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.
Abstract (translated)
我们考虑使用移动代理（例如无人机）自适应监控野火前沿的问题，其轨迹决定了传感器数据的采集地点，并因此影响了火灾蔓延估计的准确性。这是一个具有挑战性的问题，因为野火演化的随机性质要求感测、估计和控制之间的无缝集成，而在现有方法中这些过程通常被单独处理。最先进的方法要么为了建立最优性而假设线性和高斯特性，要么依赖于近似值和启发式方法，往往不提供明确的性能保证。 为了解决这些问题，我们将野火前沿监控任务作为集成了感测、估计和控制的随机最优控制问题进行建模。我们推导出了一类具有随机非线性椭圆增长模型的野火前沿的递归贝叶斯最优估计器。接着，将由此产生的非线性随机控制问题转换为有限时间范围的马尔可夫决策过程，并通过基于下限置信度的自适应搜索算法设计了一个信息导向的预测控制法则，该方法具有渐进收敛于最优策略的特点。
URL
https://arxiv.org/abs/2601.11231
