用实验数据训练LSTM模型，精准预测四足水下机器人的流体受力。
Learn to Swim: Data-Driven LSTM Hydrodynamic Model for Quadruped Robot Gait Optimization
- 基于实验数据训练LSTM，捕捉非线性水动力特性。
- 直线游动时偏差误差更小，转向时间更快且半径不变。
- 适合需高精度水下运动控制的仿生机器人研究者。
本文提出一种基于长短期记忆网络的流体实验数据驱动模型（FED-LSTM），用于预测所构建水下四足机器人的非定常、非线性水动力。该模型在循环水槽和拖曳水槽中进行的腿部受力与机体阻力实验数据上训练，相比传统用于平面流动预测的经验公式（EF）表现更优。模型在直线与转向步态优化中展现出更强的准确性与适应性，通过NSGA-II算法实现性能提升：直线游泳时减小了偏航误差，缩短了转向时间但未增大转弯半径。硬件实验进一步验证了其在精度与稳定性上优于经验公式。该方法为提升腿式机器人游泳性能提供了可靠框架，推动水下机器人运动学的发展。
原文摘要 · Abstract (English)
This paper presents a Long Short-Term Memory network-based Fluid Experiment Data-Driven model (FED-LSTM) for predicting unsteady, nonlinear hydrodynamic forces on the underwater quadruped robot we constructed. Trained on experimental data from leg force and body drag tests conducted in both a recirculating water tank and a towing tank, FED-LSTM outperforms traditional Empirical Formulas (EF) commonly used for flow prediction over flat surfaces. The model demonstrates superior accuracy and adaptability in capturing complex fluid dynamics, particularly in straight-line and turning-gait optimizations via the NSGA-II algorithm. FED-LSTM reduces deflection errors during straight-line swimming and improves turn times without increasing the turning radius. Hardware experiments further validate the model's precision and stability over EF. This approach provides a robust framework for enhancing the swimming performance of legged robots, laying the groundwork for future advances in underwater robotic locomotion.
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