Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object Stabilization

被引:45
|
作者
Deng, Zhen [1 ,2 ]
Jonetzko, Yannick [2 ]
Zhang, Liwei [1 ,2 ]
Zhang, Jianwei [2 ]
机构
[1] Fuzhou Univ, Sch Mech Engn & Automat, Fuzhou 350108, Peoples R China
[2] Univ Hamburg, Dept Informat, TAMS, D-22527 Hamburg, Germany
基金
中国国家自然科学基金; 美国国家科学基金会;
关键词
grasping force control; force estimation; slip detection; multi-fingered robotic hands; MANIPULATION; EXPERIENCE;
D O I
10.3390/s20041050
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Grasping force control is important for multi-fingered robotic hands to stabilize the grasped object. Humans are able to adjust their grasping force and react quickly to instabilities through tactile sensing. However, grasping force control through tactile sensing with robotic hands is still relatively unexplored. In this paper, we make use of tactile sensing for multi-fingered robot hands to adjust the grasping force to stabilize unknown objects without prior knowledge of their shape or physical properties. In particular, an online detection module based on Deep Neural Network (DNN) is designed to detect contact events and object material simultaneously from tactile data. In addition, a force estimation method based on Gaussian Mixture Model (GMM) is proposed to compute the contact information (i.e., contact force and contact location) from tactile data. According to the results of tactile sensing, an object stabilization controller is then employed for a robotic hand to adjust the contact configuration for object stabilization. The spatio-temporal property of tactile data is exploited during tactile sensing. Finally, the effectiveness of the proposed framework is evaluated in a real-world experiment with a five-fingered Shadow Dexterous Hand equipped with BioTac sensors.
引用
收藏
页数:21
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