  Wake sensing for bioinspired robotic swimmers has been the focus of much
investigation owing to its relevance to locomotion control, especially in the
context of schooling and target following. Many successful wake sensing
strategies have been devised based on models of von Karman-type wakes; however,
such wake sensing technologies are invalid in the context of exotic wake types
that commonly arise in swimming locomotion. Indeed, exotic wakes can exhibit
markedly different dynamics, and so must be modeled and sensed accordingly.
Here, we propose a general wake detection protocol for distinguishing between
wake types from measured hydrodynamic signals alone. An ideal-flow model is
formulated and used to demonstrate the general wake detection framework in a
proof-of-concept study. We show that wakes with different underlying dynamics
impart distinct signatures on a fish-like body, which can be observed in
time-series measurements at a single location on the body surface. These
hydrodynamic wake signatures are used to construct a wake classification
library that is then used to classify unknown wakes from hydrodynamic signal
measurements. The wake detection protocol is found to have an accuracy rate of
over 95% in the majority of performance studies conducted here. Thus, exotic
wake detection is shown to be viable, which suggests that such technologies
have the potential to become key enablers of multi-model sensing and locomotion
control strategies in the future.
