Abstract
This study addresses the problem of nonlinear error predictive compensation to achieve high positioning accuracy for advanced industrial applications. An improved calibration method based on the generalisation performance evaluation is proposed to enhance the stability and accuracy of robot calibration. With the development of technology, a deep neural network (DNN) optimised by a genetic algorithm (GA) is applied to predict the nonlinear error of the calibrated robot. To address the change of external payload, an extra compliance error model is established with a linear piecewise method. A global compensation method combining the GA-DNN nonlinear regression prediction model and the compliance error model is then proposed to achieve the robot’s high-precision positioning performance under any external payload. Experimental results obtained on a Staubli RX160L robot with a FARO laser tracker are introduced to demonstrate the effectiveness and benefits of our proposed methodology. The enhanced positioning accuracy can reach 0.22 mm with 98% probability (i.e., the maximum positioning error in all test data).
Highlights
IntroductionWith the emergence and subsequent development of digital processing and intelligent manufacturing, the breadth and depth of industrial robotic applications (i.e., application fields and task requirements) are increasing [1]
With the emergence and subsequent development of digital processing and intelligent manufacturing, the breadth and depth of industrial robotic applications are increasing [1]
Given that the calibrated robot’s residual errors generally have strong nonlinearity, a deep neural network (DNN) nonlinear regression prediction method based on the genetic algorithm (GA) optimisation of the architecture is developed as an effective solution for achieving the high-performance compensation of the robot’s nonlinear errors
Summary
With the emergence and subsequent development of digital processing and intelligent manufacturing, the breadth and depth of industrial robotic applications (i.e., application fields and task requirements) are increasing [1]. To meet the high-precision requirements of complex tasks, the absolute positioning accuracy of robots is important [2,3,4]. Robot calibration and error compensation are two important ways to improve the absolute positioning accuracy of robots, which have been studied by several scholars [5,6,7,8]. All the above calibration methods need the error model, which is based on the structural parameters of robots. Such a model contains a complex modelling process and identification steps. Zeng et al [24] proposed an error compensation method with error similarity analysis based on the semivariogram function to improve the absolute positional accuracy of industrial robots. The interpolation compensation method has its disadvantages and limitations
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
