ランダムキー最適化によるUAV捜索ミッションの検出確率最大化
Maximizing Detection Probability in UAV Search Missions using Random-Key Optimizers
UAVの捜索救助における経路計画を、確率更新とランダムキー最適化を組み合わせた手法で動的に最適化し、検出確率と資源のバランスを取る研究。
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著者: Antonio Dantas, Elcio Shiguemori, Antonio Chaves
分類: cs.NE, cs.RO
原文アブストラクト
This paper focuses on the stochastic and real-time constraints of Unmanned Aerial Vehicle (UAV) path planning in Search and Rescue (SAR) missions. We introduce a configurable data-driven and intelligent routing architecture that combines a closed-loop probability update with the Random-Key Optimizers (RKO). Our problem is mathematically modeled as a dynamic probabilistic variant of the Open Vehicle Routing Problem (OVRP), enabling the solver to switch adaptively between two objectives, thereby balancing probability maximization and resource minimization. The framework degrades elegantly under noisy priors but not under biased ones: a systematically biased map performs about half as well as flying with no prior at all. Computational tests show that the proposed scheme is scalable and competitive with single-path scenarios in terms of solution performance and serves as a strong decision guide for intelligent planning in SAR operations in unstable environments
