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Embedding Bifurcations into Pneumatic Artificial Muscle
https://orcid.org/0000-0002-8358-3138
AbstractHarnessing complex body dynamics has long been a challenge in robotics, particularly when dealing with soft dynamics, which exhibit high complexity in interacting with the environment. Recent studies indicate that these dynamics can be used as a computational resource, exemplified by the McKibben pneumatic artificial muscle, a common soft actuator. This study demonstrates that bifurcations, including periodic and chaotic dynamics, can be embedded into the pneumatic artificial muscle, with the entire bifurcation structure using the framework of physical reservoir computing. These results suggest that dynamics not present in training data can be embedded through bifurcation embedment, implying the capability to incorporate various qualitatively different patterns into pneumatic artificial muscle without the need to design and learn all required patterns explicitly. Thus, this study introduces a novel approach to simplify robotic devices and control training by reducing reliance on external pattern generators and the amount and types of training data needed for control.
Embedding Bifurcations into Pneumatic Artificial Muscle
https://orcid.org/0000-0002-8358-3138
AbstractHarnessing complex body dynamics has long been a challenge in robotics, particularly when dealing with soft dynamics, which exhibit high complexity in interacting with the environment. Recent studies indicate that these dynamics can be used as a computational resource, exemplified by the McKibben pneumatic artificial muscle, a common soft actuator. This study demonstrates that bifurcations, including periodic and chaotic dynamics, can be embedded into the pneumatic artificial muscle, with the entire bifurcation structure using the framework of physical reservoir computing. These results suggest that dynamics not present in training data can be embedded through bifurcation embedment, implying the capability to incorporate various qualitatively different patterns into pneumatic artificial muscle without the need to design and learn all required patterns explicitly. Thus, this study introduces a novel approach to simplify robotic devices and control training by reducing reliance on external pattern generators and the amount and types of training data needed for control.
Embedding Bifurcations into Pneumatic Artificial Muscle
https://orcid.org/0000-0002-8358-3138
AbstractHarnessing complex body dynamics has long been a challenge in robotics, particularly when dealing with soft dynamics, which exhibit high complexity in interacting with the environment. Recent studies indicate that these dynamics can be used as a computational resource, exemplified by the McKibben pneumatic artificial muscle, a common soft actuator. This study demonstrates that bifurcations, including periodic and chaotic dynamics, can be embedded into the pneumatic artificial muscle, with the entire bifurcation structure using the framework of physical reservoir computing. These results suggest that dynamics not present in training data can be embedded through bifurcation embedment, implying the capability to incorporate various qualitatively different patterns into pneumatic artificial muscle without the need to design and learn all required patterns explicitly. Thus, this study introduces a novel approach to simplify robotic devices and control training by reducing reliance on external pattern generators and the amount and types of training data needed for control.
Embedding Bifurcations into Pneumatic Artificial Muscle
Advanced Science
Akashi, Nozomi (Autor:in) / Kuniyoshi, Yasuo (Autor:in) / Jo, Taketomo (Autor:in) / Nishida, Mitsuhiro (Autor:in) / Sakurai, Ryo (Autor:in) / Wakao, Yasumichi (Autor:in) / Nakajima, Kohei (Autor:in)
Advanced Science ; 11
01.07.2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Embedding Bifurcations into Pneumatic Artificial Muscle (Adv. Sci. 25/2024)
| Wiley | 2024
Embedding Bifurcations into Pneumatic Artificial Muscle (Adv. Sci. 25/2024)
| Wiley | 2024
Experimental Characterization and Control of Miniaturized Pneumatic Artificial Muscle
| British Library Online Contents | 2014
Axial Mechanical Characteristics Analysis of Elongational Pneumatic Artificial Muscle
| British Library Online Contents | 2013