Bottom-level motion control for robotic fish to swim in groups: modeling and experiments.

Area of Science:

• Robotics
• Collective Behavior
• Biomimetics

Background:

• Collective behavior in fish is a significant area of interest across multiple scientific disciplines.
• Previous research on robotic fish has focused on simulation, but experimental demonstrations of stable group swimming are lacking.
• A critical challenge is the absence of a reliable bottom-level motion control system for robotic fish platforms.

Purpose of the Study:

• To design and implement a robust motion controller for robotic fish.
• To enable multiple robotic fish to swim together in a stable group.
• To overcome limitations in existing robotic fish control systems for collective behavior studies.

Main Methods:

• Developed a high-accuracy controller for sub-carangiform robotic fish, enabling precise position and direction control.
• Created a hydrodynamic-model-based simulation platform for efficient controller parameter tuning.
• Validated the simulation platform through experimental speeding and turning tests on a robotic fish.
• Conducted extensive simulations and experiments to verify the accuracy and robustness of the bottom-level motion control system.

Main Results:

• The proposed motion controller demonstrated high accuracy in controlling individual robotic fish.
• The hydrodynamic simulation platform accurately predicted experimental results.
• The bottom-level motion control proved to be accurate and robust, even in complex fluid environments.
• Successful demonstration of group swimming with three robotic fish executing prescribed trajectories was achieved.

Conclusions:

• The developed motion control strategy provides a robust foundation for multi-robotic fish group swimming.
• This work addresses a critical gap in experimental collective behavior research using robotic platforms.
• The findings pave the way for future studies on the dynamics and control of coordinated swimming in artificial fish schools.