Learning hydrodynamic signatures through proprioceptive sensing by bioinspired swimmers

Abstract

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.

Proprioceptive wake classification by a body with a passive tail

“…The results in this paper as well as in [44] are valid under the assumption that the the sensing body is directly behind the forced one in the flow. Some lateral displacement (offset) of the trailing body from the center line of the reverse Kármán vortex wake created by the leading body, occurs naturally in the experiments due to the short tether with which the trailing foil is connected to the water tunnel.…”

“…Though multiple minutes of data are available for every wake, our objective is to develop a classifier that can determine changes in the wake in near real time, requiring high performance on only a portion of the available data. Previous work [44] with a different network architecture found that, for a rigid hydrofoil, windows of input exceeding 5 s in length do not show significant increases in classification accuracy, so we adopt a 5 s time series, or 150 points at 30 Hz, as the input. We will denote the set of all 5 s windows of time series extracted from the experimental measurements as X.…”

“…Recent research into the computing power of dynamic systems in the context of reservoir computing [42,43], finds that applying complex time-series information as forcing to a complex nonlinear system (referred to as a 'reservoir') can result in information about the original system being encoded in simplified form in the states of the reservoir. In previous work [44] we introduce a rigid hydrofoil which is pinned at its leading edge in the vortex wake of a pitching upstream body as a physical reservoir, and showed that the one degree-of-freedom kinematics of its rotation contains enough information to accurately classify the wake Strouhal number without the need for sophisticated sensors. Here we extend that work by showing that a similarly pinned body with an additional freely rotating tail can serve as a more effective physical reservoir and encode more information about the flow into its two-dimensional velocity kinematics, allowing more accurate classification of wake parameters.…”

“…Performing classification of the resulting time-series data is a well-researched problem in artificial intelligence with multiple viable solutions. In related prior work [44], a shallow dense artificial neural network (DNN) was used to perform a classification of time-series data obtained from the kinematics of the rigid hydrofoil in a vortex wake. Specialist architectures designed to exploit the specific characteristics of time-series data, such as convolutional neural networks (CNNs) and recursive neural networks (RNNs), have been found to match or exceed the performance of the state-ofthe-art non-deep learning algorithms on time series classification [45].…”

A comprehensive review on fish-inspired robots

“…Recently, the increasing interest in proprioceptive sensing provides a new direction for underwater perception, which utilizes the kinematics or parts of the body to extract useful information inflow. 142 Since related research studies are just beginning, future directions might include determining the type of proprioceptive signal perceived and clarifying the neurotransmission mechanisms between proprioceptive and motor control. 143 From a long-term perspective, it is exciting to combine proprioceptive sensing with an ALL to realize powerful underwater perception.…”