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.

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 call

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.