Reconfigurable Robotic Fish Reveals How Stiffness and Wave Propagation Shape Swimming Performance

Why It Matters

The findings reveal how tunable stiffness can reconcile speed and agility, informing next‑generation underwater vehicles that must operate in diverse environments.

Key Takeaways

• Robotic fish switches between eel and tuna swimming modes.
• Stiffness distribution drives speed and thrust in tuna mode.
• Anguilliform mode offers tighter turning radius, lower speed.
• Multimodal robot outperforms single‑mode in confined waterways.
• Findings guide design of agile underwater exploration drones.

Pulse Analysis

The ability to toggle between distinct swimming gaits marks a breakthrough in biomechanical experimentation. Traditional studies relied on live specimens, where measuring internal stiffness and wave dynamics is invasive or impossible. The PKU team’s modular soft robot provides a controllable testbed, allowing researchers to isolate the influence of body rigidity on propulsion efficiency. This level of experimental precision opens new avenues for understanding evolutionary trade‑offs among fish species and for translating those principles into engineered systems.

Performance data from the reconfigurable fish underscore the physics of wave‑driven thrust. In the high‑stiffness, tuna‑like configuration, the robot maintained coherent vortex rings and achieved a peak speed of 1.24 body lengths per second at 5 Hz, delivering 142% more thrust than its flexible counterpart. Computational fluid dynamics revealed that longer wavelength propagation and reduced drag at the head are key to this efficiency. Conversely, the anguilliform mode, with broader undulation and lower stiffness, produced fragmented wakes but excelled in maneuverability, turning within a quarter of its body length.

For the underwater robotics industry, these insights translate into practical design rules. Vehicles that can dynamically stiffen sections of their hull or adjust actuation patterns could switch between rapid transit and precise navigation without sacrificing performance. Such adaptability is critical for tasks ranging from environmental monitoring in tight coral reefs to rapid response in disaster‑affected waterways. As soft‑material technologies mature, the multimodal approach demonstrated here is likely to become a cornerstone of next‑generation autonomous underwater systems.