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An adaptive niching genetic algorithm approach for generating multiple solutions of serial manipulator inverse kinematics with applications to modular robots

Published online by Cambridge University Press:  26 May 2009

Saleh Tabandeh ,
William W. Melek  and
Christopher M. Clark
Saleh Tabandeh*
Affiliation:
Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON, CanadaN2L 3G1
William W. Melek
Affiliation:
Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON, CanadaN2L 3G1
Christopher M. Clark
Affiliation:
Department of Computer Science, California Polytechnic State University, 1 Grand Avenue, San Luis Obispo, CA 94307-0354, USA
*
*Corresponding author. E-mail: tabandeh@uwaterloo.ca
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Summary

Inverse kinematics (IK) is a nonlinear problem that may have multiple solutions. A modified genetic algorithm (GA) for solving the IK of a serial robotic manipulator is presented. The algorithm is capable of finding multiple solutions of the IK through niching methods. Despite the fact that the number and position of solutions in the search space depends on the position and orientation of the end-effector as well as the kinematic configuration (KC) of the robot, the number of GA parameters that must be set by a user are limited to a minimum through the use of an adaptive niching method. The only requirement of the algorithm is the forward kinematics (FK) equations which can be easily obtained from the Denavit–Hartenberg link parameters and joint variables of the robot. For identifying and processing the outputs of the proposed GA, a modified filtering and clustering phase is also added to the algorithm. For the postprocessing stage, a numerical IK solver is used to achieve convergence to the desired accuracy. The algorithm is validated on three KCs of a modular and reconfigurable robot (MRR).

Keywords

Pose estimation and registrationSerial manipulator design and kinematicsMotion planningModular robotsSpace robotics
Type
Article
Information
Robotica , Volume 28 , Issue 4 , July 2010 , pp. 493 - 507
Copyright
Copyright © Cambridge University Press 2009

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