Skip to main content
SpringerLink
Log in

Multi-Scale Adaptive Sampling with Mobile Agents for Mapping of Forest Fires

  • Published:
Journal of Intelligent and Robotic Systems Aims and scope Submit manuscript

Abstract

The use of robotics in distributed monitoring applications requires wireless sensors that are deployed efficiently. A very important aspect of sensor deployment includes positioning them for sampling at locations most likely to yield information about the spatio-temporal field of interest, for instance, the spread of a forest fire. In this paper, we use mobile robots (agents) that estimate the time-varying spread of wildfires using a distributed multi-scale adaptive sampling strategy. The proposed parametric sampling algorithm, “EKF-NN-GAS” is based on neural networks, the extended Kalman filter (EKF), and greedy heuristics. It combines measurements arriving at different times, taken at different scale lengths, such as from ground, airborne, and spaceborne observation platforms. One of the advantages of our algorithm is the ability to incorporate robot localization uncertainty in addition to sensor measurement and field parameter uncertainty into the same EKF model. We employ potential fields, generated naturally from the estimated fire field distribution, in order to generate fire-safe trajectories that could be used to rescue vehicles and personnel. The covariance of the EKF is used as a quantitative information measure for sampling locations most likely to yield optimal information about the sampled field distribution. Neural net training is used infrequently to generate initial low resolution estimates of the fire spread parameters. We present simulation and experimental results for reconstructing complex spatio-temporal forest fire fields “truth models”, approximated by radial basis function (RBF) parameterizations. When compared to a conventional raster scan approach, our algorithm shows a significant reduction in the time necessary to map the fire field.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Anderson, B., Moore, J.: Optimal Filtering. Prentice-Hall, Englewood Cliffs, NJ (1979)

    MATH  Google Scholar 

  2. Anderson, D.H., Catchpole, E.A., DeMestre, N.J., Parkes, T.: Modeling the spread of grass fires. J. Aust. Math. Soc. (Ser. B.) 23, 451–466 (1982)

    Article  MATH  Google Scholar 

  3. Cannell, C.J., Stilwell, D.J.: A comparison of two approaches for adaptive sampling of environmental processes using autonomous underwater vehicles. Oceans 2, 1514–1521 (2005)

    Google Scholar 

  4. Christopoulos, V.N., Roumeliotis, S.: Adaptive sensing for instantaneous gas release parameter estimation. International Conference on Robotics and Automation, 2005. ICRA 2005. In: Proceedings of the 2005 IEEE, pp. 4450–4456. (2005) 18–22 Apr

  5. Chuvieco, E. (ed.): Wildland Fire Danger Estimation and Mapping: The Role of Remote Sensing Data. World Scientific Publishing Company, New Jersey, USA (2004)

    Google Scholar 

  6. Creed, E.L., Glenn, S.M., Chant, R.: Adaptive sampling experiment at LEO-15. OCC 1998 Proceedings, Marine Technology Section. pp. 576–579 (1998) Nov

  7. Elfes, A.: Using occupancy grids for mobile robot perception and navigation. Computer. 22(6), 46–57 (1989)

    Article  Google Scholar 

  8. Farrell, J.A., Shuo, P., Wei, L.: Chemical plume tracing via an autonomous underwater vehicle. IEEE J. Oceanic. Eng. 30(2), 428–442 (2005)

    Article  Google Scholar 

  9. Finney, M.A.: FARSITE: fire area simulator-model development and evaluation. Res. Pap. RMRS-RP-4. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Ogden, UT, p. 47 (1998)

  10. Fox, D., Burgard, W., Kruppa, H., Thrun, S.: A probabilistic approach to collaborative multi-robot localization. Auton Robots. 8(3), 325–344 (2000), Special issue on Heterogeneous Multi-Robot Systems

    Article  Google Scholar 

  11. Ge, S.S., Cui, Y.J.: Dynamic motion planning for mobile robots using potential field method. Int. J. Auton. Robots. 13, 207–222 (2002)

    Article  MATH  Google Scholar 

  12. Green, D.G.: Shapes of simulated fires in discrete fuels. Ecol. Mod. 20, 21–32 (1983)

    Article  Google Scholar 

  13. Green, D.G., Gill, A.M., Noble, I.R.: Fire shapes and the adequacy of fire-spread models. Ecol. Mod. 20, 33–45 (1983)

    Article  Google Scholar 

  14. Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd. Prentice Hall, USA (1998)

    Google Scholar 

  15. Hombal, V., Sanderson, A., Blidberg, R.: A non-parametric iterative algorithm for adaptive sampling and robotic vehicle path planning. International Conference on Intelligent Robots and Systems, 2006 IEEE/RSJ, pp. 217–222. (2006) Oct

  16. Howard, A., Mataric, M.J., Sukhatme, G.S.: Mobile sensor network deployment using potential fields: a distributed, scalable solution to the area coverage problem. In: Proc. of 6th International Symposium on Distributed Autonomous Robotic Systems. Fukuoka, Japan, pp. 299–308 (2002)

  17. Jatmiko, W., Sekiyama, K., Fukuda, T.: A mobile robots PSO-based for odor source localization in dynamic advection-diffusion environment. In: International Conference on Intelligent Robots and Systems, 2006, IEEE/RSJ. pp. 4527–4532 (2006) Oct

  18. Johnson, E.A., Miyanishi, K. (ed.): Forest Fires-Behavior and Ecological Effects. Academic, San Diego (2001)

    Google Scholar 

  19. Karafyllidis, I., Thanailakis, A.: A model for predicting forest fire spreading using cellular automata. Ecol. Mod. 99, 87–97 (1997)

