An Air Recirculation System Based on Bioinspired Soft Re-Air Valve for Highly Efficient Pneumatic Actuation

Sinyoung Lee, Dongun Lee, Dongjun Shin

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Owing to their compliance, lightweight, and high force density characteristics, pneumatic actuation systems have been widely implemented in various soft robots. However, pneumatic actuation systems exhibit low efficiency, poor control performance, and high noise; these make it extremely challenging to widely employ a pneumatic actuation system in mobile robots. To overcome these limitations, many researches were conducted on recycling the compressed air within such systems. However, the proposed approaches do not consider the system efficiency and exhaust performance of pneumatic systems. Therefore, this article proposes a recirculation system using a novel soft re-air valve based on the cardiac structure of fish. In particular, the proposed recirculation system recycles the compressed air to improve the system efficiency and pressurizing performance, and the soft re-air valve simultaneously prevents a decrease in the depressurizing performance. For the validation of the proposed scheme, experiments were conducted to evaluate the system efficiency, control performance, and exhaust noise. In contrast to conventional pneumatic systems, the experimental results revealed that the proposed system increased the overall system efficiency by 47.58%, reduced the position root mean square error by 8.16%, and reduced the exhaust noise by 47.52%.

Original languageEnglish
Pages (from-to)564-576
Number of pages13
JournalSoft Robotics
Volume8
Issue number5
DOIs
Publication statusPublished - 2021 Oct

Bibliographical note

Funding Information:
This work was supported by the Industrial Technology Innovation Program (No. 20007058, Development of safe and comfortable human augmentation hybrid robot suit) funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea). Also, this work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean government (MIST) (No. NRF-2016R1A5A1938472). This research was also supported by the Chung-Ang University Graduate Research Scholarship in 2017.

Publisher Copyright:
© 2020, Mary Ann Liebert, Inc., publishers.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Biophysics
  • Artificial Intelligence

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