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

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.