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

TL;DR

This paper presents a unified stochastic control framework for adaptive wildfire front monitoring using drones, integrating sensing, estimation, and control with guarantees of asymptotic optimality.

Contribution

It formulates wildfire monitoring as a stochastic optimal control problem and develops an information-seeking predictive control law with convergence guarantees.

Findings

Derived an optimal Bayesian estimator for nonlinear fire models.

Transformed the control problem into a finite-horizon MDP.

Designed an adaptive control law with asymptotic optimality.

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.