Towards Advanced Robotic Manipulations for Nuclear Decommissioning

Despite enormous remote handling requirements, remarkably very few robots are being used by the nuclear industry. Most of the remote handling tasks are still performed manually, using conventional mechanical master‐slave devices. The few robotic manipulators deployed are directly tele‐operated in rudimentary ways, with almost no autonomy or even a pre‐programmed motion. In addition, majority of these robots are under‐sensored (i.e. with no proprioception), which prevents them to use for automatic tasks. In this context, primarily this chapter discusses the human operator performance in accomplishing heavy‐duty remote handling tasks in hazardous environments such as nuclear decommissioning. Multiple factors are evaluated to analyse the human operators’ performance and workload. Also, direct human tele‐operation is compared against human‐supervised semi‐autonomous control exploiting computer vision. Secondarily, a vision‐guided solution towards enabling advanced control and automating the under‐sensored robots is presented. Maintaining the coherence with real nuclear scenario, the experiments are conducted in the lab environment and results are discussed

Simultaneous material segmentation and 3D reconstruction in industrial scenarios

Recognizing material categories is one of the core challenges in robotic nuclear waste decommissioning. All nuclear waste should be sorted and segregated according to its materials, and then different disposal post-process can be applied. In this paper, we propose a novel transfer learning approach to learn boundary-aware material segmentation from a meta-dataset and weakly annotated data. The proposed method is data-efficient, leveraging a publically available dataset for general computer vision tasks and coarsely labeled material recognition data, with only a limited number of fine pixel-wise annotations required. Importantly, our approach is integrated with a Simultaneous Localization and Mapping (SLAM) system to fuse the per-frame understanding delicately into a 3D global semantic map to facilitate robot manipulation in self-occluded object heaps or robot navigation in disaster zones. We evaluate the proposed method on the Materials in Context dataset over 23 categories and that our integrated system delivers quasi-real-time 3D semantic mapping with high-resolution images. The trained model is also verified in an industrial environment as part of the EU RoMaNs project, and promising qualitative results are presented. A video demo and the newly generated data can be found at the project website

Humboldt Bay Independent Spent Fuel Storage Installation: Site-Specific License Renewal Application

NRC Docket No. 72-27 - In accordance with the requirements of 10 CFR 72, PG&E has prepared this application for renewal of the site-specific license for the HB ISFSI. This application supports license renewal for an additional 40-year period beyond the end of the current license term of Materials License Number SNM-2514 (Docket No. 72-27). The original 20-year license will expire on November 17, 2025. This application is submitted, in accordance with 10 CFR 72.42(b) and includes the general, technical, and environmental supporting information required by applicable portions of Subpart B of 10 CFR Part 72

Humboldt Bay Independent Spent Fuel Storage Installation: Site-Specific License Renewal Application

NRC Docket No. 72-27 - In accordance with the requirements of 10 CFR 72, PG&E has prepared this application for renewal of the site-specific license for the HB ISFSI. This application supports license renewal for an additional 40-year period beyond the end of the current license term of Materials License Number SNM-2514 (Docket No. 72-27). The original 20-year license will expire on November 17, 2025. This application is submitted, in accordance with 10 CFR 72.42(b) and includes the general, technical, and environmental supporting information required by applicable portions of Subpart B of 10 CFR Part 72

Examination of Surface Deposits on Oldbury Reactor Core Graphite to Determine the Concentration and Distribution of ¹⁴C

Pile Grade A graphite was used as a moderator and reflector material in the first generation of UK Magnox nuclear power reactors. As all of these reactors are now shut down there is a need to examine the concentration and distribution of long lived radioisotopes, such as 14C, to aid in understanding their behaviour in a geological disposal facility. A selection of irradiated graphite samples from Oldbury reactor one were examined where it was observed that Raman spectroscopy can distinguish between underlying graphite and a surface deposit found on exposed channel wall surfaces. The concentration of 14C in this deposit was examined by sequentially oxidising the graphite samples in air at low temperatures (450°C and 600°C) to remove the deposit and then the underlying graphite. The gases produced were captured in a series of bubbler solutions that were analysed using liquid scintillation counting. It was observed that the surface deposit was relatively enriched with 14C, with samples originating lower in the reactor exhibiting a higher concentration of 14C. Oxidation at 600°C showed that the remaining graphite material consisted of two fractions of 14C, a surface associated fraction and a graphite lattice associated fraction. The results presented correlate well with previous studies on irradiated graphite that suggest there are up to three fractions of 14C; a readily releasable fraction (corresponding to that removed by oxidation at 450°C in this study), a slowly releasable fraction (removed early at 600°C in this study), and an unreleasable fraction (removed later at 600°C in this study)

A Novel Weakly-supervised approach for RGB-D-based Nuclear Waste Object Detection and Categorization

This paper addresses the problem of RGBD-based detection and categorization of waste objects for nuclear decommissioning. To enable autonomous robotic manipulation for nuclear decommissioning, nuclear waste objects must be detected and categorized. However, as a novel industrial application, large amounts of annotated waste object data are currently unavailable. To overcome this problem, we propose a weakly-supervised learning approach which is able to learn a deep convolutional neural network (DCNN) from unlabelled RGBD videos while requiring very few annotations. The proposed method also has the potential to be applied to other household or industrial applications. We evaluate our approach on the Washington RGBD object recognition benchmark, achieving the state-of-the-art performance among semi-supervised methods. More importantly, we introduce a novel dataset, i.e. Birmingham nuclear waste simulants dataset, and evaluate our proposed approach on this novel industrial object recognition challenge. We further propose a complete real-time pipeline for RGBD-based detection and categorization of nuclear waste simulants. Our weakly-supervised approach has demonstrated to be highly effective in solving a novel RGB-D object detection and recognition application with limited human annotations

Comparison and Screening of Nuclear Fuel Cycle Options in View of Sustainable Performance and Waste Management

Is it true that a nuclear technology approach to generate electric energy offers a clean, safe, reliable and affordable, i.e., sustainable option? In principle yes, however a technology impact on the environment strongly depends on the actual implementation bearing residual risks due to technical failures, human factors, or natural catastrophes. A full response is thus difficult and can be given first when the wicked multi-disciplinary issues get well formulated and “resolved”. These problems are lying at the interface between: the necessary R&D effort, the industrial deployment and the technology impact in view of the environmental sustainability including the management of produced hazardous waste. As such, this problem is clearly of multi-dimensional nature. This enormous complexity indicates that just a description of the problem might cause a dilemma. The paper proposes a novel holistic approach applying Multi-Criteria Decision Analysis to assess the potential of nuclear energy systems with respect to a sustainable performance. It shows how to establish a multi-level criteria structure tree and examines the trading-off techniques for scoring and ranking of options. The presented framework allows multi-criteria and multi-group treatment. The methodology can be applied to support any pre-decisional process launched in a country to find the best nuclear and/or non-nuclear option according to national preferences and priorities. The approach addresses major aspects of the environmental footprint of nuclear energy systems. As a case study, advanced nuclear fuel cycles are analyzed, which were previously investigated by the Nuclear Energy Agency (NEA/OECD) expert group WASTEMAN. Sustainability facets of waste management, resource utilization and economics are in focus

Safety Evaluation Report: Humboldt Bay Independent Spent Fuel Storage Installation

DOCKET NO. 72-27 Materials License No. SNM-251