Grasp configuration planning for a low-cost and easy-operation underactuated three-fingered robot hand

• Published in: Mechanism and machine theory Vol. 129; pp. 51 - 69
• Main Authors: Yao, Shuangji, Ceccarelli, Marco, Carbone, Giuseppe, Dong, Zhikui
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-11-2018
• Subjects: Experimental testing; Grasp configuration planning; Robotic hand; Simulation; Underactuation; Simulation; Experimental testing; Underactuation; Robotic hand; Grasp configuration planning
• ISSN: 0094-114X; 1873-3999
• DOI: 10.1016/j.mechmachtheory.2018.06.019

•We have proposed an algorithm for grasp configuration planning for the three-fingered underactuated robot hand.•The grasp configuration planning algorithm is based on human experience and knowledge by using rough set mixed neural networks.•The shapes and sizes of task objects are described by taxonomy data, and the data are used to generate grasp configurations by well-trained artificial neural networks.•The grasp simulations and experiments for robot hand are carried out to test the performance of the control methods.•The grasp examination experiment with different task objects indicate that the three-fingered underactuated robot hand can realize suitable grasp configuration by the presented grasp configuration planning algorithm.
This paper proposes a method for modeling and planning the grasping configuration of a robotic hand with underactuated finger mechanisms. The proposed modeling algorithm is based on analysis and mimicking of human grasping experience. Results of the analysis is preprocessed and stored in a database. The grasp configuration planning algorithm can be used within a real time online grasp control as based on artificial neural networks. Namely, shapes and sizes of task objects are described by taxonomy data, which are used to generate grasp configurations. Then, a robot hand grasp control system is designed as based on the proposed grasp planning with close-loop position and force feedback. Simulations and experiments are carried out to show the basic features of the proposed formulation for identifying the grasp configurations while dealing with target objects of different shapes and sizes. It is hoped that the well-trained underactuated robot hand can solve most of grasping tasks in our life. The research approach is aimed to research low-cost easy-operation solution for feasible and practical implementation.
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