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

TL;DR

This paper introduces an integrated framework combining trajectory prediction and UAV deployment optimization for maritime rescue, significantly improving search efficiency over traditional methods.

Contribution

It presents a novel end-to-end predict-then-optimize approach that incorporates uncertainty modeling and dynamic UAV deployment strategies for ocean drifter recovery.

Findings

Outperforms random search baselines in real-world tests

Effectively models spatial uncertainty using Gaussian-based sampling

Adapts UAV detection radii dynamically based on distance

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.