Title :
Modeling of robot inverse kinematics using two ANN paradigms
Author :
Yang, S.S. ; Moghavvemi, M. ; Tolman, John D.
Author_Institution :
Dept. of Electr. Eng., Malaya Univ., Kuala Lumpur, Malaysia
Abstract :
The comparison of the performance of two artificial neural network or ANN paradigms trained to learn data obtained from the kinematics model of a UMI RTX robotic arm are presented. Trained ANN simulators were implemented to position the robotic manipulator demonstrating the feasibility of using ANN technology in actual implementation
Keywords :
backpropagation; industrial manipulators; inverse problems; manipulator kinematics; radial basis function networks; ANN paradigms; UMI RTX robotic arm; artificial neural network; backpropagation; industrial robot; kinematics model; performance; radial basis function; robot inverse kinematics; robotic manipulator; trained ANN; Artificial intelligence; Artificial neural networks; Intelligent robots; Intelligent sensors; Inverse problems; Kinematics; Machine intelligence; Path planning; Robot sensing systems; Robotics and automation;
Conference_Titel :
TENCON 2000. Proceedings
Conference_Location :
Kuala Lumpur
Print_ISBN :
0-7803-6355-8
DOI :
10.1109/TENCON.2000.892245
