3D-printed soft sensors for adaptive sensing with online and offline tunable-stiffness

Abstract

The stiffness of a soft robot with structural cavities can be regulated by controlling the pressure of a fluid to render predictable changes in mechanical properties. When the soft robot interacts with the environment, the mediating fluid can also be considered an inherent information pathway for sensing. This approach to using structural tuning to improve the efficacy of a sensing task with specific states has not yet been well studied. A tunable stiffness soft sensor also renders task-relevant contact dynamics in soft robotic manipulation tasks. This article proposes a type of adaptive soft sensor that can be directly three-dimensional printed and controlled using pneumatic pressure. The tunability of such a sensor helps to adjust the sensing characteristics to better capturing specific tactile features, demonstrated by detecting texture with different frequencies. We present the design, modeling, Finite Element Simulation, and experimental characterization of a single unit of such a tunable stiffness sensor. How the sensing characteristics are affected by adjusting its stiffness is studied in depth. In addition to the tunability, the results show that such types of adaptive sensors exhibit good sensitivity (up to 2.6 KPa/N), high sensor repeatability (average std <0.008 KPa/N), low hysteresis (<6%), and good manufacturing repeatability (average std = 0.0662 KPa/N).