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Path planning over spatiotemporal models can be applied to a variety ofapplications such as UAVs searching for spreading wildfire in mountains ornetwork of balloons in time-varying atmosphere deployed for inexpensiveinternet service. A notable aspect in such applications is the dynamicallychanging environment. However, path planning algorithms often assume staticenvironments and only consider the vehicle's dynamics exploring theenvironment. We present a spatiotemporal model that uses a cross-correlationoperator to consider spatiotemporal dependence. Also, we present an adaptivestate estimator for path planning. Since the state estimation depends on thevehicle's path, the path planning needs to consider the trade-off betweenexploration and exploitation. We use a high-level decision-maker to choose anexplorative path or an exploitative path. The overall proposed frameworkconsists of an adaptive state estimator, a short-term path planner, and ahigh-level decision-maker. We tested the framework with a spatiotemporal modelsimulation where the state of each grid transits from normal, latent, and firestate. For the mission objective of visiting the grids with fire, the proposedframework outperformed the random walk (baseline) and the single-mindedexploitation (or exploration) path.

Estimation and Planning of Exploration Over Grid Map Using A Spatiotemporal Model with Incomplete State Observations