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Learning to detect ...
Learning to detect proximity to a gas source with a mobile robot
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- Lilienthal, Achim J., 1970- (author)
- University of Tübingen, Tübingen, Germany,Learning Systems Lab
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- Ulmer, Holger (author)
- University of Tübingen, Tübingen, Germany,WSI
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- Fröhlich, Holger (author)
- University of Tübingen, Tübingen, Germany,WSI
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- Werner, Felix (author)
- University of Tübingen, Tübingen, Germany,WSI
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- Zell, Andreas (author)
- University of Tübingen, Tübingen, Germany,WSI
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2004
- 2004
- English.
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In: 2004 IEEE/RSJ international conference on intelligent robots and systems, 2004 (IROS 2004). - : Institute of Electrical and Electronics Engineers (IEEE). - 0780384636 ; , s. 1444-1449
- Related links:
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https://oru.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Datalogi
- Computer and Systems Science
Publication and Content Type
- ref (subject category)
- kon (subject category)
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