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On the computation of the direct kinematics of parallel manipulators using polynomial networks

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3 Author(s)
R. Boudreau ; Ecole de Genie, Moncton Univ., NB, Canada ; S. Darenfed ; C. M. Gosselin

Polynomial learning networks are proposed in this paper to solve the forward kinematic problem for a planar three-degree-of-freedom parallel manipulator with revolute joints. These networks rapidly learn complex nonlinear functions based on a database mapping. The networks learn the forward kinematics of the manipulator based on examples of the transformation. The obtained networks are then used to follow a test trajectory. For comparison purposes, a neural network approach using backpropagation is also used for this problem. The results show that, in this application, polynomial networks learn much faster and exhibit less error than neural networks

Published in:

IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans  (Volume:28 ,  Issue: 2 )