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Objects moving in water or stationary objects in streams create a vortex wake. Such vortex wakes encode information about the objects and the flow conditions. Underwater robots that often function with constrained sensing capabilities can benefit from extracting this information from vortex wakes. Many species of fish do exactly this, by sensing flow features using their lateral lines as part of their multimodal sensing. To replicate such capabilities in robots, significant research has been devoted to developing artificial lateral line sensors that can be placed on the surface of a robot to detect pressure and velocity gradients. We advance an alternative view of embodied sensing in this paper; the kinematics of a swimmer’s body in response to the hydrodynamic forcing by the vortex wake can encode information about the vortex wake. Here we show that using artificial neural networks that take the angular velocity of the body as input, fish-like swimmers can be trained to label vortex wakes which are hydrodynamic signatures of other moving bodies and thus acquire a capability to ‘blindly’ identify them.

In the recent past the design of many aquatic robots has been inspired by the motion of fish. In some recent work the authors described an underactuated planar swimming robot, that is propelled via the motion of an internal rotor. This robot is inspired by a simplified model of the fluid-body interaction mediated by singular distributions of vorticity. Such a model is a significant simplification of the fluid-structure interaction that can be understood using resource intensive numerical computations of the Navier Stokes equation that are unwieldy from a controls perspective. At the same time the simplified model incorporates the creation of vorticity and interaction of the body with the vorticity which many control theoretical models ignore. In this paper we show that despite the complexity of the interaction between the aquatic robot and the ambient vorticity in a fluid, the response of the robot is a nearly linear function of the control input. This surprisingly simple feature emerges in our theoretical model and is validated by our experimental data of the motion of the robot. This simplifying observation is an important step towards developing control algorithms for aquatic robots.