Optimization of industrial, vision-based, intuitively generated robot point-allocating tasks using genetic algorithms
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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. © 2008 IEEE.
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Genetic algorithms; Robot vision systems; Simulated annealing; User interfaces Algorithms; Automation; Computational geometry; Computer graphics; Diesel engines; Genetic algorithms; Graphic methods; Industrial robots; Optimization; Robot programming; Robotics; Robots; (e ,3e) process; Allocating task; Electronic components; Experimental set ups; human operators; In order; Intuitive programming; Optimal sequences; Optimization processes; robot configurations; robot controls; robot path; Vision-based; Computer vision
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