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Optimization of Industrial, Vision-Based, Intuitively Generated Robot Point-Allocating Tasks Using Genetic Algorithms

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4 Author(s)
Loredo-Flores, A. ; Centro de Inves- tigacion y Estudios de Posgrado (CIEP), Univ. Autonoma de San Luis Potosi, San Luis Potosi ; Gonzalez-Galvan, E.J. ; Cervantes-Sanchez, J.J. ; Martinez-Soto, A.

Current industrial robot-programming methods require, depending on the task to be developed, an elevated degree of technical ability and time from a human operator, in order to obtain a precise, nonoptimal result. This correspondence paper presents a methodology used to generate an optimal sequence of robot configurations that enable a precise point-allocating task applicable, for instance, to spot-welding, drilling, or electronic component placement maneuvers. The optimization process starts from a nonoptimal, initial sequence designated intuitively by a human operator using an easy-to-use interface. In this correspondence paper, intuitive programming is considered as the process of defining, in a computer graphics environment and with a limited user knowledge of robotics or the industrial task, the sequence of motions that enable the execution of a complex industrial robotic maneuver. Such an initial sequence is later followed by a robot, very precisely, using a vision-based, calibration-free, robot control method. Further robot path optimization is performed with a genetic algorithm approach. An industrial robot, which is part of the experimental setup, was used in order to validate the proposed procedure.

Published in:

Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on  (Volume:38 ,  Issue: 4 )

Date of Publication:

July 2008

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