Analysis and grasp strategy modeling for underactuated multi-fingered robot hand

Abstract

A survey for grasping synthesis method with dexterous robot hand is presented in this paper. The difference of grasping characters is introduced between dexterous hand and underactuated hand. Especially the feature of self-adaptive enveloping grasp by underactuated finger mechanism is outlined as having good performance in grasping unknown objects. In order to generate valid grasps for unknown target objects and apply in real-time control system for underactuated robot hand, a grasping strategy for universal grasp tasks is proposed as based on human knowledge analysis. It is composed by off-line neural networks training section and on-line compute section. Firstly, daily grasped objects are used to build a sample space from human experience. Then, the discrete sample space is computed by a fuzzy clustering method. Finally, the data are used to generate grasp decision scheme by rough set mixed artificial neural networks. The choices of grasp configurations for the underactuated robot hand are simulated for with the aim to show the practical feasibility of the proposed modeling method.