scholarly journals Joint Stiffness Identification and Deformation Compensation of Serial Robots Based on Dual Quaternion Algebra

2018 ◽  
Vol 9 (1) ◽  
pp. 65 ◽  
Author(s):  
Guozhi Li ◽  
Fuhai Zhang ◽  
Yili Fu ◽  
Shuguo Wang
Keyword(s):  
Quaternion Algebra ◽  
Industrial Robot ◽  
Joint Stiffness ◽  
Industrial Robots ◽  
Identification Algorithm ◽  
Dual Quaternion ◽  
Compensation Algorithm ◽  
Serial Robots ◽  
Stiffness Identification ◽  
Deformation Compensation

As the application of industrial robots is limited by low stiffness that causes low precision, a joint stiffness identification algorithm for serial robots is presented. In addition, a deformation compensation algorithm is proposed for the accuracy improvement. Both of these algorithms are formulated by dual quaternion algebra, which offers a compact, efficient, and singularity-free way in robot analysis. The joint stiffness identification algorithm is derived from stiffness modeling, which is the combination of the principle of virtual work and dual quaternion algebra. To validate the effectiveness of the proposed identification algorithm and deformation compensation algorithm, an experiment was conducted on a dual arm industrial robot SDA5F. The robot performed a drilling operation during the experiment, and the forces and torques that acted on the end-effector (EE) of both arms were measured in order to apply the deformation compensation algorithm. The results of the experiment show that the proposed identification algorithm is able to identify the joint stiffness parameters of serial industrial robots, and the deformation compensation algorithm can improve the accuracy of the position and orientation of the EE. Furthermore, the performance of the forces and torques that acted on the EE during the operation were improved as well.

Joint Stiffness Identification and Flexibility Compensation of Articulated Industrial Robot

2013 ◽  
Vol 336-338 ◽  
pp. 1047-1052 ◽  
Author(s):  
Ya Lei Feng ◽  
Dao Kui Qu ◽  
Fang Xu ◽  
Hong Guang Wang
Keyword(s):  
Industrial Robot ◽  
Joint Stiffness ◽  
Industrial Robots ◽  
Absolute Position ◽  
Joint Flexibility ◽  
Position Accuracy ◽  
Stiffness Identification ◽  
Articulated Robots

Articulated robots are the most common industrial robots for their large workspace and flexibility. However, the existence of joint flexibility makes those robots difficult to achieve high absolute position accuracy. This paper presents a method to identify the joint stiffness of the robot. The basic idea of that method is to get joint stiffness based on Hooks Law through stepping movement of a single joint and calculating the corresponding joint gravity torque of every step. The practicality of that method is verified by experiment and a plan is carried out to compensate the joint flexibility.

scholarly journals Joint stiffness identification of six-revolute industrial serial robots

2011 ◽  
Vol 27 (4) ◽  
pp. 881-888 ◽  
Author(s):  
Claire Dumas ◽  
Stéphane Caro ◽  
Sébastien Garnier ◽  
Benoît Furet
Keyword(s):  
Joint Stiffness ◽  
Serial Robots ◽  
Stiffness Identification

scholarly journals Dynamic Model Identification for 6-DOF Industrial Robots

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Li Ding ◽  
Hongtao Wu ◽  
Yu Yao ◽  
Yuxuan Yang
Keyword(s):  
Robot Manipulator ◽  
Industrial Robot ◽  
Model Identification ◽  
Friction Model ◽  
Industrial Robots ◽  
Parameters Identification ◽  
Identification Algorithm ◽  
Unknown Parameters ◽  
Joint Friction ◽  
Dynamical Parameters

A complete and systematic procedure for the dynamical parameters identification of industrial robot manipulator is presented. The system model of robot including joint friction model is linear with respect to the dynamical parameters. Identification experiments are carried out for a 6-degree-of-freedom (DOF) ER-16 robot. Relevant data is sampled while the robot is tracking optimal trajectories that excite the system. The artificial bee colony algorithm is introduced to estimate the unknown parameters. And we validate the dynamical model according to torque prediction accuracy. All the results are presented to demonstrate the efficiency of our proposed identification algorithm and the accuracy of the identified robot model.

scholarly journals Joint stiffness identification of industrial serial robots

Robotica ◽  
2011 ◽  
Vol 30 (4) ◽  
pp. 649-659 ◽  
Author(s):  
Claire Dumas ◽  
Stéphane Caro ◽  
Mehdi Cherif ◽  
Sébastien Garnier ◽  
Benoît Furet
Keyword(s):  
Measurement Errors ◽  
Joint Stiffness ◽  
Experimental Tests ◽  
End Effector ◽  
Identification Method ◽  
Serial Robots ◽  
Stiffness Identification ◽  
Actuated Joints

