Learning Optimal Fin-Ray Finger Design for Soft Grasping

被引:19
作者
Deng, Zhifeng [1 ]
Li, Miao [1 ,2 ]
机构
[1] Wuhan Univ, Learning Algorithms & Soft Manipulat Lab, Inst Technol Sci, Sch Power & Mech Engn, Wuhan, Peoples R China
[2] Wuhan Cobot Technol, Wuhan, Peoples R China
基金
中国国家自然科学基金;
关键词
soft hands; robotic grasping; soft finger design; grasp quality; grasp quality criterion; ROBOTIC GRIPPER; FABRICATION; KINEMATICS; HAND;
D O I
10.3389/frobt.2020.590076
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
The development of soft hands is an important progress to empower robotic grasping with passive compliance while greatly decreasing the complexity of control. Despite the advances during the past decades, it is still not clear how to design optimal hands or fingers given the task requirements. In this paper, we propose a framework to learn the optimal design parameter for a fin-ray finger in order to achieve stable grasping. First, the pseudo-kinematics of the soft finger is learned in simulation. Second, the task constraints are encoded as a combination of desired grasping force and the empirical grasping quality function in terms of winding number. Finally, the effectiveness of the proposed approach is validated with experiments in simulation and using real-world examples as well.
引用
收藏
页数:8
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