Quantitative analysis of the force control capability of standard industrial robot axes

2012 ◽  
Author(s):  
M. Lotz ◽  
M. Pfeffermann ◽  
H. Bruhm ◽  
A. Czinki
Keyword(s):  
Quantitative Analysis ◽  
Force Control ◽  
Industrial Robot ◽  
Control Capability

Robotic Force Control Using Virtual Trajectory Generation

1999 ◽  
Vol 121 (2) ◽  
pp. 320-322
Author(s):  
Timothy S. Fielding ◽  
James K. Mills
Keyword(s):  
Force Control ◽  
Control Algorithm ◽  
Control Method ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Initial Value ◽  
Control Capability ◽  
Six Degree Of Freedom ◽  
Virtual Trajectory ◽  
Contact Force Control

A control method is introduced to permit the retrofit of a large class of commercially available industrial robots with a contact force control capability. In this control algorithm, the outer force loop modifies the desired position input trajectory by solving two sets of differential equations as initial value problems. Experimental results, obtained on a CRS Robotics Corporation A460 six degree of freedom industrial robot, are presented which demonstrate the effectiveness of the proposed approach.

A Polishing Robot Force Control System Based on Time Series Data in Industrial Internet of Things

2021 ◽  
Vol 21 (2) ◽  
pp. 1-22
Author(s):  
Chen Zhang ◽  
Zhuo Tang ◽  
Kenli Li ◽  
Jianzhong Yang ◽  
Li Yang
Keyword(s):  
Time Series ◽  
Control System ◽  
Force Control ◽  
Time Series Data ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Series Data ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Force Control System

Installing a six-dimensional force/torque sensor on an industrial arm for force feedback is a common robotic force control strategy. However, because of the high price of force/torque sensors and the closedness of an industrial robot control system, this method is not convenient for industrial mass production applications. Various types of data generated by industrial robots during the polishing process can be saved, transmitted, and applied, benefiting from the growth of the industrial internet of things (IIoT). Therefore, we propose a constant force control system that combines an industrial robot control system and industrial robot offline programming software for a polishing robot based on IIoT time series data. The system mainly consists of four parts, which can achieve constant force polishing of industrial robots in mass production. (1) Data collection module. Install a six-dimensional force/torque sensor at a manipulator and collect the robot data (current series data, etc.) and sensor data (force/torque series data). (2) Data analysis module. Establish a relationship model based on variant long short-term memory which we propose between current time series data of the polishing manipulator and data of the force sensor. (3) Data prediction module. A large number of sensorless polishing robots of the same type can utilize that model to predict force time series. (4) Trajectory optimization module. The polishing trajectories can be adjusted according to the prediction sequences. The experiments verified that the relational model we proposed has an accurate prediction, small error, and a manipulator taking advantage of this method has a better polishing effect.

A Force Control Joint for Robot–Environment Contact Application

2019 ◽  
Vol 11 (3) ◽  
Author(s):  
Qilong Wang ◽  
Wei Wang ◽  
Xilun Ding ◽  
Chao Yun
Keyword(s):  
Force Control ◽  
Model Simulation ◽  
Industrial Robot ◽  
Artificial Muscle ◽  
Hysteresis Model ◽  
Link Length ◽  
Nonlinear Hysteresis ◽  
The Difference ◽  
Parallel Configuration ◽  
Improved Accuracy

Accurate and robust force control is still a great challenge for robot–environment contact applications, such as in situ repair, polishing, and assembly. To tackle this problem, this paper proposes a force control joint with a parallel configuration, including two identical four-bar linkages driven by linear springs to push up the output end of the joint, and a parallel-connected pneumatic artificial muscle (PAM) to pull down its output end. In the new design, the link length of the linkages will be optimized to make the difference between the profile of the linkage and that of PAM constant within the limits of the joint given the force–displacement profile of PAM at a certain level of its input pressure. Furthermore, PAM's nonlinear hysteresis effect, which is believed to limit the accuracy of the joint's force control, will be represented by a new dynamics model that is to be developed from the classical Bouc–Wen (BW) hysteresis model. Simulation tests are then conducted to reveal that the adoption of the PAM hysteresis model yields improved accuracy of force control, and a series of curve trajectory tracking experiments are performed on a six-joint universal industrial robot to verify that the parallel force control joint is capable to enhance force control accuracy for robot contact applications.

