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Learning to detect proximity to a gas source with a mobile robot
University of Tübingen, Tübingen, Germany. (Learning Systems Lab)ORCID iD: 0000-0003-0217-9326
University of Tübingen, Tübingen, Germany. (WSI)
University of Tübingen, Tübingen, Germany. (WSI)
University of Tübingen, Tübingen, Germany. (WSI)
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2004 (English)In: 2004 IEEE/RSJ international conference on intelligent robots and systems, 2004 (IROS 2004), Institute of Electrical and Electronics Engineers (IEEE), 2004, Vol. 4, p. 1444-1449Conference paper, Published paper (Refereed)
Abstract [en]

As a sub-task of the general gas source localisation problem, gas source declaration is the process of determining the certainty that a source is in the immediate vicinity. Due to the turbulent character of gas transport in a natural indoor environment, it is not sufficient to search for instantaneous concentration maxima, in order to solve this task. Therefore, this paper introduces a method to classify whether an object is a gas source from a series of concentration measurements, recorded while the robot performs a rotation manoeuvre in front of a possible source. For three different gas source positions, a total of 1056 declaration experiments were carried out at different robot-to-source distances. Based on these readings, support vector machines (SVM) with optimised learning parameters were trained and the cross-validation classification performance was evaluated. The results demonstrate the feasibility of the approach to detect proximity to a gas source using only gas sensors. The paper presents also an analysis of the classification rate depending on the desired declaration accuracy, and a comparison with the classification rate that can be achieved by selecting an optimal threshold value regarding the mean sensor signal.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2004. Vol. 4, p. 1444-1449
National Category
Computer Sciences
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-3999DOI: 10.1109/IROS.2004.1389599Scopus ID: 2-s2.0-14044251563ISBN: 0-7803-8463-6 (print)OAI: oai:DiVA.org:oru-3999DiVA, id: diva2:138298
Conference
2004 IEEE/RSJ international conference on intelligent robots and systems, 2004 (IROS 2004), Sendai, Japan, 28 September-2 October, 2004
Available from: 2007-09-03 Created: 2007-09-03 Last updated: 2018-04-26Bibliographically approved

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Learning to Detect Proximity to a Gas Source with a Mobile Robot(540 kB)65 downloads
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Lilienthal, Achim J.

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