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Time-variant gas distribution mapping with obstacle information

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Abstract

This paper addresses the problem of estimating the spatial distribution of volatile substances using a mobile robot equipped with an electronic nose. Our work contributes an effective solution to two important problems that have been disregarded so far: First, obstacles in the environment (walls, furniture,...) do affect the gas spatial distribution. Second, when combining odor measurements taken at different instants of time, their ‘ages’ must be taken into account to model the ephemeral nature of gas distributions. In order to incorporate these two characteristics into the mapping process we propose modeling the spatial distribution of gases as a Gaussian Markov random field. This mathematical framework allows us to consider both: (i) the vanishing information of gas readings by means of a time-increasing uncertainty in sensor measurements, and (ii) the influence of objects in the environment by means of correlations among the different areas. Experimental validation is provided with both, simulated and real-world datasets, demonstrating the out-performance of our method when compared to previous standard techniques in gas mapping.

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Notes

  1. An exception to this is the tunable laser absorption spectroscopy (TDLAS) technology (Trincavelli et al. 2012; Frish et al. 2005), which provides integral gas concentration measurements over the path of a laser beam. However, this is still an emerging technology with important drawbacks for robotic applications, including cost, weight and power consumption.

  2. Model ppbRAE2000 from RAESystem, with a 10.6 eV UV lamp.

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Acknowledgments

The authors would like to thank Achim J. Lilienthal and Sahar Asadi for the fruitful discussions about kernel methods for gas distribution mapping. This work was supported by the Andalucía Regional Government and the European Union (FEDER) [TEP08-4016]; and by the Spanish “Ministerio de Ciencia e Innovación” and the grant program JDC-MICINN 2011 [DPI2011-25483].

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Authors and Affiliations

  1. Department of System Engineering and Automation, University of Málaga, Málaga, Spain

    Javier G. Monroy & Javier Gonzalez-Jimenez

  2. Department of Engineering, University of Almería, Almería, Spain

    Jose-Luis Blanco

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  1. Javier G. Monroy
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  2. Jose-Luis Blanco
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  3. Javier Gonzalez-Jimenez
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Correspondence to Javier G. Monroy.

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G. Monroy, J., Blanco, JL. & Gonzalez-Jimenez, J. Time-variant gas distribution mapping with obstacle information. Auton Robot 40, 1–16 (2016). https://doi.org/10.1007/s10514-015-9437-0

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