Detecting exotic wakes with hydrodynamic sensors
Detecting exotic wakes with hydrodynamic sensors
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DOI：
10.1007/s00162-019-00493-z
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发表时间：
2017-11
影响因子：
3.4
通讯作者：
Mengying Wang;Maziar S. Hemati
中科院分区：
文献类型：
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作者：
Mengying Wang;Maziar S. Hemati
仿生机器人游泳者的唤醒传感由于其与运动控制的相关性而成为许多研究的焦点，特别是在学校教育和目标跟踪的背景下。许多成功的尾流传感策略都是基于冯·卡门型尾流模型设计的；然而，这种尾流传感技术在游泳运动中常见的奇异尾流类型的情况下是无效的。事实上，奇异的尾流可能表现出明显不同的动态，因此必须相应地进行建模和感知。在这里，我们提出了一种通用尾流检测协议，用于仅从测量的流体动力信号中区分尾流类型。制定了理想流模型并用于在概念验证研究中演示一般尾流检测框架。我们表明，具有不同潜在动力学的尾流会在类鱼身体上赋予不同的特征，这可以通过在身体表面的单个位置进行时间序列测量来观察到。这些水动力尾流特征用于构建尾流分类库，然后用于对水动力信号测量中的未知尾流进行分类。在理想设置下，大多数性能研究发现唤醒检测协议的准确率超过 95%。此外，适当的调谐可以在低信噪比环境中实现 80% 或更高的准确率。因此，外来尾流检测被证明是一个可行的概念，表明此类技术有潜力成为未来多模型传感和运动控制策略的关键推动者。
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.