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A memetic algorithm approach for solving the task-based configuration optimization problem in serial modular and reconfigurable robots

Published online by Cambridge University Press:  08 December 2014

Saleh Tabandeh ,
William Melek ,
Mohammad Biglarbegian ,
Seong-hoon Peter Won  and
Chris Clark
Saleh Tabandeh
Affiliation:
Motion Control, Fanuc robotics, Rochester Hills, Michigan, USA
William Melek
Affiliation:
Mechanical and Mechatronics engineering, University of Waterloo, Waterloo, Ontario, Canada
Mohammad Biglarbegian
Affiliation:
School of Engineering, University of Guelph, Guelph, Ontario, Canada
Seong-hoon Peter Won*
Affiliation:
Mechanical and Mechatronics engineering, University of Waterloo, Waterloo, Ontario, Canada
Chris Clark
Affiliation:
Harvey Mudd College, Claremont, California, USA
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper presents a novel configuration optimization method for multi degree-of-freedom modular reconfigurable robots (MRR) using a memetic algorithm (MA) that combines genetic algorithms (GAs) and a local search method. The proposed method generates multiple solutions to the inverse kinematics (IK) problem for any given spatial task and the MA chooses the most suitable configuration based on the search objectives. Since the dimension of each robotic link in this optimization is considered telescopic, the proposed method is able to find better solutions to the IK problem than GAs. The case study for a 3-DOF MRR shows that the MA finds solutions to the IK problem much faster than a GA with noticeably less reachability error. Additional case studies show that the proposed MA method can find multiple IK solutions in various scenarios and identify the fittest solution as a suboptimal configuration for the MRR.

Keywords

memetic algorithmsreconfigurable robotsconfiguration optimizationreachability
Type
Articles
Information
Robotica , Volume 34 , Issue 9 , September 2016 , pp. 1979 - 2008
Copyright
Copyright © Cambridge University Press 2014 

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