Title :
Robot inverse kinematics: a modular neural network approach
Author :
Alsina, Pablo J. ; Gehlot, Narpat S.
Author_Institution :
Dept. de Fisic, Univ. Estadual da Paraiba, Campina Grande, Brazil
Abstract :
In this paper, a new method, based on modular neural networks, for the inverse kinematics of robotic manipulators is proposed. Neural modules are assigned to each link in order to realize its own inverse kinematics. The inverse neural modules are concatenated in a global scheme for the updating of the inverse kinematics of the manipulator. Three learning strategies are proposed for the inverse modular scheme. Simulation results for a 3 DOF manipulator and for a 4 DOF SCARA robot are presented. The training scheme, based on a simple training set is discussed
Keywords :
inverse problems; manipulator kinematics; neural nets; SCARA robot; degrees of freedom; inverse kinematics; learning strategies; modular neural network; robotic manipulator; simulation; training set; Closed-form solution; Concatenated codes; Fuzzy logic; Kinematics; Manipulators; Motion planning; Neural networks; Orbital robotics; Robot control; Service robots;
Conference_Titel :
Circuits and Systems, 1995., Proceedings., Proceedings of the 38th Midwest Symposium on
Conference_Location :
Rio de Janeiro
Print_ISBN :
0-7803-2972-4
DOI :
10.1109/MWSCAS.1995.510169
