# Development of a Numerical Model of a Bio-Inspired Sea Lion Robot

## TL;DR

Researchers developed a numerical model for a sea lion-inspired robot to improve underwater robot performance by simulating and analyzing its motion in water.

## Contribution

A validated numerical model for a bio-inspired sea lion robot using Euler–Poincaré formulation and refined hydrodynamic coefficients.

## Key findings

- The model accurately predicts the robot's translation and orientation in water.
- Hydrodynamic coefficients were refined using a genetic algorithm to reduce the sim-to-real gap.
- The framework supports simulation, control, and optimization of bio-inspired multi-body systems.

## Abstract

There is a growing demand for underwater robots to support offshore tasks such as exploration, environmental monitoring, and critical underwater missions. To enhance the performance of these systems, researchers are increasingly turning to biological inspiration to develop robots that understand and adapt the swimming strategies of aquatic animals. Numerical modeling plays a critical role in evaluating and improving the performance of these complex, multi-body robotic systems. However, developing accurate models for multi-body robots that swim freely in three dimensions remains a significant challenge. This study presents the development and validation of a numerical model of a bio-inspired California sea lion (Zalophus californianus) robot. The model was developed to simulate, analyze, and visualize the robot’s body motions in water. The equations of motion were derived in closed form using the Euler–Poincaré formulation, offering advantages for control and stability analysis. Hydrodynamic coefficients essential for estimating fluid forces were computed using computational fluid dynamics (CFD) and strip theory and further refined using a genetic algorithm to reduce the sim-to-real gap. The model demonstrated strong agreement with experiments, accurately predicting the translation and orientation of the robot. This framework provides a validated foundation for simulation, control, and optimization of bio-inspired multi-body systems.