From Forecast to Action: Uncertainty-Aware UAV Deployment for Ocean Drifter Recovery
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866910008194629632 |
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| author | Kim, Jingeun Kim, Yong-Hyuk Yoon, Yourim |
| author_facet | Kim, Jingeun Kim, Yong-Hyuk Yoon, Yourim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_09260 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Forecast to Action: Uncertainty-Aware UAV Deployment for Ocean Drifter Recovery Kim, Jingeun Kim, Yong-Hyuk Yoon, Yourim Neural and Evolutionary Computing 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. |
| title | From Forecast to Action: Uncertainty-Aware UAV Deployment for Ocean Drifter Recovery |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2512.09260 |