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

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

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

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