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Robotica

Table of Contents - 1998 - Volume 16, Issue 04  

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Using a new GA-based multiobjective optimization technique for the design of robot arms 1

Carlos A. Coello Coello a1, Alan D. Christiansen a1 and Arturo Hernández Aguirre a1
a1 Department of Computer Science, Tulane University, New Orleans, LA 70118, USA

Abstract

This paper presents a hybrid approach to optimize the counterweight balancing of a robot arm. A new technique that combines an artificial intelligence technique called the genetic algorithm (GA) and the weighted min-max multiobjective optimization method is proposed. These techniques are included in a system developed by the authors, called MOSES, which is intended to be used as a tool for engineering design optimization. The results presented here show how the new proposed technique can get better trade-off solutions and a more accurate Pareto front for this highly non-convex problem using an ad-hoc floating point representation and traditional genetic operators. Finally, a methodology to compute the ideal vector using a genetic algorithm is presented. It is shown how with a very simple dynamic approach to adjust the parameters of the GA, it is possible to obtain better results than those previously reported in the literature for this problem.

(Received September 8 1997)


Key Words: GA technique; Robot arms; Multiobjective optimization.

Footnotes

1 This work was supported in part by EPSoR grant: NSF/LEQSF (1992–93)-ADP-04.