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Abstract
The success of grasping and manipulation tasks of commercial prosthetic hands is mainly related to amputee visual feedback since they are not provided either with tactile sensors or with sophisticated control. As a consequence, slippage and object falls often occur. This article wants to address the specific issue of enhancing grasping and manipulation capabilities of existing prosthetic hands, by changing the control strategy. For this purpose, it proposes a multilevel control based on two distinct levels consisting of (I) a policy search learning algorithm combined with central pattern generators in the higher level and (2) a parallel force/position control managing slippage events in the lower level. The control has been tested on an anthropomorphic robotic hand with prosthetic features (the IH2 hand) equipped with force sensors. Bi-digital and tri-digital grasping tasks with and without slip information have been carried out. The KUKA-LWR has been employed to perturb the grasp stability inducing controlled slip events. The acquired data demonstrate that the proposed control has the potential to adapt to changes in the environment and guarantees grasp stability, by avoiding object fall thanks to prompt slippage event detection.
Keywords
Prosthetics, biomechatronics, control, slip prevention, tactile sensors
Date received: 19 February 2016; accepted: 25 July 2016
Academic Editor: Nicolas Garcia-Aracil
(ProQuest: ... denotes formulae omitted.)
Introduction
Commercial prostheses are typically velocity controlled or position controlled; no tactile system is integrated in the hand and the success of the grasp is based on the visual feedback of the amputee.1,2 On the other hand, control solutions of prosthetic hands based on tactile feedback are borrowed from robotics, where the tactile sensing allows endowing the robotic hands with autonomous dexterous manipulation features. In robotic applications, tactile systems are used for objects recognition tasks, control forces, grasp objects, and to servo surfaces.3 The control approaches can control fingers torque, force, velocity, and trajectory and include classical proportional-integral-derivative (PID), adaptive, robust, neural, fuzzy sliding mode, and their combinations.4,5 In addition, a well-consolidated approach to ensure grasp stability relies on the concept of friction cone, thus implying that the ratio between the normal force and the tangential force during grasping, multiplied by the static coefficient of friction, has to exceed 1. This method is very effective; however, it suffers from some limitations...