確率的な火災前線プロセスの情報探索型予測制御による適応的モニタリング
Adaptive Monitoring of Stochastic Fire Front Processes via Information-seeking Predictive Control
ドローンなどの移動エージェントを用いて山火事の前線を監視する際に、センシング・推定・制御を統合した確率的制御問題として定式化し、情報探索型の予測制御則を設計した研究。
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著者: Savvas Papaioannou, Panayiotis Kolios, Christos G. Panayiotou, Marios M. Polycarpou
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
