Towards occupational health improvement in foundries through dense dust and pollution monitoring using a complementary approach with mobile and stationary sensing nodes

In industrial environments, such as metallurgic facilities, human operators are exposed to harsh conditions where ambient air is often polluted with quartz, dust, lead debris and toxic fumes. Constant exposure to respirable particles can cause irreversible health damages and thus it is of high inter...

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Vydané v:Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems s. 131 - 136
Hlavní autori: Hernandez Bennetts, Victor, Schaffernicht, Erik, Lilienthal, Achim J., Han Fan, Kucner, Tomasz Piotr, Andersson, Lena, Johansson, Anders
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.10.2016
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ISSN:2153-0866
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Abstract In industrial environments, such as metallurgic facilities, human operators are exposed to harsh conditions where ambient air is often polluted with quartz, dust, lead debris and toxic fumes. Constant exposure to respirable particles can cause irreversible health damages and thus it is of high interest for occupational health experts to monitor the air quality on a regular basis. However, current monitoring procedures are carried out sparsely, with data collected in single day campaigns limited to few measurement locations. In this paper we explore the use and present first experimental results of a novel heterogeneous approach that uses a mobile robot and a network of low cost sensing nodes. The proposed system aims to address the spatial and temporal limitations of current monitoring techniques. The mobile robot, along with standard localization and mapping algorithms, allows to produce short term, spatially dense representations of the environment where dust, gas, ambient temperature and airflow information can be modelled. The sensing nodes on the other hand, can collect temporally dense (and usually spatially sparse) information during long periods of time, allowing in this way to register for example, daily variations in the pollution levels. Using data collected with the proposed system in an steel foundry, we show that a heterogeneous approach provides dense spatio-temporal information that can be used to improve the working conditions in industrial facilities.
AbstractList In industrial environments, such as metallurgic facilities, human operators are exposed to harsh conditions where ambient air is often polluted with quartz, dust, lead debris and toxic fumes. Constant exposure to respirable particles can cause irreversible health damages and thus it is of high interest for occupational health experts to monitor the air quality on a regular basis. However, current monitoring procedures are carried out sparsely, with data collected in single day campaigns limited to few measurement locations. In this paper we explore the use and present first experimental results of a novel heterogeneous approach that uses a mobile robot and a network of low cost sensing nodes. The proposed system aims to address the spatial and temporal limitations of current monitoring techniques. The mobile robot, along with standard localization and mapping algorithms, allows to produce short term, spatially dense representations of the environment where dust, gas, ambient temperature and airflow information can be modelled. The sensing nodes on the other hand, can collect temporally dense (and usually spatially sparse) information during long periods of time, allowing in this way to register for example, daily variations in the pollution levels. Using data collected with the proposed system in an steel foundry, we show that a heterogeneous approach provides dense spatio-temporal information that can be used to improve the working conditions in industrial facilities.
Author Lilienthal, Achim J.
Hernandez Bennetts, Victor
Andersson, Lena
Schaffernicht, Erik
Kucner, Tomasz Piotr
Han Fan
Johansson, Anders
Author_xml – sequence: 1
  givenname: Victor
  surname: Hernandez Bennetts
  fullname: Hernandez Bennetts, Victor
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  organization: AASS Res. Centre, Orebro Univ., Orebro, Sweden
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  givenname: Erik
  surname: Schaffernicht
  fullname: Schaffernicht, Erik
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  givenname: Achim J.
  surname: Lilienthal
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  organization: AASS Res. Centre, Orebro Univ., Orebro, Sweden
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  surname: Han Fan
  fullname: Han Fan
  email: han.fan@oru.se
  organization: AASS Res. Centre, Orebro Univ., Orebro, Sweden
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  givenname: Tomasz Piotr
  surname: Kucner
  fullname: Kucner, Tomasz Piotr
  email: tomasz.piotr.kucner@oru.se
  organization: AASS Res. Centre, Orebro Univ., Orebro, Sweden
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  givenname: Lena
  surname: Andersson
  fullname: Andersson, Lena
  organization: Dept. of Occupational & Environ. Med., Orebro Univ. Hosp., Orebro, Sweden
– sequence: 7
  givenname: Anders
  surname: Johansson
  fullname: Johansson, Anders
  organization: Dept. of Occupational & Environ. Med., Orebro Univ. Hosp., Orebro, Sweden
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Snippet In industrial environments, such as metallurgic facilities, human operators are exposed to harsh conditions where ambient air is often polluted with quartz,...
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SubjectTerms Atmospheric measurements
Mobile robots
Monitoring
Pollution measurement
Robot sensing systems
Title Towards occupational health improvement in foundries through dense dust and pollution monitoring using a complementary approach with mobile and stationary sensing nodes
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