Improved approach to the solution of inverse kinematics problems for robot manipulators
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Date
2000
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Abstract
A structured artificial neural-network (ANN) approach has been proposed here to control the motion of a robot manipulator. Many neural-network models use threshold units with sigmoid transfer functions and gradient descent-type learning rules. The learning equations used are those of the backpropagation algorithm. In this work, the solution of the kinematics of a six-degrees-of-freedom robot manipulator is implemented by using ANN. Work has been undertaken to find the best ANN configurations for this problem. Both the placement and orientation angles of a robot manipulator are used to fin the inverse kinematics solutions.
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Keywords
Backpropagation , Degrees of freedom (mechanics) , Intelligent control , Inverse kinematics , Inverse problems , Learning algorithms , Learning systems , Manipulators , Mathematical models , Motion control , Problem solving , Transfer functions , Structured artificial neural networks , Neural networks