Robotic Forceps without Position Sensors using Visual SLAM

Iwai, Takuya; Kanno, Takahiro; Miyazaki, Toshimasa; Kawase, Toshihiro; Kawashima, Kenji

doi:10.1109/icra.2019.8794321

Cited by 1 publication

(1 citation statement)

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“…[ 26 ] Periodic patterns have been successfully embedded into robotic arms and wearable devices composed of PAMs with PRC closed‐loop control. [ 27 , 28 ] Despite the pioneering efforts mentioned above, the computational capabilities of PAMs and the controls they can perform by using these capabilities have not been comprehensively analyzed to date.…”

Section: Introductionmentioning

confidence: 99%

Embedding Bifurcations into Pneumatic Artificial Muscle

Akashi,

Kuniyoshi,

et al. 2024

Advanced Science

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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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Section: Introductionmentioning

confidence: 99%