Comparison of mobile robot positioning techniques
Abstract
The article compares the accuracy of mobile robot positioning by the technique based on genetic algorithms, which are related to artificial intelligence, and by the trilateration technique. The authors consider the application of appropriate terminology borrowed from genetics and data processing algorithms for this technical problem. When using the genetic algorithm, the coordinates of the robot are found using angular methods or rigid logic methods, which are not particularly effective because of the large amount of data that is not needed for positioning, so there is a need to select the most likely indicators to find the best route to the target.
The genetic algorithm used in this study first selects the data by a certain criterion to enter the first population, and then the data falls into the beginning of the genetic algorithm. Each individual has chromosomes that represent a sequence of data, i.e., genes. After a chromosome is coded, the following genetic operations are performed: crossing over and mutation. These operations occur cyclically until a population with high fitness is found. The solution is a sequence of selected coordinates, from which a system is constructed to determine the optimal route to the destination.
The robot navigation techniques are compared in terms of coordinate positioning accuracy. Calculation results on dispersion and absolute positioning error show that the positioning using genetic algorithm gives less error than the one using trilateration method. The genetic algorithm allows finding the optimal solution of the positioning problem while reducing a significant influence of the measurement error of sensors and other measuring devices on the result.
References
Ababii, V., Sudacevschi, V., Rosca N., Lungu I. [Sensors network for robots control]. International Scientific-Practical Conference “Applied Scientific and Technical Research”, 2019, vol. 6, no. 4, pp. 26–32. (Ukr)
Gladkov, L. A., Kureychik V. V., Kureychik V. M. Geneticheskiye algoritmy [Genetic algorithms]. Moldova, Chisinau, Fizmatlit, 2010, 320 p. (Rus)
Voytyuk I. F., Dyvak M. P., Nemish V. M. The method and genetic algorithm for structural identification of interval difference operators in the tasks of ecological monitoring. Scientific Works of DonNTU, 2011, vol. 4, no. 4, pp. 71–89. (Ukr)
Rutkovskaya D. Neyronnyye seti, geneticheskiye algoritmy i nechetkiye sistemy [Neural Networks, Genetic Algorithms and Fuzzy Systems]. Russia, Moscow, Hot line-Telecom, 2006, 452 p. (Rus)
Yurevich Ye. I. Osnovy robototekhniki [Fundamentals of Robotics]. St. Petersburg «BHV-Petersburg», 2005, 416 p. (Rus)
Lebedev B. K. [Genetic algorithm for the distribution of compounds by layers in multilayer global VLSI tracing.]. Izvestiya SFedU. Engineering Sciences, Russia, Tagаnrog, Izvestia UFU, 2012, № 7, pp. 14–22. (Rus)
Zdor G. N., Potekha A.V. Methodical features of usage of genetic algorithms for prediction of development of fire robots. Vesnik Hrodzenskaha Dziarzhaunaha Universiteta Imia Ianki Kupaly. Seryia 6. Tekhnika. https://rep.bntu.by/handle/data/84744 (Rus)
Afanas’yev V.N. Dinamicheskiye sistemy upravleniya s nepolnoy informatsiyey. Algoritmicheskoye konstruirovaniye [Dynamic control systems with incomplete information. Algorithmic design]. Moscow, Lenand, 2018, 216 p. (Rus)
Gerasun V. M., Zhoga V. V., Nesmiyanov I. A. et al. [Inve¬sti¬ga¬tion of the optimal configurations of a manipulator-tripod with a rotary base]. Mechatronics, Automation, Control, 2016, no. 6, pp. 16–21. (Rus)
D printing and 3D printer device]. https://make-3d.ru/articles/chto-takoe-3d-pechat/ (20 september 2021) (Rus)
Pshikhopov V. Kh. Organization of repellers when moving mobile robots in an environment with obstacles. Mechatronics, Automation, Control, 2008, no. 2, pp. 34–41. (Rus)
Zenkevich S. L., Yushchenko A. S. Osnovy upravleniya manipulyatsionnymi robotami [Fundamentals of manipulation robots control]. Moscow, BMSTU, 2004, 480 p. (Rus)
Ulyanov S. V. [Robust intelligent control systems: system and structural analysis]. Fuzzy Systems and Soft Computing, 2008, no. 2, pp. 15–41. (Rus)
Rybakov A. V. [Hexapod robot with neural network motion control system]. Robotics and Technical Cybernetics, 2017, no. 3, pp. 59–66. (Rus)
Darintsev O. V., Migranov A. B. [Mobile group traffic planning system microrobots based on genetic algorithms. Theory and Systems Management, 2007, no. 3, pp. 163–173. (Rus)
Zhu J., Huai L., Cui R. Research and application of hybrid random selection genetic algorithm. 2017 10th International Symposium on Computational Intelligence and Design (ISCID), 2017, vol. 20, iss. 7, p. 330. https://doi.org/10.1109/ISCID41719.2017
Croitoru N. High probability mutation and error thresholds in genetic algorithms, 2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2015, vol. 17, iss. 4, p. 271. https://doi.org/10.1109/SYNASC.2015.51
Kar S., Kabir M. M. J. Comparative analysis of mining fuzzy association rule using genetic algorithm, 2019 International Con¬ference on Electrical, Computer and Communication Engineering (ECCE), 2019, vol. 7, iss. 3, p. 5. https://doi.org/10.1109/ECACE.2019.8679336
Chen C., He J., Hong T. A two-stage multi-objective genetic-fuzzy mining algorithm, 2013 IEEE International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS), 2013, vol. 10, iss. 2, p. 16. https://doi.org/10.1109/GEFS.2013.6601050
Copyright (c) 2021 Yamnenko Yu. S., Osokin V. O.
This work is licensed under a Creative Commons Attribution 4.0 International License.
