Abstract
From the point of view of set theory and mathematics, the relation between the forward kinematics (FK) and the inverse kinematics (IK) can be regarded as a nonlinear mapping between the joint space and the operation space of the robot manipulator. Considering the powerful ability of the artificial neural networks (ANN) to process nonlinear mapping relations, the IK problem can be transformed into the problem of training the weights of ANN. In this work, the solution of the IK of the MOTOMAN manipulator is implemented by using ANN. Because of its local approach ability, the radial basis function (RBF) networks of six inputs and one output are designed. The method avoids the traditional complicated deriving equations procedure and programming. Examples are given to illustrate that RBF networks not only have better computation precision than back propagation (BP) networks but also converge faster than BP networks.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The International Journal of Advanced Manufacturing Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
