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Autonomous Robots

, Volume 40, Issue 1, pp 1–16 | Cite as

Time-variant gas distribution mapping with obstacle information

  • Javier G. Monroy
  • Jose-Luis Blanco
  • Javier Gonzalez-Jimenez
Article

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.

Keywords

Mobile robotics Gas distribution mapping Robotics olfaction Gaussian Markov random field 

Notes

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].

References

  1. Airsense Analytics. (2014). The portable electronic nose-PEN3. http://www.airsense.com/en/products/portable-electronic-nose.
  2. Asadi, S., Pashami, S., Loutfi, A., & Lilienthal, A. J. (2011). TD Kernel DM+V: Time-dependent statistical gas distribution modelling on simulated measurements. In Proceedings of the 14th International Symposium on Olfaction and Electronic Nose (ISOEN), (Vol. 1362, pp. 281–283).Google Scholar
  3. Bishop, C. M. (2007). Pattern recognition and machine learning. Berlin: Springer.Google Scholar
  4. Bjorck, A. (1996). Numerical methods for least squares problems. Philadelphia: Society for Industrial and Applied Mathematics.CrossRefGoogle Scholar
  5. Blanco, J. L., Gonzalez-Jimenez, J., & Fernandez-Madrigal, J. A. (2010). Optimal filtering for non-parametric observation models: Applications to localization and SLAM. The International Journal of Robotics Research, 29(14), 1726–1742. doi: 10.1177/0278364910364165.CrossRefGoogle Scholar
  6. Blanco, J. L., G. Monroy, J., Gonzalez-Jimenez, J., & Lilienthal, A. J. (2013). A kalman filter based approach to probabilistic gas distribution mapping. In 28th Symposium On Applied Computing (SAC) (pp. 217–222). doi: 10.1145/2480362.2480409.
  7. Clifford, P. (1990). Markov random fields in statistics. In G. Grimmett & D. Welsh (Eds.), Disorder in physical systems: A volume in honour of John M. Hammersley (pp. 19–32). Oxford University Press.Google Scholar
  8. Davis, T. A. (2004). A column pre-ordering strategy for the unsymmetric-pattern multifrontal method. ACM Transactions on Mathematical Software, 30(2), 165–195. doi: 10.1145/992200.992205.CrossRefzbMATHGoogle Scholar
  9. Dellaert, F., & Kaess, M. (2006). Square root SAM: Simultaneous localization and mapping via square root information smoothing. The International Journal of Robotics Research, 25(12), 1181–1203.CrossRefzbMATHGoogle Scholar
  10. Dennis, J. E., & Schnabel, R. B. (1996). Numerical methods for unconstrained optimization and nonlinear equations. Society for Industrial and Applied Mathematics. doi: 10.1137/1.9781611971200.
  11. Fenger, J. (1999). Urban air quality—their physical and chemical characteristics. Atmospheric Environment, 33(29), 4877–4900. doi: 10.1016/S1352-2310(99)00290-3.CrossRefGoogle Scholar
  12. Fernandez-Madrigal, J. A., & Blanco, J. L. (2013). Simultaneous localization and mapping for mobile robots: Introduction and methods. Hershey: Information Science Reference.CrossRefGoogle Scholar
  13. Frish, M.B., Wainner, R. T., Green, B. D., Laderer, M. C., & Allen, M. G. (2005). Standoff gas leak detectors based on tunable diode laser absorption spectroscopy. In Proceedings of SPIE 6010, Infrared to Terahertz Technologies for Health and the Environment. doi: 10.1117/12.630599.
  14. G. Monroy, J., Gonzalez-Jimenez, J., & Blanco, J. L. (2012). Overcoming the slow recovery of MOX gas sensors through a system modeling approach. Sensors, 12(10), 13664–13680. doi: 10.3390/s121013664.
  15. G. Monroy, J., Blanco, J. L., & González-Jiménez, J. (2013). An open source framework for simulating mobile robotics olfaction. In Proceedings of the 15th International Symposium On Olfaction and Electronic Nose (ISOEN).Google Scholar
  16. Golub, G. H., & Plemmons, R. J. (1980). Large-scale geodetic least-squares adjustment by dissection and orthogonal decomposition. Linear Algebra and its Applications, 34, 3–28. doi: 10.1016/0024-3795(80)90156-1.MathSciNetCrossRefzbMATHGoogle Scholar
  17. Gonzalez-Jimenez, J., G. Monroy, J., & Blanco, J. L. (2011). The multi-chamber electronic nose. An improved olfaction sensor for mobile robotics. Sensors, 11(6), 6145–6164. doi: 10.3390/s110606145.
  18. Ishida, H., Ushiku, T., Toyama, S., Taniguchi, H., & Moriizumi, T. (2005). Mobile robot path planning using vision and olfaction to search for a gas source. In IEEE Sensors. doi: 10.1109/ICSENS.2005.1597899.
