Learning hydrodynamic signatures through proprioceptive sensing by bioinspired swimmers
Learning hydrodynamic signatures through proprioceptive sensing by bioinspired swimmers
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DOI：
10.1088/1748-3190/abd044
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发表时间：
2020-12
影响因子：
3.4
通讯作者：
B. Pollard;Phanindra Tallapragada
中科院分区：
文献类型：
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作者：
B. Pollard;Phanindra Tallapragada
在水中移动的物体或溪流中静止的物体会产生涡流。这样的涡流尾流编码关于物体和流动条件的信息。水下机器人通常具有有限的传感能力，可以从涡流尾流中提取这些信息。许多种类的鱼正是这样做的，通过使用它们的侧线作为其多模态感知的一部分来感知流动特征。为了在机器人中复制这样的能力，大量的研究致力于开发可以放置在机器人表面上以检测压力和速度梯度的人工侧线传感器。在本文中，我们提出了另一种观点的体现感测，运动学的游泳者的身体在响应的流体动力学强迫的涡流尾流可以编码有关的信息。在这里，我们表明，使用人工神经网络，以角速度的身体作为输入，鱼一样的游泳者可以被训练来标记涡尾流是其他移动的身体的流体动力学特征，从而获得一种能力，以“盲目”识别它们。
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.