Forward kinematics of parallel robot based on neural network Newton-Raphson iterative algorithm

2021 ◽  
Author(s):  
Haiqiang Zhang ◽  
Qing Gao ◽  
Minghui Zhang ◽  
Yan'an Yao
Keyword(s):  
Neural Network ◽  
Iterative Algorithm ◽  
Parallel Robot ◽  
Forward Kinematics ◽  
Newton Raphson

The Research on the Quick Method for 6-DOF Parallel Robot Forward Kinematics

2012 ◽  
Vol 466-467 ◽  
pp. 849-853
Author(s):  
Zhao Yin Zhang
Keyword(s):  
Genetic Algorithm ◽  
Parallel Robot ◽  
High Accuracy ◽  
Point Of View ◽  
Forward Kinematics ◽  
Initial Value ◽  
Quick Method ◽  
Concurrent Control ◽  
Newton Raphson ◽  
Raphson Method

6-DOF parallel robot forward kinematics can be achieved by Newton-Raphson method with more accurancy, but the result depends on the offer of initial value. It can definitely calculate the result by genetic algorithm, however, more evolved algebra is needed to make it more accurate, and sometimes it hardly meets the requirement by concurrent control. This article points to use the result of genetic as the initial value of algorithm, and ultimately make use of iteration to complete the forward kinematics. High accuracy and speed are the main features of this calculation, and another one is interpreting from the implementation point of view, which is very practical and meet the concurrent control through experiment.

Forward Kinematics of Parallel Robot Based on Neuro-Fuzzy System

2013 ◽  
Vol 470 ◽  
pp. 636-643 ◽  
Author(s):  
Xiang Wu ◽  
Zu De Zhou ◽  
Qing Song Ai ◽  
Wei Meng
Keyword(s):  
Neural Network ◽  
Iterative Method ◽  
Fuzzy System ◽  
Fuzzy Theory ◽  
Parallel Robot ◽  
Learning Ability ◽  
Forward Kinematics ◽  
Kinematics Model ◽  
Neuro Fuzzy ◽  
Self Learning

As the structure of parallel robot is special in general mechanical and electrical systems, its forward kinematics needs to be solved by nonlinear equations. In this paper, for the issue that numerical iterative method requires complex mathematical derivation and programming, and is sensitive to the initial value, a Neuro-fuzzy system is proposed for solving forward kinematics model of parallel robot. Meanwhile, inverse kinematics is used for training database, knowledge representation ability of fuzzy theory and self-learning ability of neural network are combined to overcome the shortcomings that neural network cannot express human language and fuzzy system do not have self-learning ability. In addition, training and generation efficiency of the model can also be improved by reducing the input dimension reasonably. Simulation results have been showed that, in the premise of efficiency, accuracy of forward kinematics model using Neuro-fuzzy system is better than Newton-Raphson iterative method, and has better versatility.

scholarly journals Neural Network Solutions for Forward Kinematics Problem of HEXA Parallel Robot

10.5772/5374 ◽  
2008 ◽  
Author(s):  
M. Dehghani ◽  
M. Eghtesad ◽  
A. A. ◽  
A. Khayatian ◽  
M. Ahmadi
Keyword(s):  
Neural Network ◽  
Parallel Robot ◽  
Forward Kinematics
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