  19. Lilienthal, A. J., & Duckett, T. (2004). Building gas concentration gridmaps with a mobile robot. Robotics and Autonomous Systems, 48(1), 3–16.CrossRefGoogle Scholar
  20. Lilienthal, A. J., Loutfi, A., Blanco, J. L., Galindo, C., & Gonzalez-Jimenez, J. (2007). A rao-blackwellisation approach to GDM-SLAM: Integrating SLAM and gas distribution mapping (GDM). In 3rd European Conference on Mobile Robots (ECMR).Google Scholar
  21. Lilienthal, A. J., Reggente, M., Trincavelli, M., Blanco, J. L., & Gonzalez-Jimenez, J. (2009). A statistical approach to gas distribution modelling with mobile robots—the kernel DM+V algorithm. In IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS. (pp. 570–576). doi: 10.1109/IROS.2009.5354304.
  22. Loeliger, H. A. (2004). An introduction to factor graphs. IEEE Signal Processing Magazine, 21(1), 28–41. doi: 10.1109/MSP.2004.1267047.CrossRefGoogle Scholar
  23. Loufti, A., Coradeschi, S., Lilienthal, A. J., & Gonzalez-Jimenez, J. (2009). Gas distribution mapping of multiple odour sources using a mobile robot. Robotica, 27, 311–319. doi: 10.1017/S0263574708004694.CrossRefGoogle Scholar
  24. Madsen, K., Nielsen, H. B., & Tingleff, O. (2004). Methods for non-linear least squares problems (2nd ed.).Google Scholar
  25. Marjovi, A., & Marques, L. (2013). Optimal spatial formation of swarm robotic gas sensors in odor plume finding. Autonomous Robots, 35(2–3), 93–109. doi: 10.1007/s10514-013-9336-1.
  26. MobileRobots Inc. (2014). Corporate website. http://www.mobilerobots.com
  27. Pashami, S., Asadi, S., & Lilienthal, A. J. (2010). Integration of openfoam flow simulation and filament-based gas propagation models for gas dispersion simulation. In Proceedings of the Open Source CFD International Conference.Google Scholar
  28. Reggente, M., & Lilienthal, A. J. (2009). Three-dimensional statistical gas distribution mapping in an uncontrolled indoor environment. In Proceedings of the 13th International Symposium on Olfaction and Electronic Nose (ISOEN) (Vol. 1137, pp. 109–112).Google Scholar
  29. Sanchez-Garrido, C., Monroy, J. G., Gonzalez-Jimenez J. (2014). A configurable smart e-nose for spatio-temporal olfactory analysis. IEEE Sensors (pp. 1968–1971). Spain:Valencia. doi: 10.1109/ICSENS.2014.6985418.
  30. Sensigent Intelligent Sensing Solutions. (2014). Cyranose 320. http://www.sensigent.com/products/cyranose.html
  31. Shraiman, B. I., & Siggia, E. D. (2000). Scalar turbulence. Nature, 405, 639–646. doi: 10.1038/35015000.CrossRefGoogle Scholar
  32. Stachniss, C., Plagemann, C., & Lilienthal, A. J. (2009). Gas distribution modeling using sparse gaussian process mixtures. Autonomous Robots, 26(2–3), 187–202.CrossRefGoogle Scholar
  33. Tauseef, S., Rashtchian, D., & Abbasi, S. (2011). CFD-based simulation of dense gas dispersion in presence of obstacles. Journal of Loss Prevention in the Process Industries, 24(4), 371–376. doi: 10.1016/j.jlp.2011.01.014.CrossRefGoogle Scholar
  34. Trincavelli, M., Hernandez Bennetts, V., & Lilienthal, A. J. (2012). A least squares approach for learning gas distribution maps from a set of integral gas concentration measurements obtained with a TDLAS sensor. In IEEE Sensors (pp 1–4). doi: 10.1109/ICSENS.2012.6411118.
  35. Tsujita, W., Yoshino, A., Ishida, H., & Moriizumi, T. (2005). Gas sensor network for air-pollution monitoring. Sensors and Actuators B: Chemical, 110(2), 304–311. doi: 10.1016/j.snb.2005.02.008.CrossRefGoogle Scholar
  36. Turduev, M., Cabrita, G., Kırtay, M., Gazi, V., & Marques, L. (2014). Experimental studies on chemical concentration map building by a multi-robot system using bio-inspired algorithms. Autonomous Agents and Multi-Agent Systems, 28(1), 72–100. doi: 10.1007/s10458-012-9213-x.CrossRefGoogle Scholar
  37. Winkler, G. (2003). Image analysis, random fields and Markov chain Monte Carlo methods: A mathematical introduction (Vol. 27). Berlin: Springer.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Javier G. Monroy
    • 1
  • Jose-Luis Blanco
    • 2
  • Javier Gonzalez-Jimenez
    • 1
  1. 1.Department of System Engineering and AutomationUniversity of MálagaMálagaSpain
  2. 2.Department of EngineeringUniversity of AlmeríaAlmeríaSpain

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