Sensing and Classification of Ambient Vortex Wake From the Kinematics of a Bioinspired Swimming Robot Using Neural Networks
Sensing and Classification of Ambient Vortex Wake From the Kinematics of a Bioinspired Swimming Robot Using Neural Networks
复制标题
使用神经网络从仿生游泳机器人的运动学中感知和分类环境涡流尾流
DOI：
10.1115/dscc2020-3282
复制
发表时间：
2020
影响因子：
4.8
通讯作者：
Phanindra Tallapragada
中科院分区：
文献类型：
--
作者：
B. Pollard;Phanindra Tallapragada
游泳机器人从周围流体流中的涡流场提取信息的能力具有巨大的实际应用。这种能力可能通过其他传感器增强，可用于检测和避免流体中的障碍物或潜在对手。这使得许多研究人员试图对游泳者表面上的压力场进行采样，从而提取关于流体流动的信息，例如流速和攻角。与此相反，在本文中，我们通过模拟表明，游泳者的运动学信息，特别是它的角速度，可以用来训练神经网络，可以分类周围流体中的涡流尾流。实际上，这对生成环境尾流的主体（或其运动）的类型进行了分类。实际上，物体的角速度可以比物体上的压力分布更精确地测量。我们进一步表明，一个被动的尾巴一样的附属物的游泳者可以分类的涡流场比没有这样的附属物的游泳者具有更高的精度。因此，本文所展示的结果可以在设计具有被动附件的水下机器人具有改进的环境流的传感和分类能力的显着使用。
The ability of a swimming robot to extract information from a vortex field in the ambient fluid flow has immense practical applications. Such capabilities perhaps augmented with other sensors are useful to detect and avoid obstacles or potential adversaries in the fluid. This has led many researchers to attempt to sample the pressure field on the surface of a swimmer and thus extract information about the fluid flow such as the flow velocity and angle of attack. In contrast in this paper, we show through simulations, that the kinematic information of a swimmer, specifically its angular velocity, can be used to train a neural network, that can classify the vortex wake in the surrounding fluid. In effect, this classifies the type of body (or its motion) that generates the ambient wake. In practice, the angular velocity of a body can be measured with much greater accuracy than the pressure distribution on the body. We further show that a swimmer with a passive tail-like appendage can classify the vortex field with greater accuracy than a swimmer without such an appendage. Thus the results demonstrated in this paper can be of significant use in designing aquatic robots with passive appendages with improved capabilities of sensing and classifying the ambient flow.