发表机构
Collaborative Robotics and Intelligent Systems (CoRIS) Institute; Oregon State University（协作机器人与智能系统研究所; 俄勒冈州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对水下航行器动力学建模难题，提出基于李群的随机物理信息神经网络框架，经仿真与实境测试，可学习到精确鲁棒的动力学模型，支持复杂海洋环境下的安全模型基控制。
AI 中文摘要
水下航行器的精确运动模型是完成基础设施巡检、科学采样等海洋任务自主执行的必要条件，但传统基于物理的方法难以准确描述这类运动。本文提出一种数据驱动的新型框架，用于学习随机水下航行器动力学。利用欧拉-庞加莱动力学和李群几何，我们开发了一种随机物理信息神经网络架构，该架构符合水下航行器的物理和几何约束。我们的方法采用保结构随机积分，基于矩匹配和有限维匹配构建，以确保训练的几何一致性。我们在仿真环境和港口中航行于码头桩柱的水下航行器上评估了该方法，结果表明，我们的方法可学习到精确且鲁棒的动力学模型，能在复杂海洋环境中实现基于模型的安全控制。
英文摘要
Accurate models of underwater vehicle motion are needed for autonomous execution of marine tasks like infrastructure inspection and scientific sampling. However, such motion is challenging to characterize using traditional physics-based methods. This paper presents a novel data-driven framework for learning stochastic underwater vehicle dynamics. Using Euler-Poincaré dynamics and the geometry of Lie groups, we develop a stochastic physics-informed neural network architecture that respects the physical and geometric constraints of underwater vehicles. Our approach leverages structure-preserving stochastic integration and builds upon moment matching and finite dimensional matching to ensure geometrically-consistent training. We evaluate our approach in simulation and on an underwater vehicle navigating dock pylons in a harbor environment. The results demonstrate that our method learns accurate and robust dynamics models, enabling safe model-based control in challenging marine environments.
