Proprioceptive wake classification by a body with a passive tail.

The remarkable ability of some marine animals to identify flow structures and parameters using complex non-visual sensors, such as lateral lines of fish and the whiskers of seals, has been an area of investigation for researchers looking to apply this ability to artificial robotic swimmers, which could lead to improvements in autonomous navigation and efficiency. Several species of fish in particular have been known to school effectively, even when blind. Beyond specialized sensors like the lateral lines, it is now known that some fish use purely proprioceptive sensing, using the kinematics of their fins or tails to sense their surroundings. In this paper we show that the kinematics of a body with a passive tail encode information about the ambient flow, which can be deciphered through machine learning. We demonstrate this with experimental data of the angular velocity of a hydrofoil with a passive tail that lies in the wake generated by an upstream oscillating body. Using convolutional neural networks, we show that with the kinematic data from the downstream body with a tail, the wakes can be better classified than in the case of a body without a tail. This superior sensing ability exists for a body with a tail, even if only the kinematics of the main body are used as input for the machine learning. This shows that beyond generating 'additional inputs', passive tails modulate the response of the main body in manner that is useful for hydrodynamic sensing. These findings have clear application for improving the sensing abilities of bioinspired swimming robots.

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