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A neuro-genetic-simulated annealing approach to the inverse kinematics solution of robots: a simulation based study

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Abstract

In this study, a hybrid intelligent solution system including neural networks, genetic algorithms and simulated annealing has been proposed for the inverse kinematics solution of robotic manipulators. The main purpose of the proposed system is to decrease the end effector error of a neural network based inverse kinematics solution. In the designed hybrid intelligent system, simulated annealing algorithm has been used as a genetic operator to decrease the process time of the genetic algorithm to find the optimum solution. Obtained best solution from the neural network has been included in the initial solution of genetic algorithm with randomly produced solutions. The end effector error has been reduced micrometer levels after the implementation of the hybrid intelligent solution system.

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Acknowledgments

This work was supported by Research Fund of the Sakarya University. Project Number: 2013-05-02-001.

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Authors and Affiliations

  1. Electrical and Electronics Engineering Department, Faculty of Technology, Sakarya University, 54187, Sakarya, Turkey

    Raşit Köker

  2. Industrial Engineering Department, Engineering Faculty, Sakarya University, 54187, Sakarya, Turkey

    Tarık Çakar

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  1. Raşit Köker
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  2. Tarık Çakar
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Correspondence to Raşit Köker.

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Köker, R., Çakar, T. A neuro-genetic-simulated annealing approach to the inverse kinematics solution of robots: a simulation based study. Engineering with Computers 32, 553–565 (2016). https://doi.org/10.1007/s00366-015-0432-z

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  • DOI: https://doi.org/10.1007/s00366-015-0432-z

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