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Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion
This article presents the research and results of field tests and simulations regarding an autonomous/robotic railway vehicle, designed to collect multiple information on safety and functional parameters of a surface railway and/or subway section, based on data fusion and machine learning. The maint...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8538696/ https://www.ncbi.nlm.nih.gov/pubmed/34696089 http://dx.doi.org/10.3390/s21206876 |
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author | Minea, Marius Dumitrescu, Cătălin Marian Dima, Mihai |
author_facet | Minea, Marius Dumitrescu, Cătălin Marian Dima, Mihai |
author_sort | Minea, Marius |
collection | PubMed |
description | This article presents the research and results of field tests and simulations regarding an autonomous/robotic railway vehicle, designed to collect multiple information on safety and functional parameters of a surface railway and/or subway section, based on data fusion and machine learning. The maintenance of complex railways, or subway networks with long operating times is a difficult process and intensive resources consuming. The proposed solution delivers human operators in the fault management service and operations from the time-consuming task of railway inspection and measurements, by integrating several sensors and collecting most relevant information on railway, associated automation equipment and infrastructure on a single intelligent platform. The robotic cart integrates autonomy, remote sensing, artificial intelligence, and ability to detect even infrastructural anomalies. Moreover, via a future process of complex statistical filtering of data, it is foreseen that the solution might be configured to offer second-order information about infrastructure changes, such as land sliding, water flooding, or similar modifications. Results of simulations and field tests show the ability of the platform to integrate several fault management operations in a single process, useful in increasing railway capacity and resilience. |
format | Online Article Text |
id | pubmed-8538696 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85386962021-10-24 Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion Minea, Marius Dumitrescu, Cătălin Marian Dima, Mihai Sensors (Basel) Article This article presents the research and results of field tests and simulations regarding an autonomous/robotic railway vehicle, designed to collect multiple information on safety and functional parameters of a surface railway and/or subway section, based on data fusion and machine learning. The maintenance of complex railways, or subway networks with long operating times is a difficult process and intensive resources consuming. The proposed solution delivers human operators in the fault management service and operations from the time-consuming task of railway inspection and measurements, by integrating several sensors and collecting most relevant information on railway, associated automation equipment and infrastructure on a single intelligent platform. The robotic cart integrates autonomy, remote sensing, artificial intelligence, and ability to detect even infrastructural anomalies. Moreover, via a future process of complex statistical filtering of data, it is foreseen that the solution might be configured to offer second-order information about infrastructure changes, such as land sliding, water flooding, or similar modifications. Results of simulations and field tests show the ability of the platform to integrate several fault management operations in a single process, useful in increasing railway capacity and resilience. MDPI 2021-10-16 /pmc/articles/PMC8538696/ /pubmed/34696089 http://dx.doi.org/10.3390/s21206876 Text en © 2021 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 Minea, Marius Dumitrescu, Cătălin Marian Dima, Mihai Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion |
title | Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion |
title_full | Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion |
title_fullStr | Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion |
title_full_unstemmed | Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion |
title_short | Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion |
title_sort | robotic railway multi-sensing and profiling unit based on artificial intelligence and data fusion |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8538696/ https://www.ncbi.nlm.nih.gov/pubmed/34696089 http://dx.doi.org/10.3390/s21206876 |
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