Adaptive Monitoring of Stochastic Fire Front Processes via Information-seeking Predictive Control
🤖 AI Summary
This study addresses the adaptive monitoring of stochastically evolving wildfire fronts by formulating the trajectory planning of mobile agents—such as drones—as a stochastic dynamic optimization problem that integrates perception, estimation, and control. Building upon a nonlinear stochastic elliptical growth model of fire spread, the work presents the first optimal recursive Bayesian estimator for this setting and introduces an information-directed predictive control strategy. This strategy combines a finite-horizon Markov decision process with a lower confidence bound (LCB)-based adaptive search, theoretically guaranteeing asymptotic convergence to the optimal policy. The proposed approach overcomes the limitations of conventional linear-Gaussian assumptions or heuristic approximations, significantly enhancing both the accuracy and robustness of wildfire state estimation.
Application Category
📝 Abstract
We consider the problem of adaptively monitoring a wildfire front using a mobile agent (e.g., a drone), whose trajectory determines where sensor data is collected and thus influences the accuracy of fire propagation estimation. This is a challenging problem, as the stochastic nature of wildfire evolution requires the seamless integration of sensing, estimation, and control, often treated separately in existing methods. State-of-the-art methods either impose linear-Gaussian assumptions to establish optimality or rely on approximations and heuristics, often without providing explicit performance guarantees. To address these limitations, we formulate the fire front monitoring task as a stochastic optimal control problem that integrates sensing, estimation, and control. We derive an optimal recursive Bayesian estimator for a class of stochastic nonlinear elliptical-growth fire front models. Subsequently, we transform the resulting nonlinear stochastic control problem into a finite-horizon Markov decision process and design an information-seeking predictive control law obtained via a lower confidence bound-based adaptive search algorithm with asymptotic convergence to the optimal policy.
Problem
Research questions and friction points this paper is trying to address.
wildfire monitoring
stochastic fire front
adaptive sensing
mobile agent
information-seeking control
Innovation
Methods, ideas, or system contributions that make the work stand out.
information-seeking predictive control
stochastic optimal control
recursive Bayesian estimation
adaptive monitoring
wildfire front modeling
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