| 1. |
- Lilienthal, Achim J., 1970-, et al.
(författare)
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Gas source declaration with a mobile robot
- 2004
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Ingår i: 2004 IEEE International Conference on Robotics and Automation. - New York, USA : IEEE. - 0780382323 ; , s. 1430-1435
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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., 1970-, et al.
(författare)
-
Learning to detect proximity to a gas source with a mobile robot
- 2004
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Ingår i: 2004 IEEE/RSJ international conference on intelligent robots and systems, 2004 (IROS 2004). - : Institute of Electrical and Electronics Engineers (IEEE). - 0780384636 ; , s. 1444-1449
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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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