Ma, Song;
Bucknall, Richard;
Liu, Yuanchang;
(2025)
Uncertainty-Aware Active Source Tracking of Marine Pollution using Unmanned Surface Vehicles.
In:
Proceedings of the Oceantech: Marine Robotics & Science Workshop, IROS 2025.
(pp. pp. 1-8).
IEEE
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Abstract
his 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, flow 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. This work contributes to the development of full autonomous environmental monitoring capabilities essential for rapid response to marine pollution incidents.
| Type: | Proceedings paper |
|---|---|
| Title: | Uncertainty-Aware Active Source Tracking of Marine Pollution using Unmanned Surface Vehicles |
| Event: | Oceantech: Marine Robotics & Science Workshop, IROS 2025 |
| Location: | Hangzhou, China |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://www.iros25.org/WorkshopsTutorials.html |
| Language: | English |
| Additional information: | For the purpose of open access, the author(s) has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission. |
| Keywords: | marine robotics, informative path planning, environmental monitoring, decision making, unmanned surface vehicle |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10214382 |
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