Achim J. Lilienthal, Holger Ulmer, Holger Fröhlich, Felix Werner and Andreas Zell
Learning to Detect Proximity to a Gas Source with a Mobile Robot
Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2004, pp. 1444-1449
Abstract: 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.
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@INPROCEEDINGS{Lilienthal_etal:IROS:2004,
  AUTHOR = {Lilienthal, Achim~J. and Ulmer, Holger and Fr{\"o}hlich, Holger and Werner, Felix and Zell, Andreas},
  TITLE = {Learning to Detect Proximity to a Gas Source with a Mobile Robot},
  BOOKTITLE = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  YEAR = {2004},
  MONTH = {September 28 -- October 2},
  ADRESS = {Sendai, Japan},
  PAGES = {1444--1449}
}