Power-Efficient Soft Pneumatic Actuator Using Spring-Frame Collateral Compression Mechanism

Sungjun Kim, Seung Ryeol Lee, Sinyoung Lee, Dongun Lee, Dongjun Shin

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


With the ongoing research on soft robots, the performance of soft actuators needs to be enhanced for more wide robotic applications. Currently, most soft robots based on pneumatic ac-tuation are capable of assisting small systems, but they are not fully suited for supporting joints requiring large force and range of motion. This is due to the actuation characteristics of the pneumatic artificial muscle (PAM); they are relatively slow, inefficient, and experience a significant force reduc-tion when the PAM contracts. Hence, we propose a novel PAM based on a spring-frame collateral compression mechanism. With only a single compressed air source, the external mesh-covered and inner spring-frame actuators of the proposed PAM operate simultaneously to generate considerable force. Additionally, the design of the internal actuator within the void space of PAM reduces the air consumption and consequently improves the actuator’s operating speed and efficiency. We experimentally confirmed that the proposed PAM exhibited 31.2% greater force, was 25.6% faster, and consumed 21.5% lower air compared to the conventional McKibben muscles. The performance en-hancement of the proposed PAM improves the performance of soft robots, allowing the development of more compact robots with greater assistive range.

Original languageEnglish
Article number76
Issue number3
Publication statusPublished - 2022 Mar

Bibliographical note

Funding Information:
Funding: This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean government (MIST) (NRF-2016R1A5A1938472), in part 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 & Energy (MOTIE, Korea), and in part by the Chung-Ang University Research Scholarship Grants in 2020.

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Control and Optimization


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