3D‐Printed Soft Proprioceptive Graded Porous Actuators with Strain Estimation by System Identification

Abstract

Integration of both actuation and proprioception into the robot body leads to a single integrated system that can deform and sense. Within this work, liquid rope coiling is used to 3D‐print soft graded porous actuators. By fabricating these actuators from a conductive thermoplastic elastomer, piezoresistive sensing is directly integrated. These sensor‐integrated actuators exhibit nonlinearities and hysteresis in their resistance change. To overcome this challenge, a novel approach that uses identified Wiener–Hammerstein (WH) models is proposed to estimate the strain based on the resistance change. Three actuator types were investigated, namely, a bending actuator, a contractor, and a three degrees of freedom bending segment. By using the design freedom of additive manufacturing to set the porosity, the actuation and sensing behavior of a contracting actuator can be programmed. Furthermore, the WH models can provide strain estimation with on average high fits (83%) and low root mean square (RMS) errors (6%) for all three actuators, which outperformed linear models significantly (76.2/9.4% fit/RMS error). In these results, it is indicated that combining 3D‐printed graded porous structures and system identification can realize sensor‐integrated actuators that can estimate their strain but also tailor their behavior through the porosity.