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Deep Learning Based Kinematic Modeling of 3-RRR Parallel Manipulator

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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

This paper presents a novel low cost design for a 3-RRR Planar Parallel Manipulator (PPM). These manipulators proved their superiority over serial manipulators due to their speed, precision and smaller work space where the work space area is accounted for in the design to ensure that the robot is performing its task in a smooth and simple way without getting into any singularity points. The challenge with PPM is to obtain the kinematic constraint equations of the manipulator due to their complex non-linear behavior. Screw theory is a new approach that is used to compute the direct and inverse kinematics based on the relation between each link and its’ predecessor. The design is then inserted into ADAMS to study its dynamical behavior and to obtain a data set that would be used in analyzing the system in MATLAB. A Neuro-Fuzzy Inference System (NFIS) model was constructed in order to predict the end-effector position inside the work space and it is tuned with Particle swarm optimization (PSO) and Genetic algorithm (GA).

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Author information

Authors and Affiliations

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

    Abdelrahman Sayed Sayed, Zahra Fathy Ibrahim, Habiba A. Ibrahim, Nada Ali Mohamed & Hossam Hassan Ammar

  2. School of Engineering and Applied Sciences, Nile University Campus, Juhayna Square, Sheikh Zayed District, 6th of October City, 12588, Giza, Egypt

    Abdelrahman Sayed Sayed, Zahra Fathy Ibrahim, Habiba A. Ibrahim, Nada Ali Mohamed & Hossam Hassan Ammar

  3. Robotics and Internet-of-Things Lab (RIOTU), Prince Sultan University, Riyadh, Saudi Arabia

    Ahmad Taher Azar

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

    Ahmad Taher Azar

Authors
  1. Abdelrahman Sayed Sayed
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  2. Ahmad Taher Azar
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  3. Zahra Fathy Ibrahim
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  4. Habiba A. Ibrahim
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  5. Nada Ali Mohamed
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  6. 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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Sayed, A.S., Azar, A.T., Ibrahim, Z.F., Ibrahim, H.A., Mohamed, N.A., Ammar, H.H. (2020). Deep Learning Based Kinematic Modeling of 3-RRR Parallel Manipulator. 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_29

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