Ratchet-integrated pneumatic actuator (RIPA): a large-stroke soft linear actuator inspired by sarcomere muscle contraction.

Bioinspir Biomim

Department of Mechanical Engineering, Institute of Advanced Machines and Design (IAMD), Institute of Engineering Research, Seoul National University, Seoul, 08826, Republic of Korea.

Published: March 2020

Pneumatic artificial muscles (PAMs) have a wide range of robotics applications, especially in soft robots, for their ability to generate linear force and displacement with the soft, lightweight, compact, and safe characteristics as well as high power densities. However, the compressibility of the air causes a spring-like behavior of PAMs, resulting in several common issues of limited stroke, load-dependent stroke lengths, difficulty in maintaining their length against disturbance, and necessity of accurate pressure control system. To address these issues, this study borrows inspiration from a biological soft linear actuator, a muscle, and proposes a ratchet-integrated pneumatic actuator (RIPA). Utilizing two pawls integrated at both ends of a McKibben muscle and a flexible rack inserted in the middle of the muscle, the RIPA achieves a large stroke length by accumulating displacements from multiple small strokes of the McKibben muscle by repeating the cycle of pressurization and depressurization. This cycle mimics the cross-bridge model of a sarcomere, a basic unit of a skeletal muscle, in which a muscle accumulates nanoscale strokes of myosin head motors to generate large strokes. The synergy between a PAM and the inspiration from a sarcomere enabled a large-stroke soft linear actuator that can generate independent strokes from loads. The proposed actuator is not only capable of maintaining its length against unexpected mechanical disturbances but also controllable with a relatively simple system. In this paper, we describe the design of the RIPA and provide analytical models to predict the stroke length and the period per cycle for actuation. We also present experimental results for characterization and comparison with model predictions.

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http://dx.doi.org/10.1088/1748-3190/ab7762DOI Listing

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