Article 7418

Title of the article



Chikrin Dmitriy Evgen'evich, Candidate of engineering sciences, associate professor, sub-department of radiophysics, Kazan Federal University (18 Kremlyovskaya street, Kazan, Russia), E-mail:
Egorchev Anton Aleksandrovich, Research assistant, Institute of Physics, Kazan Federal University (18 Kremlyovskaya street, Kazan, Russia), E-mail:
Golousov Svyatoslav Vladimirovich, Research assistant, Institute of Physics, Kazan Federal University (18 Kremlyovskaya street, Kazan, Russia), E-mail:
Savinkov Pavel Andreevich, Research assistant, Institute of Physics, Kazan Federal University (18 Kremlyovskaya street, Kazan, Russia), E-mail:
Tumakov Dmitriy Nikolaevich, Candidate of physical and mathematical sciences, associate professor, sub-department of applied mathematics, Kazan Federal University (18 Kremlyovskaya street, Kazan, Russia), E-mail: 

Index UDK





Background. This paper considers a simplified autopilot problem - the development of a control system that solves the problems of path planning and tactical (real time) wheel platform control when solving a class of target tasks for driving a route specified as key points.
Materials and methods. Path planning and decision making of the presented strategy are based regarding tactical control on a dynamic reflexive graph, formed by Delaunay triangulation of road scene objects with its further translation by Voronoi diagram.
Results. Boundary conditions of the testing area, imitating conditions of a manufacturing plant/parking lot are shown. Safety criteria of performing maneuvers with both static and dynamic obstacles at the analyzed road scene are determined.
Conclusions. Series of model experiments for different test sites were made, showing effectiveness of proposed method. 

Key words

autopilot, control system, dynamic graph, wheel platform, Delaunay triangulation, Voronoi diagram 

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1. Özgüner U. Autonomous ground vehicles. Massachusetts: Artech House, 2011, 280 p.
2. Litman T. Implications for Transport Planning. 2018, p. 32
3. Ridley P., Corke P. Aust. Conf. Robot. Autom. United States: Piscataway, New Jersey: IEEE Service Center, 2001, pp. 26–31.
4. Pradalier C., Tews A., Roberts J. J. F. Robot. 2008, vol. 25, no. 4–5, pp. 243–267.
5. Bell T., Elkaim G., Parkinson D. B., O’Connor M., Bell T., Elkaim G., Parkinson B. Precis. Agric. 1996, no. precision Agricu 3, pp. 767–777.
6. Eindhoven T. U. Neural Networks. 2003, p. 10.
7. Shin W., Shin J., Kim B., Jeong K. International Journal of Control, Automation and Systems. 2017, vol. 15, no. 3, pp. 1322–1331.
8. Ailon A., Berman N., Arogeti S. Automatica. 2005, vol. 41, no. 5, pp. 889–896.
9. Huang Han-Pang, Chung Shu-Yun 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566). 2004, vol. 3, pp. 2813–2818.
10. Magid E., Lavrenov R., Afanasyev I. 2017 International Conference on Mechanical, System and Control Engineering (ICMSC). 2017, no. 1, pp. 383–387.
11. Rohnert H. Inf. Process. Lett. 1986, vol. 23, no. 2, pp. 71–76.
12. Choset H., Burdick J. Int. J. Rob. Res. 2000, vol. 19, no. 2, pp. 96–125.
13. Lopez Garcia, D. A., Gomez-Bravo F. Robotica. 2012, vol. 30, no. 7, pp. 1189–1201.
14. Bailey T., Nebot E. M., Rosenblatt J. K., Durrant-Whyte H. F. Proceedings 2000 ICRA. Millen-nium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065). Sydney, Australia, 2000, vol. 3, pp. 2512–2517.
15. Tan G., He H., Aaron S. J. Cent. South Univ. Technol. 2006, vol. 13, no. 1, pp. 80–86.
16. Geiger A. Probabilistic Model. 3D Urban Scene Underst. from Movable Platforms. 2013, April, pp. 1–162.
17. Smith D. E., Starkey J. M. Veh. Syst. Dyn. 1995, vol. 24, no. 2, pp. 163–181.
18. Pepy R., Lambert A., Mounier H. 2006 2nd International Conference on Information & Communication Technologies. 2006, vol. 1, no. 1, pp. 781–786.


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