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Sökning: db:Swepub > Örebro universitet > Lilienthal Achim J. > Fröhlich Holger

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1.
  • Lilienthal, Achim J., et al. (författare)
  • Gas source declaration with a mobile robot
  • 2004
  • Ingår i: 2004 IEEE international conference on robotics and automation, ICRA '04 : Proceedings. - 0-7803-8232-3 ; s. 1430-1435
  • Konferensbidrag (refereegranskat)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 or not 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 288 declaration experiments were carried out at different robot-to-source distances. Based on these readings, two machine learning techniques (ANN, SVM) were evaluated in terms of their classification performance. With learning parameters that were optimised by grid search, a maximal hit rate of approximately 87.5% could be obtained using a support vector machine
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2.
  • Lilienthal, Achim J., et al. (författare)
  • Learning to detect proximity to a gas source with a mobile robot
  • 2004
  • Ingår i: Proceedings of the 2004 IEEE/RSJ international conference on intelligent robots and systems, 2004 (IROS 2004) : IROS - Sendai, Japan, 2004. - 0-7803-8463-6 ; s. 1444-1449
  • Konferensbidrag (refereegranskat)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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  • Resultat 1-2 av 2
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konferensbidrag (2)
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refereegranskat (2)
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Zell, Andreas, (2)
Ulmer, Holger, (2)
Werner, Felix, (2)
Stützle, Andreas, (1)
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Engelska (2)
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Naturvetenskap (2)
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