发表机构
School of Engineering & Physical Sciences, Heriot-Watt University; Engineering Department, Japan Agency for Marine-Earth Science and Technology（赫瑞瓦特大学工程与物理科学学院; 日本海洋研究开发机构工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对现有水下模拟器的不足，扩展Stonefish模拟器，加入多类物理与传感器模型，实现更贴合深海机器人需求的仿真，为相关研究提供实用基础。
AI 中文摘要
许多近期的水下模拟器侧重视觉写实性，却牺牲了物理保真度，聚焦于浅水效应，这类效应与深海环境的相关性有限，且计算成本高昂。本研究将研究重点转向深海物理与传感器写实性，提出了Stonefish模拟器的物理与传感器接地扩展版本，该版本为其流体动力学模型增添了随机IMU和DVL漂移、磁力计干扰、高阶流体动力学、地面力学、压力驱动的环境变异性以及基于物理的水下光学特性。这些新增内容旨在更好地捕捉塑造深海自主水下航行器（AUV）、遥控潜水器（ROV）、着陆器、自主水面舰艇（ASV）和滑翔机行为的力与测量值，同时保持与实时仿真的兼容性。本研究推动水下仿真向更具代表性的深海作业条件发展，这对长时导航和基于学习的自主性尤为重要，因为不准确的传感器与环境模型会引入非物理伪影和过于乐观的性能。尽管仍存在挑战，如复杂的流固相互作用和完全的环境随机性，但所提出的框架为深海条件下的导航、感知与自主性研究提供了实用基础。
英文摘要
Many recent underwater simulators emphasize visual realism at the expense of physical fidelity, focusing on shallow-water effects with limited relevance in deep-water environments and high computational cost. In this work, we shift the focus toward deep-sea physical and sensor realism. We present a physics- and sensor-grounded extension of the Stonefish simulator that augments its hydrodynamic models with stochastic IMU and DVL drift, magnetometer disturbances, higher-order hydrodynamics, terramechanics, pressure-driven environmental variability, and physically based underwater optics. These additions are designed to better capture the forces and measurements shaping the behavior of deep-ocean AUVs, ROVs, landers, ASVs, and gliders, while remaining compatible with real-time simulation. This work advances underwater simulation toward more representative deep-sea operating conditions, which is particularly relevant for long-duration navigation and learning-based autonomy, where inaccurate sensor and environmental models introduce non-physical artifacts and overly optimistic performance. While challenges remain, including complex fluid-structure interactions and full environmental stochasticity, the proposed framework provides a practical foundation for navigation, perception, and autonomy research under deep-sea conditions.
