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Modular one-to-many clutchable actuator for a soft elbow exosuit

  • M. Canesi
  • , M. Xiloyannis
  • , A. Ajoudani
  • , A. Biechi
  • , L. Masia
  • University of Pisa
  • Robotics Research Centre
  • Istituto Italiano di Tecnologia

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

15 Zitate (Scopus)

Abstract

Exoskeletons have been developed for a wide range of applications, from the military to the medical field, with the aim of augmenting human performance or compensating for neuromuscular deficiencies. However, to empower the high number of degrees of freedom of the human body, they often employ a high number of motors, increasing the size, weight and power consumption of the system. We hereby present an actuation strategy to empower our elbow exosuit that adopts a single motor to drive multiple, independently actuated, degrees of freedom. This paradigm, known as One-to-many, is achieved using a modular design where each module comprises a clutchable mechanism that allows to convert a single directional motion from the prime mover to a selectable bidirectional output. Moreover, the mechanism has a locking feature that enables the wearer of the exoskeleton to hold a static load with a minimal power consumption. We present a simple controller for the clutchable unit based on a finite-state machine model, and evaluate its performance for varying input velocities. The system is shown to have a bandwidth of 1.5 Hz, sufficient for daily elbow movements, whilst retaining a compact design.

OriginalspracheEnglisch
Titel2017 International Conference on Rehabilitation Robotics, ICORR 2017
Redakteure/-innenArash Ajoudani, Panagiotis Artemiadis, Philipp Beckerle, Giorgio Grioli, Olivier Lambercy, Katja Mombaur, Domen Novak, Georg Rauter, Carlos Rodriguez Guerrero, Gionata Salvietti, Farshid Amirabdollahian, Sivakumar Balasubramanian, Claudio Castellini, Giovanni Di Pino, Zhao Guo, Charmayne Hughes, Fumiya Iida, Tommaso Lenzi, Emanuele Ruffaldi, Fabrizio Sergi, Gim Song Soh, Marco Caimmi, Leonardo Cappello, Raffaella Carloni, Tom Carlson, Maura Casadio, Martina Coscia, Dalia De Santis, Arturo Forner-Cordero, Matthew Howard, Davide Piovesan, Adriano Siqueira, Frank Sup, Masia Lorenzo, Manuel Giuseppe Catalano, Hyunglae Lee, Carlo Menon, Stanisa Raspopovic, Mo Rastgaar, Renaud Ronsse, Edwin van Asseldonk, Bram Vanderborght, Madhusudhan Venkadesan, Matteo Bianchi, David Braun, Sasha Blue Godfrey, Fulvio Mastrogiovanni, Andrew McDaid, Stefano Rossi, Jacopo Zenzeri, Domenico Formica, Nikolaos Karavas, Laura Marchal-Crespo, Kyle B. Reed, Nevio Luigi Tagliamonte, Etienne Burdet, Angelo Basteris, Domenico Campolo, Ashish Deshpande, Venketesh Dubey, Asif Hussain, Vittorio Sanguineti, Ramazan Unal, Glauco Augusto de Paula Caurin, Yasuharu Koike, Stefano Mazzoleni, Hyung-Soon Park, C. David Remy, Ludovic Saint-Bauzel, Nikos Tsagarakis, Jan Veneman, Wenlong Zhang
Herausgeber (Verlag)IEEE Computer Society
Seiten1679-1685
Seitenumfang7
ISBN (elektronisch)9781538622964
DOIs
PublikationsstatusVeröffentlicht - 11 Aug. 2017
Extern publiziertJa
Veranstaltung2017 International Conference on Rehabilitation Robotics, ICORR 2017 - London, Großbritannien/Vereinigtes Königreich
Dauer: 17 Juli 201720 Juli 2017

Publikationsreihe

NameIEEE International Conference on Rehabilitation Robotics
ISSN (Print)1945-7898
ISSN (elektronisch)1945-7901

Konferenz

Konferenz2017 International Conference on Rehabilitation Robotics, ICORR 2017
Land/GebietGroßbritannien/Vereinigtes Königreich
OrtLondon
Zeitraum17/07/1720/07/17

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