发表机构
School of Information Science and Technology, ShanghaiTech University; State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University; Laboratory of Cognitive and Decision Intelligence for Complex System, Institute of Automation, Chinese Academy of Sciences（上海科技大学信息科学与技术学院; 北京大学工程科学学院先进制造与机器人系湍流与复杂系统国家重点实验室; 中国科学院自动化研究所复杂系统认知与决策智能实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究机器鱼在未知湍流背景流中的自我中心驻位问题，提出SWiFT框架，集成实验装置、CFD模拟器及转移管道，通过强化学习探索驻位策略，相比现有方法有显著改进，验证了仅靠自我中心反馈可驻位，推动机器鱼控制发展。
AI 中文摘要
在流动水中接近目标位置并保持驻位是机器鱼在自然水生环境中运行的一项基本且关键的能力。尽管在提高游泳效率和机动性方面取得了数十年进展，但该能力仍未充分发展，主要是由于自由游动的机器鱼在流中存在特征描述不足、高度非线性的流固相互作用。为弥补这一差距，我们提出了SWiFT框架，这是一个游泳与流工具箱，通过强化学习能够在未知湍流背景流中为身体和/或尾鳍（BCF）机器鱼高效探索自我中心驻位策略。我们的SWiFT将自由游动的水槽实验装置与高效、物理上一致的基于计算流体动力学（CFD）的模拟器以及系统的模拟到现实转移管道集成在一起。所得策略在所有指标上都比现有方法有显著改进，尤其是距离的均方根误差（RMSE）。此外，我们验证了仅靠自我中心反馈，无需任何明确的流量传感，就能在未知湍流中实现驻位，这与趋流性的生物现象密切相似。因此，这种自我中心驻位策略的成功不仅推动了机器鱼控制向实际应用发展，也凸显了SWiFT作为解决水下机器人复杂游泳任务基础的潜力。
英文摘要
Approaching a target position and holding station in flowing water is a fundamental and critical capability for robotic fish operating in natural aquatic environments. Despite decades of advances in enhancing swimming efficiency and maneuverability, this capability remains underdeveloped, largely owing to the insufficiently characterized, highly nonlinear fluid-structure interactions inherent to freely swimming robotic fish in flows. To bridge this gap, we propose the SWiFT framework, a Swimming With Flow Toolbox that enables the efficient exploration of an egocentric station-holding policy for a body and/or caudal fin (BCF) robotic fish in unknown and turbulent background flows via reinforcement learning (RL). Our SWiFT integrates a free-swimming flow-tank experimental setup with a highly efficient, physically consistent computational fluid dynamics (CFD)-based simulator and a systematic sim-to-real transfer pipeline. The resulting policy achieves substantial improvements over state-of-the-art methods across all metrics, most notably root-mean-square error (RMSE) of distance. Furthermore, we validated that egocentric feedback alone, without any explicit flow sensing, enables station-holding in unknown turbulent flows, closely mirroring the biological phenomenon of rheotaxis. Accordingly, the success of this egocentric station-holding policy not only advances robotic fish control toward real-world deployment, but also highlights SWiFT's promise as a foundation for tackling complex swimming tasks for underwater robots.
Comments20 pages, 18 figures
