Towards Sensorless Configuration Estimation in Multi-DoF Soft Robotic Structures Driven by Rolled Dielectric Elastomer Actuators

Abstract

Soft robots have the potential to overcome some of the limitations of conventional, rigid manipulators, especially in those applications where safe interaction and high flexibility are required. One critical issue concerns the need for estimating the position, the configuration, or even the shape of the soft robot without introducing bulky external sensors. Soft robots based on dielectric elastomers (DEs) offer a potential solution to this problem, due to their self-sensing feature. Thanks to self-sensing, the information on DE displacement can be estimated online via electrical measurements, performed simultaneously with high-voltage actuation, and used to potentially reconstruct the full configuration of the robot. This work presents a first investigation towards this system-level self-sensing concept, by choosing a bendable soft robotic module driven by rolled DE actuators as case study. An extended Kalman filter is implemented based on a physical model, and used to estimate the full module configuration via DE-level information only, i.e., the actuators lengths available through simple electrical measurements. The effectiveness of the architecture is evaluated by means of camera-based experiments, showing that an accurate and robust estimation of the system configuration variables can be effectively achieved.