scholarly journals Grasping Force Control for a Robotic Hand by Slip Detection Using Developed Micro Laser Doppler Velocimeter

Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 326 ◽  
Author(s):  
Nobutomo Morita ◽  
Hirofumi Nogami ◽  
Eiji Higurashi ◽  
Renshi Sawada
Keyword(s):  
Force Control ◽  
Laser Doppler ◽  
Laser Doppler Velocimeter ◽  
Robotic Hand ◽  
Grasping Force ◽  
Slip Detection

scholarly journals Grasping Force Control of Multi-fingered Robot Hand based on Slip Detection Using Tactile Sensor

2007 ◽  
Vol 25 (6) ◽  
pp. 970-978 ◽  
Author(s):  
Daisuke Gunji ◽  
Takuma Araki ◽  
Akio Namiki ◽  
Aiguo Ming ◽  
Makoto Shimojo
Keyword(s):  
Force Control ◽  
Tactile Sensor ◽  
Robot Hand ◽  
Grasping Force ◽  
Slip Detection

scholarly journals 1A2-B07 Grasping Force Control of Multi-fingered Robot Hand based on Slip Detection

2007 ◽  
Vol 2007 (0) ◽  
pp. _1A2-B07_1-_1A2-B07_4
Author(s):  
Daisuke Gunji ◽  
Yoshitomo Mizoguchi ◽  
Aigou Ming ◽  
Akio Namiki ◽  
Masatoshi Ishikawa ◽  
...  
Keyword(s):  
Force Control ◽  
Robot Hand ◽  
Grasping Force ◽  
Slip Detection

Grasping force control of multi-fingered robot hand based on slip detection using tactile sensor

2008 ◽  
Author(s):  
Daisuke Gunji ◽  
Yoshitomo Mizoguchi ◽  
Seiichi Teshigawara ◽  
Aiguo Ming ◽  
Akio Namiki ◽  
...  
Keyword(s):  
Force Control ◽  
Tactile Sensor ◽  
Robot Hand ◽  
Grasping Force ◽  
Slip Detection

Grasping force control of multi-fingered robot hand based on slip detection sing tactile sensor

2008 ◽  
Author(s):  
Daisuke Gunji ◽  
Yoshitomo Mizoguch ◽  
Seiichi Teshigawara ◽  
Aiguo Ming ◽  
Akio Namiki ◽  
...  
Keyword(s):  
Force Control ◽  
Tactile Sensor ◽  
Robot Hand ◽  
Grasping Force ◽  
Slip Detection

A Novel Theory on Grasping Force Control in a Multifingered Robotic Hand

1992 ◽  
Author(s):  
Xiaochun Gao ◽  
Shin-Min Song
Keyword(s):  
Force Control ◽  
Control Method ◽  
Robotic Hand ◽  
Promising Alternative ◽  
Grasping Forces ◽  
Novel Theory ◽  
Small Joint ◽  
Grasping Force ◽  
Compliance Model ◽  
Joint Motions

Abstract Based on inspiration of human grasping activities, a new idea is developed in this paper that grasping forces in a multifingered robotic hand can be regulated and controlled through its compliance by actively coordinating small joint motions in its fingers. According to this idea, a grasping force control model is formulated by means of a compliance model developed by the authors before, and a novel theory is then developed for grasping force control in a multifingered robot hand. The developed theory is expected to lead to a new force control method which could serve as a promising alternative for the active stiffness method. As an application of the developed theory, a two-fingered planar robotic hand is also analyzed, and the simulation results verify the developed theory.

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

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1050 ◽  
Author(s):  
Zhen Deng ◽  
Yannick Jonetzko ◽  
Liwei Zhang ◽  
Jianwei Zhang
Keyword(s):  
Force Control ◽  
Estimation Method ◽  
Gaussian Mixture ◽  
Tactile Sensing ◽  
Robotic Hand ◽  
Force Estimation ◽  
Robotic Hands ◽  
Grasping Force ◽  
Spatio Temporal ◽  
Object Material

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.

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