Nowadays, electric-powered hand prostheses do not provide adequate sensory instrumentation and artificial feedback to allow users voluntarily and finely modulate the grasp strength applied to the objects. In this work, the design of a control architecture for a myocontrol-based regulation of the grasp strength for a robotic hand equipped with contact force sensors is presented. The goal of the study was to provide the user with the capability of modulating the grasping force according to target required levels by exploiting a vibrotactile feedback. In particular, the whole human-robot control system is concerned (i.e. myocontrol, robotic hand controller, vibrotactile feedback.) In order to evaluate the intuitiveness and force tracking performance provided by the proposed control architecture, an experiment was carried out involving four naïve able-bodied subjects in a grasping strength regulation task with a myocontrolled robotic hand (the University of Bologna Hand), requiring for grasping different objects with specific target force levels. The reported results show that the control architecture successfully allowed all subjects to achieve all grasping strength levels exploiting the vibrotactile feedback information. This preliminary demonstrates that, potentially, the proposed control interface can be profitably exploited in upper-limb prosthetic applications, as well as for non-rehabilitation uses, e.g. in ultra-light teleoperation for grasping devices.

A Control Architecture for Grasp Strength Regulation in Myocontrolled Robotic Hands Using Vibrotactile Feedback: Preliminary Results / Meattini, Roberto; Biagiotti, Luigi; Palli, Gianluca; De Gregorio, Daniele; Melchiorri, Claudio. - 2019:(2019), pp. 1272-1277. ((Intervento presentato al convegno 16th IEEE International Conference on Rehabilitation Robotics, ICORR 2019 tenutosi a Toronto, Ontario, Canada nel June 24–28, 2019 [10.1109/ICORR.2019.8779476].

A Control Architecture for Grasp Strength Regulation in Myocontrolled Robotic Hands Using Vibrotactile Feedback: Preliminary Results

Biagiotti, Luigi;Palli, Gianluca;Melchiorri, Claudio
2019

Abstract

Nowadays, electric-powered hand prostheses do not provide adequate sensory instrumentation and artificial feedback to allow users voluntarily and finely modulate the grasp strength applied to the objects. In this work, the design of a control architecture for a myocontrol-based regulation of the grasp strength for a robotic hand equipped with contact force sensors is presented. The goal of the study was to provide the user with the capability of modulating the grasping force according to target required levels by exploiting a vibrotactile feedback. In particular, the whole human-robot control system is concerned (i.e. myocontrol, robotic hand controller, vibrotactile feedback.) In order to evaluate the intuitiveness and force tracking performance provided by the proposed control architecture, an experiment was carried out involving four naïve able-bodied subjects in a grasping strength regulation task with a myocontrolled robotic hand (the University of Bologna Hand), requiring for grasping different objects with specific target force levels. The reported results show that the control architecture successfully allowed all subjects to achieve all grasping strength levels exploiting the vibrotactile feedback information. This preliminary demonstrates that, potentially, the proposed control interface can be profitably exploited in upper-limb prosthetic applications, as well as for non-rehabilitation uses, e.g. in ultra-light teleoperation for grasping devices.
16th IEEE International Conference on Rehabilitation Robotics, ICORR 2019
Toronto, Ontario, Canada
June 24–28, 2019
2019
1272
1277
Meattini, Roberto; Biagiotti, Luigi; Palli, Gianluca; De Gregorio, Daniele; Melchiorri, Claudio
A Control Architecture for Grasp Strength Regulation in Myocontrolled Robotic Hands Using Vibrotactile Feedback: Preliminary Results / Meattini, Roberto; Biagiotti, Luigi; Palli, Gianluca; De Gregorio, Daniele; Melchiorri, Claudio. - 2019:(2019), pp. 1272-1277. ((Intervento presentato al convegno 16th IEEE International Conference on Rehabilitation Robotics, ICORR 2019 tenutosi a Toronto, Ontario, Canada nel June 24–28, 2019 [10.1109/ICORR.2019.8779476].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1179763
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