1,720,976 research outputs found
Development of Advanced Gamma Imaging Systems for robot supported radiological mapping for Nuclear Decommissioning
No full textNuclear decommissioning presents significant challenges due to the hazardous nature of radioactive environments and the need for precise radiological characterization. This thesis specifically addresses these challenges by advancing technologies for radiological characterization in nuclear decommissioning. The research focuses on developing advanced measurement techniques that can be integrated into robotic platforms with gamma imaging systems to improve efficiency, safety, and accuracy in radiological assessments. The primary objectives of this work are to (1) optimize measurement times, (2) develop a compact gamma imaging system for seamless integration with robotic platforms, (3) implement real-time data processing to enhance operational decisions, and (4) utilize commercially available components to ensure scalability. These goals aim to increase decommissioning efficiency while maintaining high safety and precision. Methodologically, the thesis centers on automated radiological mapping for the ARCHER (Autonomous Robot for Characterization) platform. Calibration techniques were employed to minimize measurement times when using a CdZnTe detector in both low- and high-background environments. Additionally, a lightweight gamma imaging system based on the Timepix3 detector was developed, optimized for rapid, high-resolution gamma-ray spectrometry and 3D hotspot mapping. Key findings include significant reductions in measurement times and improvements in hotspot localization accuracy. The Timepix3 gamma imaging system, designed for the ARCHER platform, enables efficient radiological mapping
in hazardous, hard-to-reach areas. Real-time data processing further enhances operational safety and responsiveness. This thesis contributes to nuclear decommissioning by demonstrating how scalable, adaptable gamma imaging methods—integrated with robotic platforms— can improve overall safety and operational efficiency. Optimizing measurement times and employing off-the-shelf components render these systems costeffective and practical for various decommissioning applications. In conclusion, the research shows that robotic platforms combined with advanced gamma imaging significantly enhance the safety, accuracy, and efficiency of nuclear decommissioning
Development of Advanced Gamma Imaging Systems for robot supported radiological mapping for Nuclear Decommissioning
No full textNuclear decommissioning presents significant challenges due to the hazardous nature of radioactive environments and the need for precise radiological characterization. This thesis specifically addresses these challenges by advancing technologies for radiological characterization in nuclear decommissioning. The research focuses on developing advanced measurement techniques that can be integrated into robotic platforms with gamma imaging systems to improve efficiency, safety, and accuracy in radiological assessments. The primary objectives of this work are to (1) optimize measurement times, (2) develop a compact gamma imaging system for seamless integration with robotic platforms, (3) implement real-time data processing to enhance operational decisions, and (4) utilize commercially available components to ensure scalability. These goals aim to increase decommissioning efficiency while maintaining high safety and precision. Methodologically, the thesis centers on automated radiological mapping for the ARCHER (Autonomous Robot for Characterization) platform. Calibration techniques were employed to minimize measurement times when using a CdZnTe detector in both low- and high-background environments. Additionally, a lightweight gamma imaging system based on the Timepix3 detector was developed, optimized for rapid, high-resolution gamma-ray spectrometry and 3D hotspot mapping. Key findings include significant reductions in measurement times and improvements in hotspot localization accuracy. The Timepix3 gamma imaging system, designed for the ARCHER platform, enables efficient radiological mapping
in hazardous, hard-to-reach areas. Real-time data processing further enhances operational safety and responsiveness. This thesis contributes to nuclear decommissioning by demonstrating how scalable, adaptable gamma imaging methods—integrated with robotic platforms— can improve overall safety and operational efficiency. Optimizing measurement times and employing off-the-shelf components render these systems costeffective and practical for various decommissioning applications. In conclusion, the research shows that robotic platforms combined with advanced gamma imaging significantly enhance the safety, accuracy, and efficiency of nuclear decommissioning
Localization of hotspots via a lightweight system combining Compton imaging with a 3D lidar camera
No full textEfficient and secure decommissioning of nuclear facilities demands advanced technologies. In this context, gamma-ray detection and imaging are crucial in identifying radioactive hotspots and monitoring radiation levels. Our study is dedicated to developing a gamma-ray detection system tailored for integration into robotic platforms for nuclear decommissioning, offering a safe and automated solution for this intricate task and ensuring the safety of human operators by mitigating radiation exposure and streamlining hotspot localization. Our approach integrates a Compton camera based 3D reconstruction algorithm with a single Timepix3 detector. This eliminates the need for a second detector and significantly reduces system weight and cost. Additionally , combining a 3D camera with the setup enhances hotspot visualization and interpretation, rendering it an ideal solution for practical nuclear decommissioning applications. In