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Using inverse learning for controlling bionic robotic fish with SMA actuators

Abstract

In this study, we develop an untethered bionic soft robotic fish for swimming motion. The body of the fish is molded using soft silicone rubber, and we utilize shape memory alloy wires for its actuators. Its lightness and flexibility allow the robotic fish to generate biomimetic swimming motions. Due to the complexity of mathematically modeling the robot’s swimming dynamics, building a realistic simulator is prohibitively difficult. Hence, in this study, we introduce inverse learning for a feedforward neural network to generate control parameters for realizing desired swimming motions and subsequently utilize the neural network for real-time control. In this paper, we report on the electro-mechanical structure of our robotic fish and the experiment of the neuro-controller.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgments

This work was supported by JSPS Grants-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area) 18H05473 and 18H05895 and by JSPS Grant-in-Aid for Scientific Research (B) 20H04214.

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Correspondence to Kewei Ning.

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Ning, K., Hartono, P. & Sawada, H. Using inverse learning for controlling bionic robotic fish with SMA actuators. MRS Advances (2022). https://doi.org/10.1557/s43580-022-00328-w

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  • DOI: https://doi.org/10.1557/s43580-022-00328-w