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Titel: Sensorless Proprioception in Multi-DoF Dielectric Elastomer Soft Robots via System-Level Self-Sensing
VerfasserIn: Prechtl, Johannes
Baltes, Matthias
Flaßkamp, Kathrin
Rizzello, Gianluca
Sprache: Englisch
Titel: IEEE ASME transactions on mechatronics : a joint publication of the IEEE Industrial Electronics Society, the IEEE Robotics and Automation Society and the ASME Dynamic Systems and Control Division
Seiten: 1-12
Verlag/Plattform: IEEE
Erscheinungsjahr: 2024
Freie Schlagwörter: Robot sensing systems
Soft robotics
Voltage measurement
Actuators
Robots
Bending
Real-time systems
DDC-Sachgruppe: 620 Ingenieurwissenschaften und Maschinenbau
Dokumenttyp: Journalartikel / Zeitschriftenartikel
Abstract: Proprioception in soft robots remains an ongoing challenge, owing to practical issues such as tight sensor integration while maintaining mechanical compliance. Soft robots based on dielectric elastomer (DE) technology could provide a compelling answer to these challenges, due to the inherent flexibility and compliance of such transducers as well as their ability to simultaneously work as actuators and sensors, i.e., self-sensing. In this work, we propose a novel real-time self-sensing scheme for DE soft robotic systems. By combining an actuator-level recursive least squares identification with an extended Kalman filter, our architecture provides an estimation of the mechanical state of the structure without requiring additional electro-mechanical sensors, solely relying on electrical measurements performed on the DEs during high voltage actuation. Experimental validation, conducted on a DE soft robot prototype, reveals that the proposed solution reconstructs the system state during actuation in a robust and accurate way, and under various external loading conditions.
DOI der Erstveröffentlichung: 10.1109/TMECH.2024.3375923
URL der Erstveröffentlichung: https://ieeexplore.ieee.org/document/10484978
Link zu diesem Datensatz: urn:nbn:de:bsz:291--ds-436057
hdl:20.500.11880/39070
http://dx.doi.org/10.22028/D291-43605
ISSN: 1941-014X
1083-4435
Datum des Eintrags: 29-Nov-2024
Fakultät: NT - Naturwissenschaftlich- Technische Fakultät
Fachrichtung: NT - Systems Engineering
Professur: NT - Prof. Dr. Stefan Seelecke
Sammlung:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons Creative Commons