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Experimental Kinematic Modeling of 6-DOF Serial Manipulator Using Hybrid Deep Learning

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Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) (AICV 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1153))

Abstract

According to its significance, robotics is always an area of interest for research and further development. While robots have varying types, design and sizes, the six degrees of freedom (DOF) serial manipulator is a famous robotic arm that has a vast areas of applications, not only in industrial application, but also in other fields such as medical and exploration applications. Accordingly, control and optimization of such robotic arm is crucial and needed. In this paper, different analyses are done on the chosen design of robotic arm. Forward kinematics are calculated and validated, then simulation using MSC ADAMS is done, followed by experimentation and tracking using Microsoft Kinect. Two approaches are used in this study: adaprive neuro fuzzy (ANF) system optimized by simulated annealing (SA) algorithm and convolutional neural networks (CNNs) optimized by adaptive moment estimation (Adam). The same inputs are given to both models and their results are compared in order to determine the best fit algorithm for higher precision in the given robotic model. The findings have shown that the accuracy of CNNs is higher. Furthermore, this advantage has a higher cost for the time of computation than for NFs with SA.

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Author information

Authors and Affiliations

  1. Smart Engineering Systems Research Center (SESC), Nile University, Sheikh Zayed City, Giza, 12588, Egypt

    Nada Ali Mohamed, Nada Elsayed Abbas, Mamdouh Ahmed Ezzeldin & Hossam Hassan Ammar

  2. Robotics and Internet of Things Lab, Prince Sultan University, Riyadh, Saudi Arabia

    Ahmad Taher Azar

  3. Faculty of Computers and Artificial Intelligence, Benha University, Banha, Egypt

    Ahmad Taher Azar

Authors
  1. Nada Ali Mohamed
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  2. Ahmad Taher Azar
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  3. Nada Elsayed Abbas
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  4. Mamdouh Ahmed Ezzeldin
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  5. Hossam Hassan Ammar
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Corresponding author

Correspondence to Ahmad Taher Azar .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Faculty of Computers and Information, Giza, Egypt

    Aboul-Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Banha, Egypt

    Ahmad Taher Azar

  3. Faculty of Computers and Information, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadajalara, Jalisco, Mexico

    Diego Oliva

  5. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Fahmy M. Tolba

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Mohamed, N.A., Azar, A.T., Abbas, N.E., Ezzeldin, M.A., Ammar, H.H. (2020). Experimental Kinematic Modeling of 6-DOF Serial Manipulator Using Hybrid Deep Learning. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_27

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