached to diagnostic equipment that is placed around the suspicious object. The resulting tracking information can be transmitted to remote analysts to further facilitate their supporting efforts. The paper will discuss the system's design, software components, the user interface used for scanning a scene, the necessary procedures for calibration of the sensors, and the processing steps of the resulting data. The discussion will close by evaluating the system's performance on 11 scenes.

By combining radiation detection technologies with robotics sensing, the ability to continuously conduct gamma-ray imaging using freely-moving systems was demonstrated in 2015.1 This new method, which was named free-moving 3D Scene Data Fusion (SDF), was then applied to mapping radioactive contamination and to contextualizing the extent of contamination and the efficacy of radiological clean-up efforts.2,3 Since then, further studies into the types of radiation detection systems to which SDF could be applied resulted in the discovery and demonstration that neutron activity could be mapped using neutron-sensitive CLLBC scintillators, arrays of pix-elated CZT detectors could be used to create multi-modal imagers, and more rudimentary detector systems such as arrays of four CsI modules could still achieve good-quality mapping by inferring source positioning through the encoded modulation of source-to-detector distance. This paper provides an overview of the SDF technology, highlights recent measurements leveraging SDF-equipped systems, discusses the continued development of quantitative algorithms4,5 and their ramifications for developing autonomous SDF-capabilities, and summarizes future directions of research and application development for free moving radiation detection systems.

Integration of contextual sensors into vehicle-borne mobile radiation detector systems delivers a rich description of the environment that could be used to estimate the complex and variable environmental gamma-ray backgrounds in urban areas. The predictions could potentially increase the sensitivity to illicit radiological and nuclear materials and could provide realistic inputs to urban radiological search simulations and algorithms. Recent work in this field has focused mainly on the predictive power of segmenting and classifying imagery from cameras and elected in its approach to aggregate the locations of gamma-ray interactions within the fielded detector array to a single point. This work builds upon the previous effort by leveraging LiDARs to create a 3D representation of the detector system and the surrounding scenery and demonstrates further improvement in the capability of attributing observed gamma-ray backgrounds to classes of surrounding materials.

This paper describes state-of-the-art digital filtering techniques that are part of GEANA, an automatic data analysis software used for the GERDA experiment. The discussed filters include a novel, nonlinear correction method for ballistic deficits, which is combined with one of three shaping filters: a pseudo-Gaussian, a modified trapezoidal, or a modified cusp filter. The performance of the filters is demonstrated with a 762 g Broad Energy Germanium (BEGe) detector, produced by Canberra, that measures γ-ray lines from radioactive sources in an energy range between 59.5 and 2614.5 keV. At 1332.5 keV, together with the ballistic deficit correction method, all filters produce a comparable energy resolution of ~1.61 keV FWHM. This value is superior to those measured by the manufacturer and those found in publications with detectors of a similar design and mass. At 59.5 keV, the modified cusp filter without a ballistic deficit correction produced the best result, with an energy resolution of 0.46 keV. It is observed that the loss in resolution by using a constant shaping time over the entire energy range is small when using the ballistic deficit correction method.