用深度神经网络模拟水下软鳍运动，实现精准控制。
Underwater Soft Fin Flapping Motion with Deep Neural Network Based Surrogate Model
- 用DNN构建仿真模型替代真实实验，加速强化学习训练
- 在真实软鳍执行器上实现复杂推力动作与高精度控制
- 适合需要高效控制的水下机器人研发人员
本研究提出一种新型框架，通过将基于深度神经网络（DNN）的代理模型与强化学习（RL）结合，实现鳍驱动水下机器人的精确受力控制。为应对复杂水下环境交互及高昂实验成本，DNN代理模型充当仿真器，支持RL智能体高效训练。同时采用网格切换控制策略，针对不同力参考范围选择最优模型，提升控制精度与稳定性。实验结果表明，经代理仿真训练的RL智能体可在真实软鳍执行器上生成复杂推力运动，并实现精准控制。该方法为复杂水下环境中鳍驱动机器人提供了高效控制解决方案。
原文摘要 · Abstract (English)
This study presents a novel framework for precise force control of fin-actuated underwater robots by integrating a deep neural network (DNN)-based surrogate model with reinforcement learning (RL). To address the complex interactions with the underwater environment and the high experimental costs, a DNN surrogate model acts as a simulator for enabling efficient training for the RL agent. Additionally, grid-switching control is applied to select optimized models for specific force reference ranges, improving control accuracy and stability. Experimental results show that the RL agent, trained in the surrogate simulation, generates complex thrust motions and achieves precise control of a real soft fin actuator. This approach provides an efficient control solution for fin-actuated robots in challenging underwater environments.
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