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.
View on arXiv@article{han2025_2505.03146,
title={ Learn to Swim: Data-Driven LSTM Hydrodynamic Model for Quadruped Robot Gait Optimization },
author={ Fei Han and Pengming Guo and Hao Chen and Weikun Li and Jingbo Ren and Naijun Liu and Ning Yang and Dixia Fan },
journal={arXiv preprint arXiv:2505.03146},
year={ 2025 }
}