Experimental Characterization and Modeling of the Self-Sensing Property in Compliant Twisted String Actuators
| Autor: | David Bombara, Revanth Konda, Jun Zhang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Control and Optimization Computer science Mechanical Engineering Biomedical Engineering 02 engineering and technology 021001 nanoscience & nanotechnology Computer Science Applications Term (time) Human-Computer Interaction Nonlinear system Hysteresis 020901 industrial engineering & automation Creep Artificial Intelligence Control and Systems Engineering Control theory C++ string handling Torque Computer Vision and Pattern Recognition Transient (oscillation) 0210 nano-technology Actuator Position sensor |
| Zdroj: | IEEE Robotics and Automation Letters. 6:974-981 |
| ISSN: | 2377-3774 |
| DOI: | 10.1109/lra.2021.3056372 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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