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Akashi N, Kuniyoshi Y, Jo T, Nishida M, Sakurai R, Wakao Y, Nakajima K. Embedding Bifurcations into Pneumatic Artificial Muscle. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2304402. [PMID: 38639352 DOI: 10.1002/advs.202304402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 03/18/2024] [Indexed: 04/20/2024]
Abstract
Harnessing 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.
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Affiliation(s)
- Nozomi Akashi
- Graduation School of Informatics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japan
| | - Yasuo Kuniyoshi
- Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan
| | - Taketomo Jo
- GX Innovation Technology Development, Bridgestone Corporation, 3-1-1 Kyobashi, Chuo-ku, Tokyo, 104-8340, Japan
| | - Mitsuhiro Nishida
- GX Innovation Technology Development, Bridgestone Corporation, 3-1-1 Kyobashi, Chuo-ku, Tokyo, 104-8340, Japan
| | - Ryo Sakurai
- GX Innovation Technology Development, Bridgestone Corporation, 3-1-1 Kyobashi, Chuo-ku, Tokyo, 104-8340, Japan
| | - Yasumichi Wakao
- GX Innovation Technology Development, Bridgestone Corporation, 3-1-1 Kyobashi, Chuo-ku, Tokyo, 104-8340, Japan
| | - Kohei Nakajima
- Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan
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Aita T, Ando H, Katori Y. Computation harvesting from nature dynamics for predicting wind speed and direction. PLoS One 2023; 18:e0295649. [PMID: 38096140 PMCID: PMC10721085 DOI: 10.1371/journal.pone.0295649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 11/27/2023] [Indexed: 12/17/2023] Open
Abstract
Natural phenomena generate complex dynamics because of nonlinear interactions among their components. The dynamics can be exploited as a kind of computational resource. For example, in the framework of natural computation, various natural phenomena such as quantum mechanics and cellular dynamics are used to realize general purpose calculations or logical operations. In recent years, simple collection of such nature dynamics has become possible in a sensor-rich society. For example, images of plant movement that have been captured indirectly by a surveillance camera can be regarded as sensor outputs reflecting the state of the wind striking the plant. Herein, based on ideas of physical reservoir computing, we present a methodology for wind speed and direction estimation from naturally occurring sensors in movies. Then we demonstrate its effectiveness through experimentation. Specifically using the proposed methodology, we investigate the computational capability of the nature dynamics, revealing its high robustness and generalization performance for computation.
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Affiliation(s)
- Takumi Aita
- Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Japan
| | - Hiroyasu Ando
- Advanced Institute for Materials Research, Tohoku University, Sendai, Japan
| | - Yuichi Katori
- School of Systems Information Science, Future University of Hakodate, Hakodate, Japan
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
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