Open AccessDurable Pneumatic Artificial Muscles with Electric Conductivity for Reliable Physical Reservoir Computing
Author(s) -
Ryo Sakurai,
Mitsuhiro Nishida,
Taketomo Jo,
Yasumichi Wakao,
Kohei Nakajima
Publication year - 2022
Publication title -
journal of robotics and mechatronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.257
H-Index - 19
eISSN - 1883-8049
pISSN - 0915-3942
DOI - 10.20965/jrm.2022.p0240
Subject(s) - actuator , soft robotics , reservoir computing , computer science , nonlinear system , soft computing , robotics , durability , control engineering , cyber physical system , artificial intelligence , robot , mechanical engineering , simulation , engineering , artificial neural network , physics , quantum mechanics , database , recurrent neural network , operating system
A McKibben-type pneumatic artificial muscle (PAM) is a soft actuator that is widely used in soft robotics, and it generally exhibits complex material dynamics with nonlinearity and hysteresis. In this letter, we propose an extremely durable PAM containing carbon black aggregates and show that its dynamics can be used as a computational resource based on the framework of physical reservoir computing (PRC). By monitoring the information processing capacity of our PAM, we verified that its computational performance will not degrade even if it is randomly actuated more than one million times, which indicates extreme durability. Furthermore, we demonstrate that the sensing function can be outsourced to the soft material dynamics itself without external sensors based on the framework of PRC. Our study paves the way toward reliable information processing powered by soft material dynamics.