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

Savvas Papaioannou, Panayiotis Kolios, Christos G. Panayiotou, Marios M. Polycarpou

TL;DR

The paper tackles adaptive monitoring of a stochastic wildfire front using a mobile sensing agent, addressing the coupled challenges of sensing, estimation, and control under nonlinear, non-Gaussian dynamics. It develops a recursive Bayesian estimator for elliptical-fire-front propagation and reformulates the nonlinear problem as a finite-horizon MDP, solved via an information-seeking predictive controller based on a lower-confidence-bound (LCB) adaptive search, with asymptotic convergence to the optimal policy. Key contributions include a set-valued likelihood for multi-object set observations, a risk-weighted dispersion cost, and a particle-filter-based planning framework that handles uncertain sensing and environmental variability. The approach is validated in simulated environments showing that non-myopic planning improves fire-front state estimation and enables more effective information gathering for disaster response.

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.

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

TL;DR

The paper tackles adaptive monitoring of a stochastic wildfire front using a mobile sensing agent, addressing the coupled challenges of sensing, estimation, and control under nonlinear, non-Gaussian dynamics. It develops a recursive Bayesian estimator for elliptical-fire-front propagation and reformulates the nonlinear problem as a finite-horizon MDP, solved via an information-seeking predictive controller based on a lower-confidence-bound (LCB) adaptive search, with asymptotic convergence to the optimal policy. Key contributions include a set-valued likelihood for multi-object set observations, a risk-weighted dispersion cost, and a particle-filter-based planning framework that handles uncertain sensing and environmental variability. The approach is validated in simulated environments showing that non-myopic planning improves fire-front state estimation and enables more effective information gathering for disaster response.

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.
Paper Structure (13 sections, 10 equations, 2 figures, 1 algorithm)

This paper contains 13 sections, 10 equations, 2 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Problem Objective
  4. Fire Front Propagation Model
  5. Agent Dynamics and Sensing Model
  6. Adaptive Monitoring via Information-seeking Predictive Control
  7. Fire Front Recursive State Estimation
  8. Information-seeking Predictive Control
  9. Adaptive LCB-guided Policy Search
  10. Evaluation
  11. Simulation Setup
  12. Results
  13. Conclusion

Figures (2)

  • Figure 1: Simulation Setup: (a) Fire front true evolution, (b)(c) Wind speed parameters, (d) Risk map, and (e)(f) Fire spread rate parameters.
  • Figure 2: Performance Evaluation: (a) Agent trajectory ($\star$ and $\times$ indicate start and stop states respectively), and fire front state (i.e., estimated with solid line, and true state with dotted line) obtained with Alg. 1, over 25-steps experiment, with myopic settings (i.e., horizon $T=1$), (b) Same result with non-myopic settings (i.e. horizon $T=3$), (c) Estimation error for different configurations of the proposed approach and comparisons with baselines.