Multi-Scale Adaptive Sampling with Mobile Agents for Mapping of Forest Fires |
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Authors: | Muhammad F Mysorewala Dan O Popa Frank L Lewis |
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Affiliation: | (1) Automation & Robotics Research Institute, The University of Texas, Arlington, TX, USA |
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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. |
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Keywords: | Adaptive sampling Sensor fusion Kalman filter Potential fields Environmental monitoring Forest fires |
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