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  4. The power of metaheuristic algorithms for robotics: singularity & trajectory

The power of metaheuristic algorithms for robotics: singularity & trajectory

MMC.
2024;
Volume 11, Number 4
: pp. 946–953
https://doi.org/10.23939/mmc2024.04.946
Received: January 07, 2024
Revised: August 16, 2024
Accepted: September 02, 2024

Harrade I., Kmich M., Sayyouri M., Chalh Z.  The power of metaheuristic algorithms for robotics: singularity & trajectory. Mathematical Modeling and Computing. Vol. 11, No. 4, pp. 946–953 (2024)

Authors:
  1. I. Harrade
  2. M. Kmich
  3. M. Sayyouri
  4. Z. Chalh
1
National School of Applied Sciences, Sidi Mohamed Ben Abdellah-Fez University, Engineering Systems and Applications Laboratory, Fez, Morocco
2
National School of Applied Sciences, Sidi Mohamed Ben Abdellah-Fez University, Engineering Systems and Applications Laboratory, Fez, Morocco
3
National School of Applied Sciences, Sidi Mohamed Ben Abdellah-Fez University, Engineering Systems and Applications Laboratory, Fez, Morocco
4
National School of Applied Sciences, Sidi Mohamed Ben Abdellah-Fez University, Engineering Systems and Applications Laboratory, Fez, Morocco

When calculating the kinematic model of any kind of robot, parallel or planar, the singularity problem frequently crops up.  We propose the application of metaheuristic algorithms to identify the needed target to solve this issue and minimize calculus.  Simulation results using several metaheuristic algorithms (MA) on the same population have been obtained with reduced computing time (0.50 s).  The efficacy of the suggested technique for maximizing the position and trajectory of the joints in a 3-DOF or 3-RRR (with three rotational degrees of freedom) planar parallel manipulator robot is amply illustrated by them.  The sine-cosine algorithm (SCA) and certain target points are essentially the basis of the method, which determines the optimal desired path.  These outcomes show how well the suggested strategy works for maximizing calculations, positions, and the ideal robot trajectory.

