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Research studies method of fighting fire with UAVs

November 29, 2018  - By

By Sharon Rabinovich, Renwick E. Curry and Gabriel H. Elkaim, University of California, Santa Cruz

Figure 1. Greedy uncertainty suppression (GUS). (Chart: Authors)

Figure 1. Greedy uncertainty suppression (GUS). (Chart: Authors)

Exploring a wide area in search of a hazardous substance emitting source or expansion of a fire front is an ideal UAV mission. Wildfire monitoring missions exemplify such a problem.

Most multi-UAV systems address problems related to search in an environment of interest. The UAVs cooperate and share data to obtain information within a certain aspect of the environment.

Regardless of the number of UAVs and size of the area of interest (AOI), cooperative systems deliver a perfectly up-to-date picture of the environment with coordination.

This paper investigates a coordination scheme for missions facing uncertainty about the periphery in the AOI. It takes into account the UAVs’ state, observations, the overall mission, and allocates each UAV to a specific task, enabling the multi-UAV system to act in a coordinated manner.

If a coordination algorithm for an environment with uncertainty is available, the overall system still leans on sensing capabilities. Even if the system uses the most advanced sensors, sometimes the environmental conditions are restrictive; that is, UAV sensors cannot reach far enough, and measured data can only be local and quantized data.

The goal of quantized estimation is to develop techniques to effectively reconstruct the data. The research approach relies on a technique for estimation of propagated boundary with quantized measurements and proposes a new class of one-dimensional estimator: the Greedy Uncertainty Suppression (GUS) strategy.

The monitoring application involves large numbers of possibly randomly distributed inexpensive sensors, with limited sensing and processing. The estimator incorporates observations gathered by multiple observers and uses the quantized kalman filter estimation to update the expected location and unobserved spreadrate.

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