Inverse kinematics solution of robot manipulator end-effector position using multi-neural networks
Joint Authors
Hamidi, Amjad Jalil
Karim, Azad Rahim
Rahim, Firas Abd al-Razzaq
Source
Engineering and Technology Journal
Issue
Vol. 34, Issue 7A (31 Jul. 2016), pp.1360-1368, 9 p.
Publisher
Publication Date
2016-07-31
Country of Publication
Iraq
No. of Pages
9
Main Subjects
Abstract EN
-This paper proposes multi-neural networks structure for solving the inverse kinematic problem of the robot manipulator end-effector position.
It offers an opportunity to reduce substantially the error of the solution.
This error frequently arises when only one neural network is used.
In this structure, each neural network is a multilayer perceptron (MLP) trained by the back propagation algorithm.
The proposed approach verified by including it within an overall Cartesian trajectory planning system.
This structure could produce the robot joint variables that are not included in the training data with an average error ±0.06º, and ±0.15º, ±0.05º for joint angles θ?, θ? and θ? respectively.
From the simulation results, the proposed structure of multineural network has superior performance for modeling the complex robot kinematics
American Psychological Association (APA)
Rahim, Firas Abd al-Razzaq& Karim, Azad Rahim& Hamidi, Amjad Jalil. 2016. Inverse kinematics solution of robot manipulator end-effector position using multi-neural networks. Engineering and Technology Journal،Vol. 34, no. 7A, pp.1360-1368.
https://search.emarefa.net/detail/BIM-700674
Modern Language Association (MLA)
Hamidi, Amjad Jalil…[et al.]. Inverse kinematics solution of robot manipulator end-effector position using multi-neural networks. Engineering and Technology Journal Vol. 34, no. 7A (2016), pp.1360-1368.
https://search.emarefa.net/detail/BIM-700674
American Medical Association (AMA)
Rahim, Firas Abd al-Razzaq& Karim, Azad Rahim& Hamidi, Amjad Jalil. Inverse kinematics solution of robot manipulator end-effector position using multi-neural networks. Engineering and Technology Journal. 2016. Vol. 34, no. 7A, pp.1360-1368.
https://search.emarefa.net/detail/BIM-700674
Data Type
Journal Articles
Language
English
Notes
Includes appendices : p. 1365-1363
Record ID
BIM-700674