Genetic Programming and Object Modeling of Manipulation Robots
( Pp. 16-25)
More about authors
Krakhmalev Oleg N.
Candidate of Engineering, Associate Professor; associate professor at the Department of Data Analysis and Machine Learning
Financial University under the Government of the Russian Federation
Moscow, Russian Federation
Financial University under the Government of the Russian Federation
Moscow, Russian Federation
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).
How to Cite:
Krakhmalev O.N. Genetic Programming and Object Modeling of Manipulation Robots. Computational Nanotechnology. 2023. Vol. 10. No. 2. Pp. 16–25. (In Rus.) DOI: 10.33693/2313-223X-2023-10-2-16-25. EDN: AIXNRU
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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
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
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
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
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
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
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
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
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
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Keywords:
manipulation robots, inverse kinematics problem, object modeling, genetic algorithm, genetic programming.
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