用流场信息提升无人船在动态海洋中的导航成功率
MarineFormer: A Spatio-Temporal Attention Model for USV Navigation in Dynamic Marine Environments
- 用双注意力机制融合流场与传感器数据，改进导航决策
- 在模拟环境中使任务完成率提升23%，路径更短
- 适合研究海洋机器人自主导航的学者和工程师
在存在空间变化流扰动及动态、静态障碍物的海洋环境中，自主导航极具挑战性。本文表明，引入局部流场测量能从根本上改变问题性质，使原本无法解决的导航场景变得可解。然而，仅拥有流场数据仍不够，必须有效融合传统传感输入（如自身状态和障碍物状态）。为此，我们提出基于Transformer的MarineFormer策略架构，采用空间注意力实现传感器融合，时间注意力捕捉环境动态。MarineFormer在具有真实流场特征和障碍物的二维模拟环境中通过强化学习端到端训练。与经典及前沿基线相比，该方法使任务完成成功率提升近23%，同时减少路径长度。消融实验进一步验证了流场测量的关键作用及所提架构的有效性。
原文摘要 · Abstract (English)
Autonomous navigation in marine environments can be extremely challenging, especially in the presence of spatially varying flow disturbances and dynamic and static obstacles. In this work, we demonstrate that incorporating local flow field measurements fundamentally alters the nature of the problem, transforming otherwise unsolvable navigation scenarios into tractable ones. However, the mere availability of flow data is not sufficient; it must be effectively fused with conventional sensory inputs such as ego-state and obstacle states. To this end, we propose \textbf{MarineFormer}, a Transformer-based policy architecture that integrates two complementary attention mechanisms: spatial attention for sensor fusion, and temporal attention for capturing environmental dynamics. MarineFormer is trained end-to-end via reinforcement learning in a 2D simulated environment with realistic flow features and obstacles. Extensive evaluations against classical and state-of-the-art baselines show that our approach improves episode completion success rate by nearly 23\% while reducing path length. Ablation studies further highlight the critical role of flow measurements and the effectiveness of our proposed architecture in leveraging them.
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