From Forecast to Action: Uncertainty-Aware UAV Deployment for Ocean Drifter Recovery

• URL: http://arxiv.org/abs/2512.09260v1
• Date: Wed, 10 Dec 2025 02:31:17 GMT
• Title: From Forecast to Action: Uncertainty-Aware UAV Deployment for Ocean Drifter Recovery
• Authors: Jingeun Kim, Yong-Hyuk Kim, Yourim Yoon,
• Abstract summary: We present a novel framework for maritime search operations that integrates trajectory forecasting with UAV deployment optimization.<n>A large language model predicts the drifter's trajectory, and spatial uncertainty is modeled using Gaussian-based particle sampling.<n>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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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