• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2626-2631
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• 2011

CST(C-shape sharp turn) represented the motion whereby fish bend their tail quickly in a C-shape to achieve an emergent changing of its swimming direction on fish swimming. But there is not yet the general motion trajectory functions related to CST. In this paper, we proposed the very simple motion functions related to CST sequence recorded from a real fish by biologists. Through the computer simulations, we confirmed the usefulness of the proposed function.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.510-518
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• 2014

In this study, in order to improve and optimize the performance of the turning mechanism for a fish robot in the fluid, we propose the tail joint trajectories using neural networks to mimic the CST(C-shape Sharp Turn) patterns of a real fish which is optimized in the natural environment. In order to mimic the CST patterns of a fish, we convert the sequential recording CST patterns into the coordinate data, and change the numerical coordinate data into a functions. We change the motion functions to the relative joint angles which is adapted to suit robot's shape and data. However, these relative joint trajectories obtained by the sequential recording of the carp have low-precision. It is difficult to apply to the control of a fish robot. Therefore, the relative joint trajectories are interpolated using neural networks with superior generalization ability and applied to the fish robot. we have found that the proposed method using neural networks is superior to ones using high-order polynomial equation through the computer simulations.