Genetic Programming and Object Modeling of Manipulation Robots

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Abstract

The application of a genetic algorithm to solve the inverse kinematics problem of manipulative robots is considered. The basic concepts of the method of finding solutions using a genetic algorithm are defined. A block diagram of a simple genetic algorithm is presented. It is justified to use multiprocessor computing systems (transputers) to calculate genetic operators. This will greatly increase the efficiency of genetic algorithms. Manipulation systems with three and four links are selected as examples. The problem statement consisted in determining the hinge coordinates of an industrial robot by the specified Cartesian coordinates of the position of the center of the tool (TCP – Tool Center Point) installed on its final link. The results obtained confirm the effectiveness of genetic algorithms in solving inverse kinematics problems of industrial (manipulation) robots. Based on graph theory, the genetic programming procedure is defined as a way to find optimal kinematic structures of robot manipulation systems. The use of genetic programming for modification of object models of manipulation robots is shown. The object representation of the dynamic model of manipulation robots is considered. It is noted that the recombination of objects corresponding to mathematical expressions having mechanical meaning requires kinematic correspondence of the objects used. It is proposed to draw up object diagrams using computer programs that automate this process based on the principle of visual programming (Low-code).

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About the authors

Oleg N. Krakhmalev

Financial University under the Government of the Russian Federation

Author for correspondence.
Email: onkrakhmalev@fa.ru
ORCID iD: 0000-0002-9388-4137

Candidate of Engineering, Associate Professor; associate professor at the Department of Data Analysis and Machine Learning of the Financial University under the Government of the Russian Federation