a proof-of-concept measurement utilizing a 137Cs source, our system accurately localized and visualized the source in 3D with an angular error of 1 • and estimated the activity with a 3% relative error. This promising result underscores the system's potential for deployment in real-world decommissioning settings. Future endeavors will expand the technology's applications in authentic decommissioning scenarios and optimize its integration with robotic platforms. The outcomes of our study contribute to heightened safety and accuracy for nuclear decommissioning works through the advancement of cost-effective and efficient gamma-ray detection systems.This work was also supported by the Research Foundation - Flanders (FWO) scholarship nr 1SA2621N and 1SA2623N hosted by University Hasselt. The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation - Flanders (FWO) and the Flemish Government. This research was performed in the context of the Archer project. The Archer project was carried out by academic research partners UHasselt
and KU Leuven in collaboration with the industrial partners EQUANS and Magics Instruments. This project is funded by the Energy Transition Fund of the FOD economy (federal government Belgium). The publication exclusively contains the opinions of the authors. The General Directorate Energy is not liable for any use of the information in the current paper
A single-layer compton camera for nuclear decommissioning: An improved back-projection algorithm
No full textThe decommissioning of nuclear facilities, a critical and hazardous process due to radiation exposure, necessitates the advancement of radiological measurement techniques concerning safety and efficiency. This study presents an optimized back-projection method, integrating a novel single-layer Compton camera and 3D camera setup with the Timepix3 readout chip to improve the precision and efficiency of gamma-ray source localization in decommissioning scenarios. Implementing 'twin addition' and 'twin multiplication' techniques addressed data ambiguities that arise from event selection in the Compton camera, enhancing the reliability of localization. Introducing a zoom function significantly improved computational efficiency, achieving 163 times faster computation times without sacrificing accuracy. Our method demonstrated enhanced precision with a median angular error of 1.41 • , outperforming traditional methods and showing competitive advantages over state-of-the-art technologies, including the Caliste-HD detector. The feasibility of integrating this methodology onto mobile robotic platforms suggests a promising avenue to minimize human radiation exposure and optimize decom-missioning tasks, ensuring safer and more effective nuclear facility decommissioning
Learning Multiple Radiation SourceDistribution Models using Gaussian Processes
No full textOver the past years, automated, robotic radiation source localisation has become of emerging interest due to a variety of reasons, e.g. disaster response, homeland security, or dismantling and decommissioning of nuclear contaminated areas. Nowadays, to perform in-the-field measurements, radiation protection officers and safety personnel are tasked with characterising an environment before a nuclear contaminated area can enter the final phase of the dismantling and decommissioning process. This involves some severe drawbacks such as the absence of any a priori information on the potentially contaminated area. Besides the potential health risks involved, this preliminary task is very time-consuming and prone to errors concerning the taken measurements and the post-processing of the obtained measurements. To further automate this task, this paper presents an approach to build a radiation model of the environment based on measurements collected by a robotic arm during in-situ laboratory tests. The task of estimating the radiation distribution in an environment is modeled as a regression problem, where the framework of Gaussian Processes is adopted. The experiments conducted in an in-situ laboratory environment demonstrate that the approach is feasible to model the radiation distribution caused by multiple radiation point sources, for both static measurements, where a robot stops moving to sample a measurement, and dynamic measurements, where a robot executes measurements in a continuous manner
Minimal Detection Time for Localization of Radioactive Hot Spots in Low and Elevated Background Environments Using a CZT Gamma-Ray Spectrometer
No full textThis study determines the minimal detection time (MDT) needed for successful localization of radioactive hot spots during nuclear decommissioning work. An automated XY stage, equipped with a CdZnTe (CZT) spectrometer, was used to identify and localize hot spots of Am-241, Cs-137, and Co-60 in a 1.7 x 1.7-m area. The stage served as a preliminary test platform for the development of an automated robotic characterization platform [Autonomous Robotic platform for CHaractERization (ARCHER) robot]. The dependence of the MDT on the detector efficiency and background (BKG) level was examined. For low BKG environments, the MDT for Cs-137 was 871 ms and resulted in an error of the source localization of 14.21 mm and an error of the activity of 6.85%. For elevated BKG levels, the MDT increased to 15 526 ms. The Cs-137 source was localized with an error of 34.13 mm and an error of the source activity of -7.04%. The MDT determination method used here offers a valuable approach for decreasing total scanning times while avoiding missing the presence of hot spots.This research was performed in the context of the ARCHER project, carried out by academic research partners Hasselt University (UHasselt) and KU Leuven in collaboration with the industrial partners EQUANS and Magics Instruments. This project is funded by the Energy Transition Fund of FOD economy (Federal Government Belgium). The publication exclusively contains the opinions of the authors. The General Directorate Energy is not liable regarding any use of the information in the current paper. This work was also supported by the Fund for Scientific Research Flanders [Fonds Wetenschappelijk Onderzoek (FWO)] scholarship nr 1SA2621N hosted by UHasselt and a Bijzonder Onderzoeksfonds (BOF) scholarship nr 60704300460004 of UHasselt. The authors would like to thank Samy Keymis and Levon Soghomonyan for their contributions to the development of the XY platform
