Adaptive Monitoring of Stochastic Fire Front Processes via Information-seeking Predictive Control

• URL: http://arxiv.org/abs/2601.11231v2
• Date: Sat, 21 Feb 2026 11:30:37 GMT
• Title: Adaptive Monitoring of Stochastic Fire Front Processes via Information-seeking Predictive Control
• Abstract summary: We consider the problem of adaptively monitoring a wildfire front using a mobile agent (e.g., a drone)<n>This is a challenging problem, as the nature of wildfire evolution requires the seamless integration of sensing, estimation, and control.<n>We formulate the fire front monitoring task as an optimal control problem that integrates sensing, estimation, and control.
• Score: 7.775545390766459
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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