Estimating the Shape of Soft Pneumatic Actuators using Active Vibroacoustic Sensing

Abstract

Soft robotic devices, including actuators fabricated from materials with a low modulus of elasticity, such as silicone elastomers, have gained significant interest in recent years. A flexible sensor is a vital component for estimating the conditions of soft actuators, such as shape, and deformation due to contact events. However, it is challenging to develop a flexible sensor with tolerability and versatility for soft actuators. Additionally, when an embedded sensor is employed, the fabrication process becomes complex. Therefore, to have tolerability and to increase versatility, we propose a method for estimating the shape of a soft pneumatic actuator based on vibroacoustic sensing. We employ a data-driven approach by utilizing several machine-learning techniques; hence, the proposed method could be applied to other types of actuators without employing any specific sensor or changing the fabrication process. The convolutional neural network is used as one of the dominant techniques, and huge annotated datasets are required. However, datasets can be obtained automatically using a robotic system, and estimation targets can be changed smoothly by switching datasets. We confirmed the feasibility, and versatility of the proposed method through several evaluation experiments. The bending angle can be estimated in real-time using active vibroacoustic, by emitting sweep signals. The mean error of bending angle estimation was between 5° to 10°. Furthermore, the proposed method is applied to a tensile actuator, and the mean error of the estimated length was approximately 0.21 mm.