Abstract

The broad spread of cooperative robots into many application domains has resulted in a demand for intuitive and effective solutions for teleoperated control. A relevant role in teleoperation has been assumed by impedance controllers, that allow the increase of stability and accuracy during interaction. This paper aims to test a teleoperation method based on an impedance controller, namely tele-impedance control, that is usable in unstructured environments since it relies only on wearable sensors. The proposed solution maps the joint stiffness and position of the human user, computed through six EMG and two M-IMU sensors, into the remote system to be teleoperated. We developed a 2-DoFs virtual task involving virtual physical interactions to compare the performance of our solution with the one of a traditional position-based controller. The study has been conducted on five healthy participants, who experienced both controllers in two different sessions. The tele-impedance approach has proved to be less physically demanding and more intuitive than the position-based one. Experimental data also allow us to investigate the strategy employed by the volunteers in the case of remote interactions, while using the two controllers. Of note, even though with the position controller the variation of subject impedance has no effect on the virtual arm, participants still tend to regulate both impedance and position of their own arm.

Details

Title
Tele-Impedance control of a virtual avatar based on EMG and M-IMU sensors: a proof-of-concept
Author
Buscaglione, Silvia 1 ; Noccaro, Alessia 2 ; Tagliamonte, Nevio 3 ; Ticchiarelli, Giulia 1 ; Di Pino, Giovanni 1 ; Formica, Domenico 4 

 Universitá Campus Bio-Medico di Roma, NeXT Lab: Research Unit of Neurophysiology and Neuroengineering of Human-Technology Interaction, Rome, Italy (GRID:grid.9657.d) (ISNI:0000 0004 1757 5329) 
 Newcastle University, Neurorobotics Lab, Newcastle upon Tyne, UK (GRID:grid.1006.7) (ISNI:0000 0001 0462 7212) 
 Universitá Campus Bio-Medico di Roma, CREO Lab: Research Unit of Advanced Robotics and Human-Centred Technologies, Rome, Italy (GRID:grid.9657.d) (ISNI:0000 0004 1757 5329); Fondazione Santa Lucia, NeuroRobot Lab: Laboratory of Robotic Neurorehabilitation, Rome, Italy (GRID:grid.417778.a) (ISNI:0000 0001 0692 3437) 
 Universitá Campus Bio-Medico di Roma, NeXT Lab: Research Unit of Neurophysiology and Neuroengineering of Human-Technology Interaction, Rome, Italy (GRID:grid.9657.d) (ISNI:0000 0004 1757 5329); Newcastle University, Neurorobotics Lab, Newcastle upon Tyne, UK (GRID:grid.1006.7) (ISNI:0000 0001 0462 7212) 
Pages
18543
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3091023785
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.