scholarly journals CAD/CAM-based Position/Force Control for a Ball-End Abrasive Tool and Its Application to an Industrial Robot

2008 ◽  
Vol 2 (4) ◽  
pp. 742-752 ◽  
Author(s):  
Fusaomi NAGATA ◽  
Tetsuo HASE ◽  
Zenku HAGA ◽  
Masaaki OMOTO ◽  
Keigo WATANABE
Keyword(s):  
Force Control ◽  
Industrial Robot ◽  
Abrasive Tool ◽  
Cad Cam

scholarly journals Application of Hybrid Compliance/Force Control for Industrial Robot.

1994 ◽  
Vol 12 (6) ◽  
pp. 893-898 ◽  
Author(s):  
Takashi Katsuragawa ◽  
Kiyoshi Ioi ◽  
Nobuyuki Kubota ◽  
Osamu Noro
Keyword(s):  
Force Control ◽  
Industrial Robot

scholarly journals Force Control Improvement in Collaborative Robots through Theory Analysis and Experimental Endorsement

2020 ◽  
Vol 10 (12) ◽  
pp. 4329 ◽  
Author(s):  
Rodrigo Pérez-Ubeda ◽  
Ranko Zotovic-Stanisic ◽  
Santiago C. Gutiérrez
Keyword(s):  
Force Control ◽  
Industrial Robot ◽  
Good Alternative ◽  
Theory Analysis ◽  
Collaborative Robots ◽  
Loop Control ◽  
Robot Controller ◽  
Different Types ◽  
Collaborative Robot ◽  
Jacobian Inverse

Due to the elasticity of their joints, collaborative robots are seldom used in applications with force control. Besides, the industrial robot controllers are closed and do not allow the user to access the motor torques and other parameters, hindering the possibility of carrying out a customized control. A good alternative to achieve a custom force control is sending the output of the force regulator to the robot controller through motion commands (inner/outer loop control). There are different types of motion commands (e.g., position or velocity). They may be implemented in different ways (Jacobian inverse vs. Jacobian transpose), but this information is usually not available for the user. This article is dedicated to the analysis of the effect of different inner loops and their combination with several external controllers. Two of the most determinant factors found are the type of the inner loop and the stiffness matrix. The theoretical deductions have been experimentally verified on a collaborative robot UR3, allowing us to choose the best behaviour in a polishing operation according to pre-established criteria.

Fuzzy adaptive force control of industrial robot manipulators with position servos

Mechatronics ◽  
1995 ◽  
Vol 5 (8) ◽  
pp. 899-918 ◽  
Author(s):  
I.H. Suh ◽  
K.S. Eom ◽  
H.J. Yeo ◽  
S.-R. Ow
Keyword(s):  
Force Control ◽  
Robot Manipulators ◽  
Industrial Robot ◽  
Adaptive Force Control ◽  
Fuzzy Adaptive

Force Control Based on Model Predictive for Lower Limb Rehabilitation Training

2015 ◽  
Vol 738-739 ◽  
pp. 991-994
Author(s):  
Fu Cheng Cao ◽  
Hong Wu Qin
Keyword(s):  
Force Control ◽  
Lower Limb ◽  
Control Method ◽  
Control Strategies ◽  
Industrial Robot ◽  
Control Law ◽  
Rehabilitation Robot ◽  
Patient Centered ◽  
Limb Rehabilitation ◽  
Lower Limb Rehabilitation

Human as a varying dynamic system, the control strategies of human-robot interacts differ significantly from that of conventional industrial robot. Considered the patient-centered exercise regimens, a force control method based predict is presented to control a lower limb rehabilitation robot. The control law is introduced that optimises the the maintained force level and limits excessive forceto injury the subject's lower extremity joints. Simulation results show that the robot could guide thelower limb of subjects to move under predefined model of the external force.

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