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A High-Performance Gamma Spectrometer for Unmanned Systems Based on Off-the-Shelf Components

Since the Fukushima Daiichi Nuclear Power Plant accident in March 2011, the technology available for unmanned aerial vehicles (UAVs) for radiation monitoring has improved greatly. Remote access to radiation-contaminated areas not only eliminates unnecessary exposure of civilians or military personne...

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Autores principales: Chierici, Andrea, Malizia, Andrea, Di Giovanni, Daniele, Ciolini, Riccardo, d’Errico, Francesco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838996/
https://www.ncbi.nlm.nih.gov/pubmed/35161824
http://dx.doi.org/10.3390/s22031078
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author Chierici, Andrea
Malizia, Andrea
Di Giovanni, Daniele
Ciolini, Riccardo
d’Errico, Francesco
author_facet Chierici, Andrea
Malizia, Andrea
Di Giovanni, Daniele
Ciolini, Riccardo
d’Errico, Francesco
author_sort Chierici, Andrea
collection PubMed
description Since the Fukushima Daiichi Nuclear Power Plant accident in March 2011, the technology available for unmanned aerial vehicles (UAVs) for radiation monitoring has improved greatly. Remote access to radiation-contaminated areas not only eliminates unnecessary exposure of civilians or military personnel, but also allows workers to explore inaccessible places. Hazardous levels of radioactive contamination can be expected as a result of accidents in the nuclear power industry or as a result of the intentional release of radioactive materials for terrorist purposes (dirty bombs, building contamination, etc.). The possibility to detect, identify, and characterize radiation and nuclear material using mobile and remote sensing platforms is a common requirement in the radiation sensing community. The technology has applications in homeland security and law enforcement, customs and border protection, nuclear power plant safety and security, nuclear waste monitoring, environmental recovery, and the military. In this work, the authors have developed, implemented, and characterized a gamma-ray detection and spectroscopy system capable of operating on a UAV. The system was mainly developed using open-source software and affordable hardware components to reduce development and maintenance costs and provide satisfactory performance as a detection instrument. The designed platform can be used to perform mapping or localization tasks to improve the risk assessment process for first responders during the management of radiological and nuclear incidents. First, the design process of the system is described; the result of the characterization of the platform is then presented together with the use of the prototype installed on a UAV in an exercise simulating a radiological and nuclear contamination scenario.
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spelling pubmed-88389962022-02-13 A High-Performance Gamma Spectrometer for Unmanned Systems Based on Off-the-Shelf Components Chierici, Andrea Malizia, Andrea Di Giovanni, Daniele Ciolini, Riccardo d’Errico, Francesco Sensors (Basel) Article Since the Fukushima Daiichi Nuclear Power Plant accident in March 2011, the technology available for unmanned aerial vehicles (UAVs) for radiation monitoring has improved greatly. Remote access to radiation-contaminated areas not only eliminates unnecessary exposure of civilians or military personnel, but also allows workers to explore inaccessible places. Hazardous levels of radioactive contamination can be expected as a result of accidents in the nuclear power industry or as a result of the intentional release of radioactive materials for terrorist purposes (dirty bombs, building contamination, etc.). The possibility to detect, identify, and characterize radiation and nuclear material using mobile and remote sensing platforms is a common requirement in the radiation sensing community. The technology has applications in homeland security and law enforcement, customs and border protection, nuclear power plant safety and security, nuclear waste monitoring, environmental recovery, and the military. In this work, the authors have developed, implemented, and characterized a gamma-ray detection and spectroscopy system capable of operating on a UAV. The system was mainly developed using open-source software and affordable hardware components to reduce development and maintenance costs and provide satisfactory performance as a detection instrument. The designed platform can be used to perform mapping or localization tasks to improve the risk assessment process for first responders during the management of radiological and nuclear incidents. First, the design process of the system is described; the result of the characterization of the platform is then presented together with the use of the prototype installed on a UAV in an exercise simulating a radiological and nuclear contamination scenario. MDPI 2022-01-29 /pmc/articles/PMC8838996/ /pubmed/35161824 http://dx.doi.org/10.3390/s22031078 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chierici, Andrea
Malizia, Andrea
Di Giovanni, Daniele
Ciolini, Riccardo
d’Errico, Francesco
A High-Performance Gamma Spectrometer for Unmanned Systems Based on Off-the-Shelf Components
title A High-Performance Gamma Spectrometer for Unmanned Systems Based on Off-the-Shelf Components
title_full A High-Performance Gamma Spectrometer for Unmanned Systems Based on Off-the-Shelf Components
title_fullStr A High-Performance Gamma Spectrometer for Unmanned Systems Based on Off-the-Shelf Components
title_full_unstemmed A High-Performance Gamma Spectrometer for Unmanned Systems Based on Off-the-Shelf Components
title_short A High-Performance Gamma Spectrometer for Unmanned Systems Based on Off-the-Shelf Components
title_sort high-performance gamma spectrometer for unmanned systems based on off-the-shelf components
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838996/
https://www.ncbi.nlm.nih.gov/pubmed/35161824
http://dx.doi.org/10.3390/s22031078
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