Russian Federation, Moscow

References

  1. Al Tahtawi A., Agni M., Hendrawati T. Small-scale robot arm design with pick and place mission based on inverse kinematics. Journal of Robotics and Control. 2021. No. 2. P. 6. DOI: https://doi.org/10.18196/jrc.26124
  2. Byun G., Kikuuwe R. Stiff and safe task-space position and attitude controller for robotic manipulators. Robomech J. 2020. No. 7. P. 18. DOI: https://doi.org/10.1186/s40648-020-00166-1
  3. Ferrentino E., Chiacchio P. On the optimal resolution of inverse kinematics for redundant manipulators using a topological analysis. J. Mechanisms Robotics. 2020. No. 12 (3). P. 031002. DOI: https://doi.org/10.1115/1.4045178
  4. Kalyayev A.V., Galuyev G.A. Digital neurocomputer VLSI-systems with parallel architecture. In: International Neural Network Conference. Dordrecht: Springer, 1990. DOI: https://doi.org/10.1007/978-94-009-0643-3_17
  5. Karpińska J., Tchoń K. Performance-oriented design of in- verse kinematics algorithms: Extended Jacobian approxi-mation of the Jacobian pseudo-inverse. J. Mechanisms Robotics. 2012. No. 4 (2). P. 021008. DOI: https://doi.org/10.1115/1.4006192
  6. Krakhmalev N.O., Korostelyov D.A. Solutions of the inverse kinematic problem for manipulation robots based on the genetic algorithm. IOP Conf. Ser.: Mater. Sci. Eng. 2020. No. 747. P. 012117. DOI: https://doi.org/10.1088/1757-899X/747/1/012117
  7. Krakhmalev O., Krakhmalev N., Gataullin S. et al. Mathe-matics model for 6-DOF joints manipulation robots. Mathe-matics. 2021. No. 9. P. 2828. DOI: https://doi.org/10.3390/math9212828
  8. Krakhmalev O. Object-oriented modeling of manipulation systems dynamics based on transformation matrices of homo-geneous coordinates. Robotics and Technical Cybernetics. 2017. No. 2 (15). Pp. 32–36.
  9. Krakhmalev O. Object-oriented simulation of robots’ mani-pulation systems. Robotics and Technical Cybernetics. 2018. No. 4 (21). Pp. 41–47. DOI: https://doi.org/10.31776/RTCJ.6406
  10. Krakhmalev O. Designing object diagrams and the method of structural mutations in models of robots’ manipulation systems. Proceedings of 14th International Conference on Electromechanics and Robotics “Zavalishin’s Readings”. Smart Innovation. Systems and Technologies. 2020. No. 154. Pp. 209–221. URL: https://link.springer.com/chapter/10.1007/978-981-13-9267-2_18
  11. Krakhmalev O.N. Use of structural mutations in object-oriented mathematical models of robot manipulation systems. Mathematical Models and Computer Simulations. 2020. No. 12 (1). Pp. 90–98. DOI: https://doi.org/10.1134/S023408791906008X
  12. Krakhmalev O., Korchagin S., Pleshakova E. et al. Parallel computational algorithm for object‐oriented modeling of manipulation robots. Mathematics. 2021. No. 9. P. 2886. DOI: https://doi.org/10.3390/math9222886
  13. Liu Q., Tian W., Li B., Ma Y. Kinematics of a 5-axis hybrid robot near singular configurations. Robotics and Computer-Integrated Manufacturing. 2022. No. 75. P. 102294. DOI: https://doi.org/10.1016/j.rcim.2021.102294
  14. Malik A., Henderson T., Prazenica R. Multi-objective swarm intelligence trajectory generation for a 7 degree of freedom robotic manipulator. Robotics. 2021. No. 10. P. 127. DOI: https://doi.org/10.3390/robotics10040127
  15. Malik A., Lischuk Y., Henderson T., Prazenica R. A deep reinforcement-learning approach for inverse kinematics solution of a high degree of freedom robotic manipulator. Robotics. 2022. No. 11. P. 44. DOI: https://doi.org/10.3390/robotics11020044
  16. Marsono M., Yoto Y., Suyetno A., Nurmalasari R. Design and programming of 5 axis manipulator robot with GrblGru open source software on preparing vocational students’ robotic skills. Journal of Robotics and Control. 2021. No. 2. P. 6. DOI: https://doi.org/10.18196/jrc.26134
  17. Strey A., Avellana N., Holgado R. et al. A configurable parallel neurocomputer. In: Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems, November 20–23, 1995. doi: 10.1109/ANNES.1995.499438
  18. Tian W., Mou M., Yang J., Yin F. Kinematic calibration of a 5-DOF hybrid kinematic machine tool by considering the ill-posed identification problem using regularisation method. Robotics and Computer-Integrated Manufacturing. 2019. No. 60. Pp. 49–62. DOI: https://doi.org/10.1016/j.rcim.2019.05.016
  19. Torchigin V.P., Kobyakov A.E. Neurocomputers based on massively parallel architecture using optical means. In: Proceedings of SPIE. The International Society for Optical Engineering, December 1994. DOI: https://doi.org/10.1117/12.195591
  20. Tringali A., Cocuzza S. Globally optimal inverse kinematics method for a redundant robot manipulator with linear and nonlinear constraints. Robotics. 2020. No. 9. P. 61. DOI: https://doi.org/10.3390/robotics9030061
  21. Wajiansyah A., Supriadi S., Gaffar A.F., Putra A.B. Modeling of 2-DOF hexapod leg using analytical method. Journal of Robotics and Control. 2021. No. 2. P. 5. DOI: https://doi.org/10.18196/jrc.25119
  22. Ye H., Wang D., Wu J. et al. Forward and inverse kinematics of a 5-DOF hybrid robot for composite material machining. Robotics and Computer-Integrated Manufacturing. 2020. No. 65. P. 101961. DOI: https://doi.org/10.1016/j.rcim.2020. 101961
  23. Krakhmalev O.N. Object modeling in the kinematics of manipulative robots. Neurocomputers: Development, Application. 2022. No. 5. Pp. 55–66. (In Rus.) DOI: https://doi.org/10.18127/j19998554-202205-06

Supplementary files

Supplementary Files
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1. JATS XML
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2. Fig. 1. A simple genetic algorithm

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3. Fig. 2. 3-DOF manipulation system

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4. Fig. 3. 4-DOF manipulation system

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5. Fig. 4. Mean fitness calculations (1)

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6. Fig. 5. Mean fitness calculations (2)

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7. Fig. 6. Chromosomes of parents: a – parent 1; b – parent 2

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8. Fig. 7. Chromosomes of descendants: a – descendant 1, b – descendant 2

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9. Fig. 8. Object scheme of dynamic model (4)

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10. Fig. 9. Object scheme of element m123

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11. Fig. 10. Chromosomes of the 1st and 2nd parents of the element m123

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12. Fig. 11. Recombination of genes in chromosomes of the 1st and 2nd parents of the element m123

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13. Fig. 12. Chromosomes of individuals of the 1st and 2nd descendants of the element m123

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