用无人船主动追踪海洋污染源，能自适应环境并准确判断定位结果。
Active Tracking of Marine Pollution Sources: An Uncertainty-Aware Categorical Bayesian Framework for Unmanned Surface Vehicles
- 基于贝叶斯分类分布与信息路径规划，实时更新污染源位置信念。
- 95.8%定位成功率，在多种海况下优于传统方法。
- 引入可信区间作为终止条件，适合自主环保监测系统部署。
本文提出一种面向无人水面艇（USV）的不确定性感知框架，用于主动追踪海洋污染源。该框架采用由贝叶斯推断驱动的信息路径规划（IPP），将污染源位置信念建模为类别分布。研究构建了高保真度仿真流程，结合计算流体动力学（CFD）模拟真实污染物扩散，以及基于Gazebo的水动力模型和ArduPilot控制。此外，引入最小可信区间（SCI）作为估计不确定性的量化指标，并用作自主终止准则。在多种波浪条件和源位置下的大量仿真表明，该框架实现了95.8%的成功率，在定位精度和环境适应性方面显著优于基线方法。该框架具备可扩展性且兼容ROS，为全自动环境监测与快速应急响应提供基础支持。
原文摘要 · Abstract (English)
This paper presents an uncertainty-aware framework for the active tracking of marine pollution sources using Unmanned Surface Vehicles (USVs). The proposed framework employs an Informative Path Planning (IPP) strategy driven by Bayesian inference, modelling the belief of source location as a categorical distribution. This work presents a high-fidelity simulation pipeline, coupling Computational Fluid Dynamics (CFD) for realistic pollutant dispersion with Gazebo-based hydrodynamics and ArduPilot for USV control. Furthermore, this paper introduces the Smallest Credible Interval (SCI) as a metric to quantify estimation uncertainty and to serve as an autonomous termination criterion. Extensive simulations across diverse wave conditions and source locations demonstrate that the proposed framework achieves a 95.8% success rate, significantly outperforming baseline methods in both localisation accuracy and environmental adaptability. This framework provides a scalable and ROS-compatible foundation for fully autonomous environmental monitoring and rapid incident response.
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