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MEMS sensors for assessing flow- related control of an underwater biomimetic robotic stingray

机译：MEMS传感器，用于评估水下仿生机器人黄貂鱼的流量相关控制

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Amajor difference between manmade underwater robotic vehicles (URVs) and undersea animals is the dense arrays of sensors on the body of the latter which enable them to execute extreme control of their limbs and demonstrate super-maneuverability. There is a high demand for miniaturized, lowpowered, lightweight and robust sensors that can perform sensing on URVs to improve their control and maneuverability. In this paper, we present the design, fabrication and experimental testing of two types of microelectromechanical systems (MEMS) sensors that benefit the situational awareness and control of a robotic stingray. The first one is a piezoresistive liquid crystal polymer haircell flow sensor which is employed to determine the velocity of propagation of the stingray. The second one is Pb (Zr0.52Ti0.48)O-3 piezoelectric micro-diaphragm pressure sensor which measures various flapping parameters of the stingray's fins that are key parameters to control the robot locomotion. The polymer flow sensors determine that by increasing the flapping frequency of the fins from 0.5 to 3 Hz the average velocity of the stingray increases from 0.05 to 0.4 BL s(-1), respectively. The role of these sensors in detecting errors in control and functioning of the actuators in performing tasks like flapping at a desired amplitude and frequency, swimming at a desired velocity and direction are quantified. The proposed sensors are also used to provide inputs for a model predictive control which allows the robot to track a desired trajectory. Although a robotic stingray is used as a platform to emphasize the role of theMEMSsensors, the applications can be extended to most URVs.
机译：人造水下机器人车辆（URV）与海底动物之间的主要区别在于，后者体内的传感器阵列密集，这使它们能够对肢体进行极端控制并显示出超机动性。迫切需要能够对URV进行感应以改善其控制和机动性的小型，低功耗，轻便且坚固的传感器。在本文中，我们介绍了两种类型的微机电系统（MEMS）传感器的设计，制造和实验测试，这些传感器有利于机器人黄貂鱼的态势感知和控制。第一个是压阻液晶聚合物毛细胞流量传感器，用于确定黄貂鱼的传播速度。第二个是Pb（Zr0.52Ti0.48）O-3压电微隔膜压力传感器，该传感器测量黄貂鱼鳍的各种拍打参数，这些参数是控制机器人运动的关键参数。聚合物流量传感器确定，通过将翅片的拍打频率从0.5 Hz增加到3 Hz，黄貂鱼的平均速度分别从0.05 BL s（-1）增加到0.4 BL s（-1）。量化了这些传感器在检测执行器的控制和功能错误中的作用，以执行诸如以期望的幅度和频率拍动，以期望的速度和方向游动的任务。所提出的传感器还用于提供模型预测控制的输入，该模型预测控制允许机器人跟踪所需的轨迹。尽管机器人黄貂鱼被用作强调MEMS传感器作用的平台，但其应用范围可以扩展到大多数URV。

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