3-RRR planar parallel manipulator robot
3-DOF Robot
metaheuristic algorithms
avoid singularity
joint position
optimal trajectory
visual servoing
  1. Shi H., Li R., Bai X., Zhang Y., Min L., Wang D., Lu X., Yan Y., Lei Y.  A review for control theory and condition monitoring on construction robots.  Journal of Field Robotics.  40 (4), 934–954 (2023).
  2. Choudhury R., Singh Y.  Planar parallel manipulators: A review on kinematic, dynamic, and control aspects.  Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering. (2023).
  3. Liu S., Qiu Z., Zhang X.  Singularity and path-planning with the working mode conversion of a 3-DOF 3-RRR planar parallel manipulator.  Mechanism and Machine Theory.  107, 166–182 (2017).
  4. Harrade I., Daoui A., Chalh Z., Sayyouri M.  Visual Servoing of a 3R Robot by Metaheuristic Algorithms.  Statistics, Optimization & Information Computing.  11 (1), 116–124 (2023).
  5. Hubert J.  Parallel manipulators, singularities and static analysis.  PhD Thesis, École Nationale Supérieure des Mines de Paris (2010).
  6. Guo J., Nguyen H. T., Liu C., Cheah C. C.  Convolutional Neural Network-Based Robot Control for an Eye-in-Hand Camera.  IEEE Transactions on Systems, Man, and Cybernetics: Systems.  53 (8), 4764–4775  (2023).
  7. Chotikunnan P., Pititheeraphab Y.  Adaptive P Control and Adaptive Fuzzy Logic Controller with Expert System Imple-mentation for Robotic Manipulator Application.  Journal of Robotics and Control (JRC).  4 (2), 217–226 (2023).
  8. Turgut O. E., Turgut M. S., Kirtepe E.  A systematic review of the emerging metaheuristic algorithms on solving complex optimization problems.  Neural Computing and Applications.  35, 14275–14378  (2023).
  9. Sahoo S. K., Saha A. K., Ezugwu A. E., Agushaka J. O., Abuhaija B., Alsoud A. R., Abualigah L.  Moth Flame Optimization: Theory, Modifications, Hybridizations, and Applications.  Archives of Computational Methods in Engineering.  30, 391–426 (2023).
  10. Ganguli C., Shandilya S. K., Nehrey M., Havryliuk M.  Adaptive Artificial Bee Colony Algorithm for Nature-Inspired Cyber Defense.  Systems.  11 (1), 27 (2023).
  11. Golalipour K., Faraji Davoudkhani I., Nasri S., Naderipour A., Mirjalili S., Abdelaziz A. Y., El-Shahat A.  The corona virus search optimizer for solving global and engineering optimization problems.  Alexandria Engineering Journal.  78, 614–642 (2023).
  12. El Asri F., Tajani C., Fakhouri H.  Investigation of ant colony optimization with Levy flight technique for a class of stochastic combinatorial optimization problem.  Mathematical Modeling and Computing.  10 (4), 1132–1142 (2023).
  13. Brambila-Hernández J. A., García-Morales M. Á., Fraire-Huacuja H. J., Villegas-Huerta E., Becerra-del-Ángel A.  Hybrid Harmony Search Optimization Algorithm for Continuous Functions.  Mathematical and Computational Applications.  28 (2), 29 (2023).
  14. Abualigah L., Diabat A.  Advances in Sine Cosine Algorithm: A comprehensive survey.  Artificial Intelligence Review.  54, 2567–2608 (2021).
  15. Mirjalili S.  SCA: A Sine Cosine Algorithm for solving optimization problems.  Knowledge-Based Systems.  96, 120–133 (2016).
  16. Alandoli E. A., Lee T. S.  A critical review of control techniques for flexible and rigid link manipulators.  Robotica.  38 (12), 2239–2265 (2020).
  17. Benameur S., Tadrist S., Mellal M. A., Williams E. J.  Basic Concepts of Manipulator Robot Control.  Design and Control Advances in Robotics.  1–12 (2023).
  18. Adagolodjo Y., Renda F., Duriez C.  Coupling Numerical Deformable Models in Global and Reduced Coordinates for the Simulation of the Direct and the Inverse Kinematics of Soft Robots.  IEEE Robotics and Automation Letters.  6 (2), 3910–3917 (2021).
  19. Sayed A. S., Mohamed N. A., Salem A. A., Ammar H. H.  Modeling of Nonlinear 3-RRR Planar Parallel Manipulator: Kinematics and Dynamics Experimental Analysis.  International Journal of Mechanical & Mechatronics Engineering.  20, 175–185 (2020).
  20. Rocha C. R., Tonetto C. P., Dias A.  A comparison between the Denavit–Hartenberg and the screw-based methods used in kinematic modeling of robot manipulators.  Robotics and Computer-Integrated Manufacturing.  27 (4), 723–728 (2011).
  21. Gambhire S. J., Kishore D. R., Londhe P. S., Pawar S. N.  Review of sliding mode based control techniques for control system applications.  International Journal of Dynamics and Control.  9, 363–378 (2021).
  22. Nawress B., Lakhal A. N. G., Braïek N. B.  Neural State and Disturbance Observer-based Sliding Mode Control of a Unicycle Robot.  2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET). 1–6 (2023).
  23. Chotikunnan P., Chotikunnan R.  Dual design PID controller for robotic manipulator application.  Journal of Robotics and Control (JRC).  4 (1), 23–34 (2023).
  24. Spong M. W.  An historical perspective on the control of robotic manipulators.  Annual Review of Control, Robotics, and Autonomous Systems.  5, 1–31 (2022).
  25. Wang C., Frazelle C. G., Wagner J. R., Walker I. D.  Dynamic Control of Multi-Section Three-Dimensional Continuum Manipulators Based on Virtual Discrete-Jointed Robot Models.  IEEE/ASME Transactions on Mechatronics.  26 (2), 777–788 (2021).
  26. Mashkov O. A., Chumakevich V. A., Mamchur Y. V., Kosenko V. R.  The method of inverse problems of dynamics for the synthesis of a system of stabilization of the movement of a dynamic object on operatively programmable trajectories.  Mathematical Modeling and Computing.  7 (1), 29–38 (2020).
  27. Kada D., Kouidere A., Balatif O., Rachik M.  Mathematical modeling of the gaming disorder model with media coverage: optimal control approach.  Mathematical Modeling and Computing.  10 (1), 245–260 (2023).