论文标题
软气动执行器的被动和主动声感应
Passive and Active Acoustic Sensing for Soft Pneumatic Actuators
论文作者
论文摘要
我们提出了一种使用嵌入式麦克风和扬声器来测量不同执行器特性的软气动执行器的感应方法。执行器的物理状态确定声音通过结构传播时的特定调制。使用简单的机器学习，我们创建了一个计算传感器，该传感器会从声音录音中渗透相应的状态。我们在柔软的气动连续性执行器上演示了声传感器，并使用它来测量接触位置，接触力，对象材料，执行器膨胀和执行器温度。我们表明，传感器是可靠的（六个接触位置的平均分类速率为93％），精确（平均空间精度为3.7 mm），并且对诸如背景噪声（例如背景噪声）的常见干扰稳健。最后，我们将不同的声音和学习方法比较，并通过20毫秒的白噪声和支持向量分类器作为传感器模型获得最佳结果。
We propose a sensorization method for soft pneumatic actuators that uses an embedded microphone and speaker to measure different actuator properties. The physical state of the actuator determines the specific modulation of sound as it travels through the structure. Using simple machine learning, we create a computational sensor that infers the corresponding state from sound recordings. We demonstrate the acoustic sensor on a soft pneumatic continuum actuator and use it to measure contact locations, contact forces, object materials, actuator inflation, and actuator temperature. We show that the sensor is reliable (average classification rate for six contact locations of 93%), precise (mean spatial accuracy of 3.7 mm), and robust against common disturbances like background noise. Finally, we compare different sounds and learning methods and achieve best results with 20 ms of white noise and a support vector classifier as the sensor model.
