A Novel Soft Pneumatic Artificial Muscle with High-Contraction Ratio

Kwanghyun Han, Nam Ho Kim, Dongjun Shin

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)

Abstract

There is a growing interest in soft actuators for human-friendly robotic applications. However, it is very challenging for conventional soft actuators to achieve both a large working distance and high force. To address this problem, we present a high-contraction ratio pneumatic artificial muscle (HCRPAM), which has a novel actuation concept. The HCRPAM can contract substantially while generating a large force suitable for a wide range of robotic applications. Our proposed prototyping method allows for an easy and quick fabrication, considering various design variables. We derived a mathematical model using a virtual work principle, and validated the model experimentally. We conducted simulations for the design optimization using this model. Our experimental results show that the HCRPAM has a 183.3% larger contraction ratio and 37.1% higher force output than the conventional pneumatic artificial muscle (McKibben muscle). Furthermore, the actuator has a compatible position tracking performance of 1.0 Hz and relatively low hysteresis error of 4.8%. Finally, we discussed the controllable bending characteristics of the HCRPAM, which uses heterogeneous materials and has an asymmetrical structure to make it comfortable for a human to wear.

Original languageEnglish
Pages (from-to)554-566
Number of pages13
JournalSoft Robotics
Volume5
Issue number5
DOIs
Publication statusPublished - 2018 Oct

Bibliographical note

Funding Information:
We acknowledge the assistance provided by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2015R1C1A1A02036412 and No. NRF-2016R1A5A1938472).

Funding Information:
This research was supported by the Chung-Ang Graduate Research Scholarship in 2015.

Publisher Copyright:
© Copyright 2018, Mary Ann Liebert, Inc., publishers 2018.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Biophysics
  • Artificial Intelligence

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