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

Claudio Melchiorri,Luigi Biagiotti,Daniele De Gregorio,Roberto Meattini,Gianluca Palli

doi:10.1109/icorr.2019.8779476

Claudio Melchiorri, Luigi Biagiotti + Show 3 more

Open Access

https://doi.org/10.1109/icorr.2019.8779476

Copy DOI

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.

Highlights

The act of correctly grasping objects has been shown over decades to be a very complex task, since many degrees of freedom have to be activated in a simultaneous and coordinated manner, along with learning and percetion skills [1]
A myocontrolled robotic hand consists of the online extraction of control signals from muscle activity measurements in order to regulate the behavior of the grasping device
It has been shown how the providing of an artificial sensory feedback related to the grasp strength plays a role in improving the consistency of the commands generated by means of myocontrol during target grasping tasks [6]

Summary

Introduction

The act of correctly grasping objects has been shown over decades to be a very complex task, since many degrees of freedom have to be activated in a simultaneous and coordinated manner, along with learning and percetion skills [1] In this relation, the human hand presents remarkable capabilities in object manipulation, whereas, in contrast, the control of grasping devices such that to replicate human’s proportional and fine physical interactions with objects is still a big challange in the scientific community [2]. The human hand presents remarkable capabilities in object manipulation, whereas, in contrast, the control of grasping devices such that to replicate human’s proportional and fine physical interactions with objects is still a big challange in the scientific community [2] This is the case of human-robot interface systems.

Objectives

Methods

Results

Conclusion