Design and Comparison of a Passive and Active Multiple Radiological Point Source Localisation Algorithm
No full textOver the past years, automated, robotic radiation source localisation has become of emerging interest due to a variety of reasons, e.g. disaster response, homeland security, or dismantling and decommissioning of nuclear contaminated areas. Nowadays, to perform in-the-field measurements, radiation protection officers are tasked with characterising an environment before dismantling and decommissioning can take place. This is challenging because of the absence of a priori information on the potentially contaminated area. Besides the health risks involved, this preparatory task is very time-consuming and prone to errors concerning the taken measurements and the post-processing of these measurements. To further automate this key preliminary task, this paper presents two search algorithms to localise multiple radiological point sources in the environment: a passive localisation algorithm where a robotic platform scans a surface using a predefined pattern, and an active source localisation algorithm that chooses the next best position to take a measurement in order to characterise an environment. The developed approaches are first tested in a simulation environment and then validated using in-situ laboratory measurements using a Kromek CZT sensor and a two-dimensional linear guidance system. The experiments show that a correct representation of the environment is contained both for the passive and active localisation approach. Furthermore, the active localisation approach demonstrates that a large reduction in the amount of measurements to char-acterise an environment can be obtained without compromising on the estimation accuracy
Learning Multiple Radiation SourceDistribution Models using Gaussian Processes
No full textOver the past years, automated, robotic radiation source localisation has become of emerging interest due to a variety of reasons, e.g. disaster response, homeland security, or dismantling and decommissioning of nuclear contaminated areas. Nowadays, to perform in-the-field measurements, radiation protection officers and safety personnel are tasked with characterising an environment before a nuclear contaminated area can enter the final phase of the dismantling and decommissioning process. This involves some severe drawbacks such as the absence of any a priori information on the potentially contaminated area. Besides the potential health risks involved, this preliminary task is very time-consuming and prone to errors concerning the taken measurements and the post-processing of the obtained measurements. To further automate this task, this paper presents an approach to build a radiation model of the environment based on measurements collected by a robotic arm during in-situ laboratory tests. The task of estimating the radiation distribution in an environment is modeled as a regression problem, where the framework of Gaussian Processes is adopted. The experiments conducted in an in-situ laboratory environment demonstrate that the approach is feasible to model the radiation distribution caused by multiple radiation point sources, for both static measurements, where a robot stops moving to sample a measurement, and dynamic measurements, where a robot executes measurements in a continuous manner
Design and Comparison of a Passive and Active Multiple Radiological Point Source Localisation Algorithm
No full textOver the past years, automated, robotic radiation source localisation has become of emerging interest due to a variety of reasons, e.g. disaster response, homeland security, or dismantling and decommissioning of nuclear contaminated areas. Nowadays, to perform in-the-field measurements, radiation protection officers are tasked with characterising an environment before dismantling and decommissioning can take place. This is challenging because of the absence of a priori information on the potentially contaminated area. Besides the health risks involved, this preparatory task is very time-consuming and prone to errors concerning the taken measurements and the post-processing of these measurements. To further automate this key preliminary task, this paper presents two search algorithms to localise multiple radiological point sources in the environment: a passive localisation algorithm where a robotic platform scans a surface using a predefined pattern, and an active source localisation algorithm that chooses the next best position to take a measurement in order to characterise an environment. The developed approaches are first tested in a simulation environment and then validated using in-situ laboratory measurements using a Kromek CZT sensor and a two-dimensional linear guidance system. The experiments show that a correct representation of the environment is contained both for the passive and active localisation approach. Furthermore, the active localisation approach demonstrates that a large reduction in the amount of measurements to char-acterise an environment can be obtained without compromising on the estimation accuracy
Advapix TPX3 detector with Realsense L515 Lidar Camera for Localization and Characterization of Hotspots
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