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.

scholarly journals Integrated linkage-driven dexterous anthropomorphic robotic hand

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Uikyum Kim ◽  
Dawoon Jung ◽  
Heeyoen Jeong ◽  
Jongwoo Park ◽  
Hyun-Mok Jung ◽  
...  
Keyword(s):  
Everyday Life ◽  
Tactile Sensing ◽  
Robotic Hand ◽  
Robot Arm ◽  
Robotic Hands ◽  
Robotic Arms ◽  
Low Weight ◽  
Compact Size ◽  
Grasping Force ◽  
High Flexibility

AbstractRobotic hands perform several amazing functions similar to the human hands, thereby offering high flexibility in terms of the tasks performed. However, developing integrated hands without additional actuation parts while maintaining important functions such as human-level dexterity and grasping force is challenging. The actuation parts make it difficult to integrate these hands into existing robotic arms, thus limiting their applicability. Based on a linkage-driven mechanism, an integrated linkage-driven dexterous anthropomorphic robotic hand called ILDA hand, which integrates all the components required for actuation and sensing and possesses high dexterity, is developed. It has the following features: 15-degree-of-freedom (20 joints), a fingertip force of 34N, compact size (maximum length: 218 mm) without additional parts, low weight of 1.1 kg, and tactile sensing capabilities. Actual manipulation tasks involving tools used in everyday life are performed with the hand mounted on a commercial robot arm.

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 Indirect Force Control of a Cable-Driven Parallel Robot: Tension Estimation using Artificial Neural Network trained by Force Sensor Measurements

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2520 ◽  
Author(s):  
Jinlong Piao ◽  
Eui-Sun Kim ◽  
Hongseok Choi ◽  
Chang-Bae Moon ◽  
Eunpyo Choi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Force Control ◽  
Estimation Method ◽  
Parallel Robot ◽  
Force Estimation ◽  
End Effector ◽  
Cable Properties ◽  
Cable Force ◽  
Artificial Neural

In a cable-driven parallel robot (CDPR), force sensors are utilized at each winch motor to measure the cable tension in order to obtain the force distribution at the robot end-effector. However, because of the effects of friction in the pulleys and the unmodeled cable properties of the robot, the measured cable tensions are often inaccurate, which causes force-control difficulties. To overcome this issue, this paper presents an artificial neural network (ANN)-based indirect end-effector force-estimation method, and its application to CDPR force control. The pulley friction and other unmodeled effects are considered as black-box uncertainties, and the tension at the end-effector is estimated by compensating for these uncertainties using an ANN that is developed using the training datasets from CDPR experiments. The estimated cable tensions at the end-effector are used to design a P-controller to track the desired force. The performance of the proposed ANN model is verified through comparisons with the forces measured directly at the end-effector. Furthermore, cable force control is implemented based on the compensated tensions to evaluate the performance of the CDPR in wrench space. The experimental results show that the proposed friction-compensation method is suitable for application in CDPRs to control the cable force.

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.

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