Detecting exotic wakes with hydrodynamic sensors
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 Kármán-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. Under ideal settings, the wake detection protocol is found to have an accuracy rate of over 95% in the majority of performance studies conducted. Further, proper tuning can lead to accuracy rates of 80% or better in low signal-to-noise environments. Thus, exotic wake detection is shown to be a viable concept, suggesting that such technologies have the potential to become key enablers of multiple-model sensing and locomotion control strategies in the future.
KeywordsWake detection Flow classification Bioinspired sensing Machine learning Vertex dynamics Hydrodynamic signals
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