2025/12/10 by Jingeun Kim, Kim, Jingeun, Yong-Hyuk Kim +3
Engineering · #Maritime Navigation and Safety #Underwater Vehicles and Communication Systems #UAV Applications and Optimization
paper · pdf · doi:10.48550/arxiv.2512.09260
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.