1,720,974 research outputs found
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
Human-robot mobile co-manipulation of flexible objects by fusing wrench and skeleton tracking data
No full textstatus: Publishe
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
Towards a Semi-Autonomous Robot Platform for the Characterisation of Radiological Environments
No full textsponsorship: The authors would like to acknowledge the financial support of the Belgian Federal Public Service (FOD) Economy, Energy transition fund towards the ARCHER project. The authors would also like to thank Vlaams Agentschap Innoveren & Ondernemen (VLAIO) for granting Ivo Dekker's Baekeland mandate HBC.2020.2884, also facilitating this research. (Belgian Federal Public Service (FOD) Economy, Energy transition fund towards the ARCHER project, Vlaams Agentschap Innoveren & Ondernemen (VLAIO)|HBC.2020.2884)status: Publishe
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
No full textCompton imaging to support a new robotic platform for mapping nuclear decommissioning sites
No full textThe localisation, identification and remediation of hotspots in a nuclear decommissioning site is an important step in the decommissioning process. This ensures that operators, carrying out future decommissioning activities, will no longer be exposed to unneeded dose-uptake.
The current practice for identifying hotspots is to use human operators for radiological measurements, but due to the high dose rate of the hotspots, special measures are necessary to protect workers and the available time to perform mapping and characterisation steps is limited. This not only introduces the risk of missing sources or performing inaccurate measurements and other specific ALARA related challenges, but it is a time consuming and inefficient way of mapping.
Robots can be employed to automate this repetitive work of mapping and characterising to reduce the exposure of workers and increase the accuracy of the measurement. Within the current ‘energy transition funds’ project ARCHER (Autonomous Robotic platform for CHaractERisation), a robot platform was developed that aims at minimising the need for human intervention. This platform contains a lightweight CZT gamma spectrometer or a Compton camera for radiological mapping. ARCHER is executed by academic research partners (UHasselt and KU Leuven) in collaboration with industrial partners (Tecnubel and Magics) and financial support of the Belgian FOD Economy.
Using a Compton camera reduces the need for excessive manoeuvres of the platform and, as measurements are performed relatively far away from the source, the chance of contaminating the platform is limited. Complementary, the CZT Spectrometer is also used for scanning the contaminated surfaces and identification of the hotspots.
Lab-scale tests have been performed with a mapping routine. The CZT scanning approach was optimised for distance, measurement time and mapping performance. Cs-137 and Co-60 sources were used to simulate hotspots. Results show that sources could be localised with an accuracy of up to 5 mm. The Compton camera was subjected to the same tests and also showed that the Cs-137 sources could be localised.
The provided presentation demonstrates initial results regarding the use of the ARCHER platform in a lab-scale environment. The use of the Compton camera has been found an added value to the currently used method in ARCHER. Future research will aim at further development of the used gamma imaging approach to detect a wider energy range. The robot platform itself will also go into the next development phase where in situ acceptance testing will be performed at an installation currently in decommissioning
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
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