Experimental Characterization and Modeling of the Self-Sensing Property in Compliant Twisted String Actuators

Abstract

Twisted string actuators (TSAs) have exhibited great promise in robotic applications by generating high translational force with low input torque. Despite great success, it remains a challenge to reliably estimate the strain of TSAs using compact solutions while maintaining actuator compliance. The inclusion of position sensors not only increases system complexity but also decreases system compliance, a property often crucial in soft robots. We recently constructed a compliant TSA with self-sensing capability by adopting conductive and stretchable super-coiled polymer (SCP) strings; however, only quasi-static measurements of the strain-resistance correlation were obtained. This study proposes a strategy to experimentally characterize and model the transient self-sensing property in compliant TSAs. The correlation between resistance and strain is characterized under different motor twisting sequences and step durations, and exhibited transient decay, hysteresis, and creep. A self-sensing model that consists of a log-based nonlinear term, a rate-dependent Prandtl-Ishlinskii hysteresis term, and a creep term is proposed for the compliant TSAs. Experimental results confirm the high effectiveness of the proposed self-sensing approach, with the average model validation error less than 0.036 cm.