{"ID":2830516,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.09260","arxiv_id":"2512.09260","title":"From Forecast to Action: Uncertainty-Aware UAV Deployment for Ocean Drifter Recovery","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2512.09260","url_pdf":"https://arxiv.org/pdf/2512.09260v2","authors":"[\"Jingeun Kim\",\"Yong-Hyuk Kim\",\"Yourim Yoon\"]","published":"2025-12-10T02:31:17Z","proceeding":"cs.NE","tasks":"[\"cs.NE\"]","methods":"[\"Language Model\"]","has_code":false}