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Role of Pectoral Fin Flexibility in Robotic Fish Performance

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Abstract

Pectoral fins play a vital role in the maneuvering and locomotion of fish, and they have become an important actuation mechanism for robotic fish. In this paper, we explore the effect of flexibility of robotic fish pectoral fins on the robot locomotion performance and mechanical efficiency. A dynamic model for the robotic fish is presented, where the flexible fin is modeled as multiple rigid elements connected via torsional springs and dampers. Blade element theory is used to capture the hydrodynamic force on the fin. The model is validated with experimental results obtained on a robotic fish prototype, equipped with 3D-printed fins of different flexibility. The model is then used to analyze the impacts of fin flexibility and power/recovery stroke speed ratio on the robot swimming speed and mechanical efficiency. It is found that, in general, flexible fins demonstrate advantages over rigid fins in speed and efficiency at relatively low fin-beat frequencies, while rigid fins outperform flexible fins at higher frequencies. For a given fin flexibility, the optimal frequency for speed performance differs from the optimal frequency for mechanical efficiency. In addition, for any given fin, there is an optimal power/recovery stroke speed ratio, typically in the range of 2–3, that maximizes the speed performance. Overall, the presented model offers a promising tool for fin flexibility and gait design, to achieve speed and efficiency objectives for robotic fish actuated with pectoral fins.

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Acknowledgements

The authors would like to gratefully acknowledge Dr. Shahram Pouya for the useful discussion on scaling laws and Mr. John Thon for the technical support on the robotic fish prototype.

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Correspondence to Xiaobo Tan.

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Communicated by Maurizio Porfiri.

This work was supported by National Science Foundation (Grants IIP-1343413, IIS-1319602, CCF-1331852, ECCS-1446793).

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Bazaz Behbahani, S., Tan, X. Role of Pectoral Fin Flexibility in Robotic Fish Performance. J Nonlinear Sci 27, 1155–1181 (2017). https://doi.org/10.1007/s00332-017-9373-6

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