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Inverse Kinematics of Redundant Manipulators Formulated as Quadratic Programming Optimization Problem Solved Using Recurrent Neural Networks: A Review

Published online by Cambridge University Press:  25 November 2019

Ahmed A. Hassan Open the ORCID record for Ahmed A. Hassan [Opens in a new window] ,
Mohamed El-Habrouk  and
Samir Deghedie
Ahmed A. Hassan*
Affiliation:
Department of Electrical Engineering, Faculty of Engineering, Alexandria University, Alexandria, Egypt
Mohamed El-Habrouk
Affiliation:
Department of Electrical Engineering, Faculty of Engineering, Alexandria University, Alexandria, Egypt
Samir Deghedie
Affiliation:
Department of Electrical Engineering, Faculty of Engineering, Alexandria University, Alexandria, Egypt
*
*Corresponding author. E-mail: [email protected]
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Summary

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The Inverse Kinematics (IK) problem of manipulators can be divided into two distinct steps: (1) Problem formulation, where the problem is developed into a form which can then be solved using various methods. (2) Problem solution, where the IK problem is actually solved by producing the values of different joint space variables (joint angles, joint velocities or joint accelerations). The main focus of this paper is concentrated on the discussion of the IK problem of redundant manipulators, formulated as a quadratic programming optimization problem solved by different kinds of recurrent neural networks.

Keywords

Inverse kinematicsRedundant manipulatorsQuadratic programmingRecurrent neural networksBi-criteria kinematic control
Type
Articles
Information
Robotica , Volume 38 , Issue 8 , August 2020 , pp. 1495 - 1512
Copyright
© Cambridge University Press 2019

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