Adaptive alignment control for a dual-PSD based industrial robot calibration system

Author(s):  
Zhihui Deng ◽  
Yunyi Jia ◽  
Jiatong Bao ◽  
Chengzhi Su ◽  
Yu Cheng ◽  
...  
Author(s):  
Jiabo Zhang ◽  
Xibin Wang ◽  
Ke Wen ◽  
Yinghao Zhou ◽  
Yi Yue ◽  
...  

Purpose The purpose of this study is the presentation and research of a simple and rapid calibration methodology for industrial robot. Extensive research efforts were devoted to meet the requirements of online compensation, closed-loop feedback control and high-precision machining during the flexible machining process of robot for large-scale cabin. Design/methodology/approach A simple and rapid method to design and construct the transformation relation between the base coordinate system of robot and the measurement coordinate system was proposed based on geometric constraint. By establishing the Denavit–Hartenberg model for robot calibration, a method of two-step error for kinematic parameters calibration was put forward, which aided in achievement of step-by-step calibration of angle and distance errors. Furthermore, KUKA robot was considered as the research object, and related experiments were performed based on laser tracker. Findings The experimental results demonstrated that the accuracy of the coordinate transformation could reach 0.128 mm, which meets the transformation requirements. Compared to other methods used in this study, the calibration method of two-step error could significantly improve the positioning accuracy of robot up to 0.271 mm. Originality/value The methodology based on geometric constraint and two-step error is simple and can rapidly calibrate the kinematic parameters of robot. It also leads to the improvement in the positioning accuracy of robot.


2009 ◽  
Vol 3 (2) ◽  
pp. 116-132 ◽  
Author(s):  
Yong Liu ◽  
Ning Xi ◽  
Yantao Shen ◽  
Xiongzi Li ◽  
George Zhang ◽  
...  

2021 ◽  
Vol 33 (1) ◽  
pp. 158-171
Author(s):  
Monica Tiboni ◽  
◽  
Giovanni Legnani ◽  
Nicola Pellegrini

Modeless industrial robot calibration plays an important role in the increasing employment of robots in industry. This approach allows to develop a procedure able to compensate the pose errors without complex parametric model. The paper presents a study aimed at comparing neural-kinematic (N-K) architectures for a modeless non-parametric robotic calibration. A multilayer perceptron feed-forward neural network, trained in a supervised manner with the back-propagation learning technique, is coupled in different modes with the ideal kinematic model of the robot. A comparative performance analysis of different neural-kinematic architectures was executed on a two degrees of freedom SCARA manipulator, for direct and inverse kinematics. Afterward the optimal schemes have been identified and further tested on a three degrees of freedom full SCARA robot and on a Stewart platform. The analysis on simulated data shows that the accuracy of the robot pose can be improved by an order of magnitude after compensation.


Robotica ◽  
2004 ◽  
Vol 22 (5) ◽  
pp. 505-512 ◽  
Author(s):  
Lukas Beyer ◽  
Jens Wulfsberg

The accuracy of pose of industrial robots is often unsatis-factory for advanced applications. Particularly regarding off-line programming, exchangeability and high precision tasks problems may occur which can be very time-consuming and costly to solve. Therefore a calibration system ROSY has been developed in order to increase the accuracy of standard robots and parallel-kinematic structures, like the Tricept robots.


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