SUMMARYThis paper presents a new methodology for the joint stiffness identification of industrial serial robots and as consequence for the evaluation of both translational and rotational displacements of the robot's end-effector subject to an external wrench (force and torque). In this paper, the robot's links are supposed to be quite stiffer than the actuated joints as it is usually the case with industrial serial robots. The robustness of the identification method and the sensitivity of the results to measurement errors, and the number of experimental tests are also analyzed. The Kuka KR240-2 robot is used as an illustrative example throughout the paper.

scholarly journals Modeling and Identification of an Industrial Robot with a Selective Modal Approach

2020 ◽  
Vol 10 (13) ◽  
pp. 4619 ◽  
Author(s):  
Matteo Bottin ◽  
Silvio Cocuzza ◽  
Nicola Comand ◽  
Alberto Doria
Keyword(s):  
Dynamic Model ◽  
Material Removal ◽  
Industrial Robot ◽  
Joint Stiffness ◽  
Industrial Applications ◽  
Industrial Robots ◽  
Robotic Assembly ◽  
Machined Surface ◽  
Modal Approach ◽  
Removal Processes

The stiffness properties of industrial robots are very important for many industrial applications, such as automatic robotic assembly and material removal processes (e.g., machining and deburring). On the one hand, in robotic assembly, joint compliance can be useful for compensating dimensional errors in the parts to be assembled; on the other hand, in material removal processes, a high Cartesian stiffness of the end-effector is required. Moreover, low frequency chatter vibrations can be induced when low-stiffness robots are used, with an impairment in the quality of the machined surface. In this paper, a compliant joint dynamic model of an industrial robot has been developed, in which joint stiffness has been experimentally identified using a modal approach. First, a novel method to select the test configurations has been developed, so that in each configuration the mode of vibration that chiefly involves only one joint is excited. Then, experimental tests are carried out in the selected configurations in order to identify joint stiffness. Finally, the developed dynamic model of the robot is used to predict the variation of the natural frequencies in the workspace.

Joint stiffness identification of industrial serial robots using 3D digital image correlation techniques

2021 ◽  
pp. 095440622110028
Author(s):  
Jianping Lin ◽  
Yongji Li ◽  
Yong Xie ◽  
Jiahao Hu ◽  
Junying Min
Keyword(s):  
Digital Image Correlation ◽  
Digital Image ◽  
High Efficiency ◽  
Joint Stiffness ◽  
Industrial Robots ◽  
Image Correlation ◽  
Stiffness Identification ◽  
3D Digital Image Correlation ◽  
External Forces ◽  
Improved Accuracy

Industrial robots have been widely used in manufacturing for advantages of flexibility and high efficiency, while there exists a critical problem of low stiffness. Measuring the stiffnesses of joints accurately have a positive effect on optimizing the stiffness through compensation or posture adjustment. This study proposed a new method for stiffness identification of serial industrial robots using 3D digital image correlation (3D-DIC) techniques, which exhibits high accuracies. External forces are applied to the robot end and its 6-dimensional displacements are recorded with a 3D-DIC system. The values of joint stiffness are evaluated from the data of robot configurations, displacements and forces. The proposed method is implemented on the KUKA KR600-2830 robot experimentally and the average absolute value of relative error is 5.8%, which demonstrates that the proposed method provides much improved accuracy compared to the traditional method.

scholarly journals Estimating Human Wrist Stiffness during a Tooling Task

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3260 ◽  
Author(s):  
Gia-Hoang Phan ◽  
Clint Hansen ◽  
Paolo Tommasino ◽  
Aamani Budhota ◽  
Dhanya Menoth Mohan ◽  
...  
Keyword(s):  
Industrial Robot ◽  
Joint Stiffness ◽  
Muscular Activity ◽  
Mechanical Impedance ◽  
Industrial Robots ◽  
Human Operator ◽  
Stochastic Methods ◽  
Flexion Extension ◽  
Human Operators ◽  
Finishing Tool

In this work, we propose a practical approach to estimate human joint stiffness during tooling tasks for the purpose of programming a robot by demonstration. More specifically, we estimate the stiffness along the wrist radial-ulnar deviation while a human operator performs flexion-extension movements during a polishing task. The joint stiffness information allows to transfer skills from expert human operators to industrial robots. A typical hand-held, abrasive tool used by humans during finishing tasks was instrumented at the handle (through which both robots and humans are attached to the tool) to assess the 3D force/torque interactions between operator and tool during finishing task, as well as the 3D kinematics of the tool itself. Building upon stochastic methods for human arm impedance estimation, the novelty of our approach is that we rely on the natural variability taking place during the multi-passes task itself to estimate (neuro-)mechanical impedance during motion. Our apparatus (hand-held, finishing tool instrumented with motion capture and multi-axis force/torque sensors) and algorithms (for filtering and impedance estimation) were first tested on an impedance-controlled industrial robot carrying out the finishing task of interest, where the impedance could be pre-programmed. We were able to accurately estimate impedance in this case. The same apparatus and algorithms were then applied to the same task performed by a human operators. The stiffness values of the human operator, at different force level, correlated positively with the muscular activity, measured during the same task.

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