Learn to Swim: Data-Driven LSTM Hydrodynamic Model for Quadruped Robot Gait Optimization

• URL: http://arxiv.org/abs/2505.03146v1
• Date: Tue, 06 May 2025 03:42:16 GMT
• Title: Learn to Swim: Data-Driven LSTM Hydrodynamic Model for Quadruped Robot Gait Optimization
• Authors: Fei Han, Pengming Guo, Hao Chen, Weikun Li, Jingbo Ren, Naijun Liu, Ning Yang, Dixia Fan,
• Abstract summary: This paper presents a network-based Fluid Experiment Data-Driven model (FED-LSTM) for predicting unsteady, nonlinear hydrodynamic forces on an underwater quadruped robot.<n>We trained on experimental data from leg force and body drag tests conducted in both a recirculating water tank and a towing tank.<n>The model demonstrates superior accuracy and adaptability in capturing complex fluid dynamics.
• Score: 3.0610505741393057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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