Grasping force control of a robotic hand based on a torque-velocity transformation using F/T sensors with gravity compensation

Author(s):  
Joonhee Jo ◽  
Sung-Kyun Kim ◽  
Yonghwan Oh ◽  
Sang-Rok Oh
Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 326 ◽  
Author(s):  
Nobutomo Morita ◽  
Hirofumi Nogami ◽  
Eiji Higurashi ◽  
Renshi Sawada

Author(s):  
Xiaochun Gao ◽  
Shin-Min Song

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.


Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1050 ◽  
Author(s):  
Zhen Deng ◽  
Yannick Jonetzko ◽  
Liwei Zhang ◽  
Jianwei Zhang

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.


2017 ◽  
Vol 9 (6) ◽  
Author(s):  
Toshihiro Nishimura ◽  
Yoshinori Fujihira ◽  
Tetsuyou Watanabe

This paper presents a novel fingertip system with a two-layer structure for robotic hands. The outer part of the structure consists of a rubber bag filled with fluid, called the “fluid fingertip,” while the inner part consists of a rigid link mechanism called a “microgripper.” The fingertip thus is a rigid/fluid hybrid system. The fluid fingertip is effective for grasping delicate objects, that is, it can decrease the impulsive force upon contact, and absorb uncertainties in object shapes and contact force. However, it can only apply a small grasping force such that holding a heavy object with a robotic hand with fluid fingertips is difficult. Additionally, contact uncertainties including inaccuracies in the contact position control cannot be avoided. In contrast, rigid fingertips can apply considerable grasping forces and thus grasp heavy objects effectively, although this makes delicate grasping difficult. To maintain the benefits of the fluid fingertip while overcoming its disadvantages, the present study examines passively operable microgripper-embedded fluid fingertips. Our goal is to use the gripper to enhance the positioning accuracy and increase the grasping force by adding geometrical constraints to the existing mechanical constraints. Grasping tests showed that the gripper with the developed fingertips can grasp a wide variety of objects, both fragile and heavy.


2007 ◽  
Vol 25 (6) ◽  
pp. 970-978 ◽  
Author(s):  
Daisuke Gunji ◽  
Takuma Araki ◽  
Akio Namiki ◽  
Aiguo Ming ◽  
Makoto Shimojo

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