We present a novel predict-then-optimize framework for maritime search operations that integrates trajectory forecasting with UAV deployment optimization-an end-to-end approach not addressed in prior work. A large language model predicts the drifter's trajectory, and spatial uncertainty is modeled using Gaussian-based particle sampling. Unlike traditional static deployment methods, we dynamically adapt UAV detection radii based on distance and optimize their placement using meta-heuristic algorithms. Experiments on real-world data from the Korean coastline demonstrate that our method, particularly the repair mechanism designed for this problem, significantly outperforms the random search baselines. This work introduces a practical and robust integration of trajectory prediction and spatial optimization for intelligent maritime rescue.

翻译：本文提出了一种新颖的预测-优化框架，用于海上搜救任务，该框架将轨迹预测与无人机部署优化相结合——这是一种现有研究未曾涉及的端到端方法。我们利用大语言模型预测漂流物的运动轨迹，并采用基于高斯分布的粒子采样对空间不确定性进行建模。与传统的静态部署方法不同，我们根据距离动态调整无人机的探测半径，并运用元启发式算法优化其部署位置。在韩国海岸线真实数据上的实验表明，我们的方法——特别是针对该问题设计的修复机制——显著优于随机搜索基线。本研究为智能海上救援提供了一种实用且鲁棒的轨迹预测与空间优化集成方案。