    Article  Google Scholar 

  20. Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: Proc. IEEE, International Conference on Robotics and Automation, pp. 500–505. (1985) 25–28 Mar

  21. Krogh, B.H.: A generalized potential field approach to obstacle avoidance control. In: Proc. of International Robotics Research Conference. pp. 1150–1156 (1984) Aug

  22. Latombe, J.: Robot Motion Planning. Academic, Boston (1991)

    Google Scholar 

  23. Lewis, F.L., Xie, L., Popa, D.O.: Optimal and Robust Estimation: With an Introduction to Stochastic Control Theory. CRC, Florida, USA (2007)

    Google Scholar 

  24. Li, X., Yeh, A.G..: Neural-network-based cellular-automata for simulating multiple land use changes using GIS. Int. J. Geogr. Inf. Sci. 16(4), 323–344 (2002)

    Article  Google Scholar 

  25. Lippmann, R.P.: Pattern classification using neural networks. Commun. Mag, IEEE. 27(11), 47–50, 59–64 (1989) Nov

    Article  Google Scholar 

  26. Low, K.H., Gordon, G.J., Dolan, J.M., Khosla, P.: Adaptive Sampling for Multi-Robot Wide-Area Exploration. In: 2007 IEEE International Conference on Robotics and Automation. pp. 755–760, (2007) 10–14 Apr

  27. Mandel, J., Darema, F. (Ed.): Dynamic Data Driven Wildfire Modeling. Dynamic Data Driven Applications Systems. Academic, New York (2004)

    Google Scholar 

  28. Muzy, A., Innocenti, E., Aiello, A., Santucci, J., Wainer, G.: Specification of discrete event models for fire spreading. Simulation 81(2), 103–117 (2005)

    Article  Google Scholar 

  29. Olfati-Saber, R.: Distributed Kalman filter with embedded consensus filters. In: 44th IEEE Conference Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC ‘05 pp. 8179–8184, (2005) 12–15 Dec

  30. Popa, D.O.: Optimal sampling using singular value decomposition of the parameter variance space. In: 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3131–3136, (2005) 2–6 Aug

  31. Popa, D.O., Mysorewala, M.F., Lewis, F.L.: EKF-based adaptive sampling with mobile robotic sensor nodes. In: International Conference on Intelligent Robots and Systems, 2006 IEEE/RSJ, pp. 2451–2456 (2006) Oct

  32. Popa, D.O., Mysorewala, M.F., Lewis, F.L.: Deployment Algorithms and In-Door Experimental Vehicles for Studying Mobile Wireless Sensor Networks. To appear in ACIS International Journal of Sensor Networks (2009)

  33. Popa, D.O., Stephanou, H.E., Helm, C., Sanderson, A.C.: Robotic deployment of sensor networks using potential fields. In: The Proc. of International Conference on Robotics and Automation (2004) April–May

  34. Popa, D.O, et al.: Adaptive sampling algorithms for multiple autonomous underwater vehicles. In: Autonomous Underwater Vehicles, 2004 IEEE/OES, pp. 108–118 (2004) 17–18 June

  35. Rao, B.S., Durrant-Whyte, H.F.: Fully decentralised algorithm for multisensor Kalman filtering. IEE Proc Part D. 138(5), 413–420 (1991)

    Google Scholar 

  36. Rothermel, R.C.: How to predict the spread and intensity of forest and range fires. Gen. Tech. Rep. INT-143. U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT, p. 161 (1983)

  37. Roumeliotis, S.I., Bekey, G.A.: Distributed Multi-Robot Localization. Distributed Autonomous Robotic Systems 4. Springer Verlag, New York, pp. 179–188 (2000)

    Google Scholar 

  38. Russell, R.A., Bab-Hadiashar, A., Shepherd, R.L., Wallace, G.G.: A comparison of reactive robot chemotaxis algorithms. Robot. Auton. Syst. 45(2), 83–97 (2003) 30 Nov

    Article  Google Scholar 

  39. Scott, D.: Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley, New York (1992)

    Book  MATH  Google Scholar 

  40. Silverman, B.: Density Estimation for Statistics and Data Analysis. Chapman & Hall/CRC, USA (1986)

    MATH  Google Scholar 

  41. Singh, A. et al.: Multiscale sensing: a new paradigm for actuated sensing of high frequency dynamic phenomena. In: International Conference on Intelligent Robots and Systems, 2006 IEEE/RSJ, pp. 328–335 (2006) Oct

  42. Trunfio, G.A.: Predicting Wildfire Spreading through a Hexagonal Cellular Automata Model. ACRI, pp. 385–394 (2004)

  43. Wagner, V.: A simple fire growth model. Forestry Chron. 45, 103–104 (1969)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Automation & Robotics Research Institute, The University of Texas, Arlington, TX, USA

    Muhammad F. Mysorewala, Dan O. Popa & Frank L. Lewis

Authors
  1. Muhammad F. Mysorewala
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dan O. Popa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Frank L. Lewis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammad F. Mysorewala.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mysorewala, M.F., Popa, D.O. & Lewis, F.L. Multi-Scale Adaptive Sampling with Mobile Agents for Mapping of Forest Fires. J Intell Robot Syst 54, 535–565 (2009). https://doi.org/10.1007/s10846-008-9246-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-008-9246-1

Keywords

Search

Navigation