Sensing Deformation in Vacuum Driven Foam-Based Actuator via Inductive Method.

porous material proprioceptive artificial muscle robust adaptive control soft sensing vacuum-powered soft actuator

Journal

Frontiers in robotics and AI
ISSN: 2296-9144
Titre abrégé: Front Robot AI
Pays: Switzerland
ID NLM: 101749350

Informations de publication

Date de publication:
2021
Historique:
received: 16 07 2021
accepted: 16 11 2021
medline: 14 12 2021
pubmed: 14 12 2021
entrez: 16 6 2023
Statut: epublish

Résumé

Perception in soft robotics is crucial to allow a safe interaction to effectively explore the environment. Despite the inherent capabilities of soft materials, embedding reliable sensing in soft actuators or robots could introduce constraints in the overall design (e.g., loss of deformability, undesired trajectories, etc.) or reduce their compliant characteristics. Consequently, an adequate stiffness for both sensor and actuator becomes a crucial design parameter. In particular, for sensing the deformation related to actuation motion, sensing and actuating strategies must work in full mechanical synergy. In this view, an inductive sensing solution is presented, exploiting open-cell foam and a copper (Cu) wire in an Inductive Foam Sensor (IFS). Due to entangled air cells high deformability is enabled upon vacuum pressure, and proprioceptive information is provided. The IFS is then successfully integrated into the earlier developed Ultralight Hybrid Pneumatic Artificial Muscle (UH-PAM), which encases an elastomeric bellow skin and plastic rings. Such sensorized UH-PAM (SUH-PAM) is capable of a high contraction ratio (54% upon -80 kPa), while the inductive sensing shows a high sensitivity of 0.01031/1% and a hysteresis of 5.35%, with an average error of 1.85%, respectively. In order to implement a robust feedback control system, an adaptable proportional sliding mode control is presented. As a result, the SUH-PAM motion can be controlled to the mm-scale, with an RMSE of 0.925 mm, and high robustness against disturbances is demonstrated.

Identifiants

pubmed: 37324169
doi: 10.3389/frobt.2021.742885
pii: 742885
pmc: PMC10262191
doi:

Types de publication

Journal Article

Langues

eng

Pagination

742885

Informations de copyright

Copyright © 2021 Joe, Wang, Totaro and Beccai.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Références

Soft Robot. 2019 Dec;6(6):768-777
pubmed: 31373881
PLoS One. 2021 Apr 22;16(4):e0250325
pubmed: 33886654
Soft Robot. 2017 Mar 1;4(1):23-32
pubmed: 28289573
Soft Robot. 2017 Sep 1;4(3):261-273
pubmed: 29062629
Sci Robot. 2016 Dec 6;1(1):
pubmed: 33157858
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:2115-2119
pubmed: 28268749
Sensors (Basel). 2019 Mar 12;19(5):
pubmed: 30871069
Front Robot AI. 2018 Feb 13;5:2
pubmed: 33500889
Sci Robot. 2017 Aug 30;2(9):
pubmed: 33157853
Soft Robot. 2019 Oct;6(5):671-684
pubmed: 31241408
Soft Robot. 2020 Aug;7(4):462-477
pubmed: 32031920
Soft Robot. 2017 Jun;4(2):117-125
pubmed: 29182091

Auteurs

Seonggun Joe (S)

Soft Biorobotics Perception Lab, Istituto Italiano di Tecnologia (IIT), Genova, GE, Italy.
The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy.

Hongbo Wang (H)

Soft Biorobotics Perception Lab, Istituto Italiano di Tecnologia (IIT), Genova, GE, Italy.

Massimo Totaro (M)

Soft Biorobotics Perception Lab, Istituto Italiano di Tecnologia (IIT), Genova, GE, Italy.

Lucia Beccai (L)

Soft Biorobotics Perception Lab, Istituto Italiano di Tecnologia (IIT), Genova, GE, Italy.

Classifications MeSH