Fast Convergent Ali Baba and Forty Thieves Algorithm for Inverse Kinematic Solution of 7-DOF Robotic Manipulator

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Published Dec 15, 2023
https://doi.org/10.56158/jpte.2023.58.2.02
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Vol. 2 No. 02 (2023): December

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Bilal Özak
Graduate School of Natural and Applied Sciences, Erciyes University, Kayseri, Türkiye
https://orcid.org/0000-0002-8128-3879

Abstract

Inverse kinematics is a crucial topic in robotics, enabling robots to calculate the joint angles required to achieve the desired end effector position and orientation. Solving the inverse kinematics problem quickly with high accuracy is vital for robot manipulators. If sufficient speed is provided, the real-time motion planning task of robot manipulators can be achieved. Real-time motion planning of complex robot manipulators is not possible with classical mathematical methods. Overcoming this problem will provide many benefits in the design and control of robot manipulators. In contemporary research, metaheuristic approaches have become widely employed for addressing the inverse kinematics problem. This investigation utilizes the efficient and simple Ali Baba and the Forty Thieves (AFT) algorithm to resolve the inverse kinematics problem. To increase convergence speed of AFT, a parameter has been used in early iterations of the algorithm to prevent thieves from randomly searching within the search area to find Ali Baba when they realized they had been deceived. Additionally, an approach has been proposed regarding the accuracy of the information brought by the Marjaneh. Finally, the inverse kinematics solution of the 7-DOF robot manipulator was carried out comparatively.

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite
Özak, B. (2023). Fast Convergent Ali Baba and Forty Thieves Algorithm for Inverse Kinematic Solution of 7-DOF Robotic Manipulator. International Journal of Pioneering Technology and Engineering, 2(02), 181–187. https://doi.org/10.56158/jpte.2023.58.2.02

References

  1. Osaba, E., Villar-Rodriguez, E., Del Ser, J., Nebro, A. J., Molina, D., LaTorre, A., ... & Herrera, F. “A tutorial on the design, experimentation, and application of metaheuristic algorithms to real-world optimization problems” Swarm and Evolutionary Computation, 64, 100888,2021
  2. Zaman, H. R. R., & Gharehchopogh, F. S. “An improved particle swarm optimization with backtracking search optimization algorithm for solving continuous optimization problems” Engineering with Computers, 38(Suppl 4), 2797-2831,2022.
  3. Holland, J. H. “Genetic algorithms” Scientific american, 267(1), 66-73,1992.
  4. Storn, R., & Price, K. “Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces.” Journal of global optimization, 11, 341-359,1997.
  5. Eberhart, R., & Kennedy, J. “Particle swarm optimization.” In Proceedings of the IEEE international conference on neural networks Vol. 4, pp. 1942-1948,1995
  6. Karaboga, D. “An idea based on honeybee swarm for numerical optimization.” Technical report-tr06, Erciyes university, engineering faculty, computer engineering department, Vol. 200, pp. 1-10,2005.
  7. Mirjalili, S. “SCA: a sine cosine algorithm for solving optimization problems”. Knowledge-based systems, 96, 120-133,2016.
  8. Kurdila J. and Ben-Tzvi P., “Dynamics and control of robotic systems.” John Wiley and Sons,2019.
  9. Sahu, V. S. D. M., Samal, P., and Panigrahi, C. K., 2022. Modelling, and control techniques of robotic manipulators: A review. Materials Today: Proceedings, 56:2758-2766.
  10. Köker, R., Öz, C., Çakar T, and Ekiz H., “A study of neural network based inverse kinematics solution for a three-joint robot” Rob Auton Syst, 49:227–234,2004.
  11. Dereli, S., and Köker, R., “A meta-heuristic proposal for inverse kinematics solution of 7-DOF serial robotic manipulator: quantum behaved particle swarm algorithm”. Artificial Intelligence Review, 53:949-964,2020.
  12. Abdor-Sierra, J. A., Merchán-Cruz, E. A., and Rodríguez-Cañizo, R. G., “A comparative analysis of metaheuristic algorithms for solving the inverse kinematics of robot manipulators” Results in Engineering, 16:100597,2022.
  13. Kucuk, S., 2017. “Optimal trajectory generation algorithm for serial and parallel manipulators,” Robot. Comput. Integrated Manuf. 48:219–232,2017.
  14. Souza, D. A., Batista, J. G., dos Reis, L. L., and Júnior, A. B., “PID controller with novel PSO applied to a joint of a robotic manipulator”. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 43:1-14,2021.
  15. Braik, M., Ryalat, M. H., & Al-Zoubi, H. “A novel meta-heuristic algorithm for solving numerical optimization problems": Ali Baba and the forty thieves.” Neural Computing and Applications, 34(1), 409-455,2021.
  16. Yaskawa America, Inc., “SIA20D Compact 7-Axis Robot Arm”, Jan. 2018.