Learn to Swim: Data-Driven LSTM Hydrodynamic Model for Quadruped Robot Gait Optimization
This work addresses the problem of optimizing swimming gaits for underwater legged robots, representing an incremental improvement in hydrodynamic modeling for a specific domain.
The paper tackled predicting hydrodynamic forces on an underwater quadruped robot by developing an LSTM-based model trained on experimental data, which outperformed traditional methods by reducing deflection errors in straight-line swimming and improving turn times without increasing turning radius.
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.