Open AccessPredicting the Motion of a Robot Manipulator with Unknown Trajectories Based on an Artificial Neural Network
Author(s) -
Sai Hong Tang,
Chun Kit Ang,
Mohd Khairol Anuar Mohd Ariffin,
Syamsiah Mashohor
Publication year - 2014
Publication title -
international journal of advanced robotic systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 46
eISSN - 1729-8814
pISSN - 1729-8806
DOI - 10.5772/59278
Subject(s) - computer science , kinematics , artificial neural network , robustness (evolution) , robot , nonlinear system , robot manipulator , artificial intelligence , motion (physics) , control theory (sociology) , control (management) , biochemistry , chemistry , physics , classical mechanics , quantum mechanics , gene
Mathematically, the motion of a robot manipulator can be computed through the integration of kinematics, dynamics, and trajectories calculations. However, the calculations are complex and only can be applied if the configuration of the robot and the characteristics of the joint trajectories are known. This paper introduces the use of artificial neural networks (ANN) to overcome these shortcomings by solving nonlinear functions and adapting the characteristics of unknown trajectories. A virtual six-degree-of-freedom (DOF) robot manipulator is exploited as an example to show the robustness of the developed ANN topology
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