Abstract
This paper studies the inverse kinematics of two non-spherical wrist configurations of painting robot. The simplest analytical solution of orthogonal wrist configuration is deduced in this paper for the first time. For the oblique wrist configuration, there is no analytical solution for the configuration. So it is necessary to solve by general method, which cannot achieve high precision and high speed as analytic solution. Two general methods are optimized in this paper. Firstly, the elimination method is optimized to reduce the solving speed to 20% of the original one, and the completeness of the method is supplemented. Based on the Gauss damped least squares method, a new optimization method is proposed to improve the solving speed. The enhanced step length coefficient is introduced to conduct studies with the machine learning correlation method. It has been proved that, on the basis of ensuring the stability of motion, the number of iterations can be effectively reduced and the average number of iterations can be less than 5 times, which can effectively improve the speed of solution. In the simulation and experimental environment, it is verified.
Highlights
Accepted: March 8, 2020Published: April 3, 2020
Radial Basis Function (RBF) Neural Networks (NNs) was employed and Quadratic Programming (QP) method was incorporated in the training algorithm of the NNs
The change of the number of iterations with k can be usually obtained as shown in Fig 5 in the process of solving with Gauss damping least square (GDLS) method
Summary
Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files. The optimized algorithm for inverse kinematics of two painting robots with non-spherical wrist affecting factors of the coefficient, studied the coefficient through machine learning classification and regression methods, and compared the performance of various models. We will introduce robotic products using two configurations, some general new methods and optimization methods in other papers, and machine learning methods for studying the enhanced step length coefficient. The inverse kinematics of the 6R robot manipulator was solved by adopting analytical, geometric, and algebraic methods combined with the Paden Kahan subproblem as well as matrix theory in [17,18,19]. Radial Basis Function (RBF) Neural Networks (NNs) was employed and Quadratic Programming (QP) method was incorporated in the training algorithm of the NNs. Various soft computing methods can achieve accuracy up to the micron level, but the solution speed is still a problem. XBGboost combines all the predictions of a group of weak learners to train a strong learner through additive training strategies
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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.
