[论文解读] A Robust Simulation Framework for Verification and Validation of Autonomous Maritime Navigation in Adverse Weather and Constrained Environments

Mayur S. Patil, Nataraj Sudharsan+10 more|arXiv (Cornell University)|Mar 3, 2026

Maritime Navigation and Safety被引用 0

一句话总结

该论文提出了一个增强的虚拟仿真框架，将高保真天气和水深建模整合到对现实 maritime 条件下 MASS 的 V&V 中。

ABSTRACT

Maritime Autonomous Surface Ships (MASS) have emerged as a promising solution to enhance navigational safety, operational efficiency, and long-term cost effectiveness. However, their reliable deployment requires rigorous verification and validation (V\&V) under various environmental conditions, including extreme and safety-critical scenarios. This paper presents an enhanced virtual simulation framework to support the V\&V of MASS in realistic maritime environments, with particular emphasis on the influence of weather and bathymetry on autonomous navigation performance. The framework incorporates a high-fidelity environmental modeling suite capable of simulating adverse weather conditions such as rain, fog, and wave dynamics. The key factors that affect weather, such as rain and visibility, are parameterized to affect sea-state characteristics, perception, and sensing systems, resulting in position and velocity uncertainty, reduced visibility, and degraded situational awareness. Furthermore, high-resolution bathymetric data from major U.S. ports are integrated to enable depth-aware navigation, grounding prevention capabilities, and evaluation of vessel controllability in shallow or confined waterways. The proposed framework offers extensive configurability, enabling systematic testing in a wide spectrum of maritime conditions, including scenarios that are impractical or unsafe to replicate in real-world trials, thus supporting the V\&V of MASS.

研究动机与目标

• 推动在不利环境条件下对 Maritime Autonomous Surface Ships (MASS) 的鲁棒验证与确认。
• 在现有仿真平台中增加天气、雷达传感退化和水深信息以实现深度感知导航。
• Enable configurable, realistic testing scenarios that are impractical in real-world trials.
• Enable configurable, realistic testing scenarios that are impractical in real-world trials.

提出的方法

• 将天气、雷达传感和水深模型整合到 Unity/MATLAB-Simulink/ROS2 数字孪生框架中。
• 使用可配置的可视性与遮挡感知管线对雷达测量进行建模。
• 利用 ITU-R P.838 与 P.840 模型实现天气诱发的雷达衰减，计算 A_w 及对 SNR 的影响。
• 整合 NOAA/USGS 的高分辨率水深数据，转换为用于规划的深度感知占用栅格。
• 在基于栅格的增强规划框架内，结合深度相关的波浪载荷与接触式底盘规避（grounding-aware CDCA）。
• 通过跨多种天气与雷达配置的综合性能指标（PIs）评估 MASS 的性能。

实验结果

研究问题

• RQ1 恶劣天气条件如何影响雷达感知、感知精度及 MASS 的自主导航性能？
• RQ2 水深与深度约束对深度感知规划、规避接触和船舶可控性有何影响？
• RQ3 不同雷达配置如何减轻或放大在仿真环境不确定性下的性能下降？
• RQ4 增强的仿真框架是否能够为环境感知的 MASS 测试提供可重复、可扩展的 V&V？
• RQ5 在具有挑战性的条件下，反映碰撞避免鲁棒性与控制器稳定性的关键性能指标有哪些？

主要发现

• 环境因素显著影响船舶性能，恶劣天气下测量不确定性增加并触发过早的碰撞避免响应。
• 高功率雷达配置在不利条件下降低目标状态估计不确定性并稳定自治模块。
• 雷达在发射功率降低、歧义增多、目标跟踪噪声增大时性能下降，导致控制轨迹更嘈杂。
• 水深整合实现深度感知的规划与规避接触，影响浅水区域的波浪载荷与船舶响应。
• 该框架清晰显示了环境与海床地形对自治导航的敏感性，为 MASS 的环境感知 V&V 提供支持。