Preprint Article Version 1 This version is not peer-reviewed

Scalable O(log2n) Dynamics Control for Soft Exoskeletons

Version 1 : Received: 26 September 2024 / Approved: 27 September 2024 / Online: 29 September 2024 (03:40:42 CEST)

How to cite: Colorado, J. D.; Mendez, D.; Gomez-Bautista, A.; Bermeo, J. E.; Alvarado-Rojas, C.; Cuellar, F. Scalable O(log2n) Dynamics Control for Soft Exoskeletons. Preprints 2024, 2024092214. https://doi.org/10.20944/preprints202409.2214.v1 Colorado, J. D.; Mendez, D.; Gomez-Bautista, A.; Bermeo, J. E.; Alvarado-Rojas, C.; Cuellar, F. Scalable O(log2n) Dynamics Control for Soft Exoskeletons. Preprints 2024, 2024092214. https://doi.org/10.20944/preprints202409.2214.v1

Abstract

Robotic exoskeletons are being actively applied to support the activities of daily living (ADL) for patients with hand motion impairments. In terms of actuation, soft materials and sensors have opened new alternatives upon conventional rigid body structures. In this arena, biomimetic soft systems are playing an important role for modeling and controlling the human hand kinematics without the restrictions of rigid mechanical joints, while having an entire deformable body with limitless points of actuation. In this paper, we address the computational limitations for modeling large-scale articulated systems for soft robotic exoskeletons, by integrating a parallel architecture to compute the exoskeleton’s dynamics equations of motion (EoM), obtaining a computation with O(log2n) complexity for the highly-articulated n degrees of freedom.

Keywords

soft-exoskeletons; parallel computing; embedded systems; HIL; dynamics contro

Subject

Engineering, Bioengineering

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