Underwater Soft Fin Flapping Motion with Deep Neural Network Based Surrogate Model
Journal:
arXiv
Published Date:
Feb 5, 2025
Abstract
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.
