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Towards occupational health improvement in foundries through dense dust and pollution monitoring using a complementary approach with mobile and stationary sensing nodes
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS MRO Lab)ORCID iD: 0000-0001-5061-5474
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS MRO Lab)ORCID iD: 0000-0002-0804-8637
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS MRO Lab)ORCID iD: 0000-0003-0217-9326
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS MRO Lab)ORCID iD: 0000-0003-1662-0960
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2016 (English)In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2016, 131-136 p., 7759045Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016. 131-136 p., 7759045
Keyword [en]
Occupational Health; Mobile Robot Olfaction; Pollution Monitoring
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-55230DOI: 10.1109/IROS.2016.7759045ISI: 000391921700019Scopus ID: 2-s2.0-85006511897ISBN: 9781509037629 (print)OAI: oai:DiVA.org:oru-55230DiVA: diva2:1070802
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016), Daejeong, Korea, October 9-14, 2016
Projects
RAISE
Funder
Knowledge Foundation, 20130196
Available from: 2017-02-02 Created: 2017-02-02 Last updated: 2017-03-07Bibliographically approved

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Hernandez Bennetts, VictorSchaffernicht, ErikLilienthal, Achim J.Fan, HanKucner, Tomasz Piotr
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School of Science and Technology, Örebro University, Sweden
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