Kinematic parameter calibration method for industrial robot manipulator using the relative position

2008 ◽  
Vol 22 (6) ◽  
pp. 1084-1090 ◽  
Author(s):  
In-Chul Ha
2013 ◽  
Author(s):  
Shuang Zhao ◽  
Lianqing Zhu ◽  
Qingshan Chen ◽  
Zhikang Pan ◽  
Yangkuan Guo

Robotica ◽  
2021 ◽  
pp. 1-22
Author(s):  
Zhouxiang Jiang ◽  
Min Huang

SUMMARY In typical calibration methods (kinematic or non-kinematic) for serial industrial robot, though measurement instruments with high resolutions are adopted, measurement configurations are optimized, and redundant parameters are eliminated from identification model, calibration accuracy is still limited under measurement noise. This might be because huge gaps still exist among the singular values of typical identification Jacobians, thereby causing the identification models ill conditioned. This paper addresses such problem by using new identification models established in two steps. First, the typical models are divided into the submodels with truncated singular values. In this way, the unknown parameters corresponding to the abnormal singular values are removed, thereby reducing the condition numbers of the new submodels. However, these models might still be ill conditioned. Therefore, the second step is to further centralize the singular values of each submodel by using a matrix balance method. Afterward, all submodels are well conditioned and obtain much higher observability indices compared with those of typical models. Simulation results indicate that significant improvements in the stability of identification results and the identifiability of unknown parameters are acquired by using the new identification submodels. Experimental results indicate that the proposed calibration method increases the identification accuracy without incurring additional hardware setup costs to the typical calibration method.


2021 ◽  
Vol 11 (3) ◽  
pp. 1287
Author(s):  
Tianyan Chen ◽  
Jinsong Lin ◽  
Deyu Wu ◽  
Haibin Wu

Based on the current situation of high precision and comparatively low APA (absolute positioning accuracy) in industrial robots, a calibration method to enhance the APA of industrial robots is proposed. In view of the "hidden" characteristics of the RBCS (robot base coordinate system) and the FCS (flange coordinate system) in the measurement process, a comparatively general measurement and calibration method of the RBCS and the FCS is proposed, and the source of the robot terminal position error is classified into three aspects: positioning error of industrial RBCS, kinematics parameter error of manipulator, and positioning error of industrial robot end FCS. The robot position error model is established, and the relation equation of the robot end position error and the industrial robot model parameter error is deduced. By solving the equation, the parameter error identification and the supplementary results are obtained, and the method of compensating the error by using the robot joint angle is realized. The Leica laser tracker is used to verify the calibration method on ABB IRB120 industrial robot. The experimental results show that the calibration method can effectively enhance the APA of the robot.


Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yuxiang Wang ◽  
Zhangwei Chen ◽  
Hongfei Zu ◽  
Xiang Zhang ◽  
Chentao Mao ◽  
...  

The positioning accuracy of a robot is of great significance in advanced robotic manufacturing systems. This paper proposes a novel calibration method for improving robot positioning accuracy. First of all, geometric parameters are identified on the basis of the product of exponentials (POE) formula. The errors of the reduction ratio and the coupling ratio are identified at the same time. Then, joint stiffness identification is carried out by adding a load to the end-effector. Finally, residual errors caused by nongeometric parameters are compensated by a multilayer perceptron neural network (MLPNN) based on beetle swarm optimization algorithm. The calibration is implemented on a SIASUN SR210D robot manipulator. Results show that the proposed method possesses better performance in terms of faster convergence and higher precision.


2021 ◽  
Vol 11 (13) ◽  
pp. 5914
Author(s):  
Daniel Reyes-Uquillas ◽  
Tesheng Hsiao

In this article, we aim to achieve manual guidance of a robot manipulator to perform tasks that require strict path following and would benefit from collaboration with a human to guide the motion. The robot can be used as a tool to increase the accuracy of a human operator while remaining compliant with the human instructions. We propose a dual-loop control structure where the outer admittance control loop allows the robot to be compliant along a path considering the projection of the external force to the tangential-normal-binormal (TNB) frame associated with the path. The inner motion control loop is designed based on a modified sliding mode control (SMC) law. We evaluate the system behavior to forces applied from different directions to the end-effector of a 6-DOF industrial robot in a linear motion test. Next, a second test using a 3D path as a tracking task is conducted, where we specify three interaction types: free motion (FM), force-applied motion (FAM), and combined motion with virtual forces (CVF). Results show that the difference of root mean square error (RMSE) among the cases is less than 0.1 mm, which proves the feasibility of applying this method for various path-tracking applications in compliant human–robot collaboration.


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