The DEXMART hand: Mechatronic design and experimental evaluation of synergy-based control for human-like grasping. (April 2014)

• Record Type: Journal Article
• Authors: Palli, G.; Melchiorri, C.; Vassura, G.; Scarcia, U.; Moriello, L.; Berselli, G.; Cavallo, A.; De Maria, G.; Natale, C.; Pirozzi, S.; May, C.; Ficuciello, F.; Siciliano, B.
• Is Part Of: International journal of robotics research. Volume 33:Number 5(2014:Apr.)
• Page Start: 799
• Page End: 824
• Publication Date: 2014-04
• Subjects: Robotic hands -- mechatronic design -- tendon transmission -- twisted string -- optoelectronic sensors -- postural synergies -- grasping; Robots -- Periodicals; Robots, Industrial -- Periodicals; 629.89205
• DOI: 10.1177/0278364913519897
• Languages: English
• ISSNs: 0278-3649

Abstract

This paper summarizes recent activities carried out for the development of an innovative anthropomorphic robotic hand called the DEXMART Hand. The main goal of this research is to face the problems that affect current robotic hands by introducing suitable design solutions aimed at achieving simplification and cost reduction while possibly enhancing robustness and performance. While certain aspects of the DEXMART Hand development have been presented in previous papers, this paper is the first to give a comprehensive description of the final hand version and its use to replicate human-like grasping. In this paper, particular emphasis is placed on the kinematics of the fingers and of the thumb, the wrist architecture, the dimensioning of the actuation system, and the final implementation of the position, force and tactile sensors. The paper focuses also on how these solutions have been integrated into the mechanical structure of this innovative robotic hand to enable precise force and displacement control of the whole system. Another important aspect is the lack of suitable control tools that severely limits the development of robotic hand applications. To address this issue, a new method for the observation of human hand behavior during interaction with common day-to-day objects by means of a 3D computer vision system is presented in this work together with a strategy for mapping human hand postures to the robotic hand. A simple control strategy based on postural synergies has been used to reduce the complexity of the grasp planning problem. As a preliminary evaluation of the DEXMART Hand's capabilities, this approach has been adopted in this paper to simplify and speed up the transfer of human actions to the robotic hand, showing its effectiveness in reproducing human-like grasping.