{"ID":2866814,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.20593","arxiv_id":"2509.20593","title":"Uncertainty-Aware Active Source Tracking of Marine Pollution using Unmanned Surface Vehicles","abstract":"This paper proposes an uncertainty-aware marine pollution source tracking framework for unmanned surface vehicles (USVs). By integrating high-fidelity marine pollution dispersion simulation with informative path planning techniques, we demonstrate effective identification of pollution sources in marine environments. The proposed approach is implemented based on Robot Operating System (ROS), processing real-time sensor data to update probabilistic source location estimates. The system progressively refines the estimation of source location while quantifying uncertainty levels in its predictions. Experiments conducted in simulated environments with varying source locations, wave conditions, and starting positions demonstrate the framework's ability to localise pollution sources with high accuracy. Results show that the proposed approach achieves reliable source localisation efficiently and outperforms the existing baseline. This work contributes to the development of full autonomous environmental monitoring capabilities essential for rapid response to marine pollution incidents.","short_abstract":"This paper proposes an uncertainty-aware marine pollution source tracking framework for unmanned surface vehicles (USVs). By integrating high-fidelity marine pollution dispersion simulation with informative path planning techniques, we demonstrate effective identification of pollution sources in marine environments. Th...","url_abs":"https://arxiv.org/abs/2509.20593","url_pdf":"https://arxiv.org/pdf/2509.20593v2","authors":"[\"Song Ma\",\"Yanchao Wang\",\"Richard Bucknall\",\"Yuanchang Liu\"]","published":"2025-09-24T22:23:06Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}