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Scientific and Technical Information Processing

, Volume 44, Issue 6, pp 430–439 | Cite as

A Heuristic Algorithm for Isolated Obstacle Detection by a Mobile Robot Based on Ranging Data

  • V. E. Pavlovsky
Article
  • 14 Downloads

Abstract

An algorithm for single isolated obstacle detection by a mobile robot using a range finder is described. The main algorithm block is constructed as a system of production rules that introduce logical relationships that make it possible to determine whether there is an obstacle in the field of normals to the surface. Detected obstacles are plotted on a 2D map. Obstacle mapping methods are discussed.

Keywords

mobile robot range finder production system 

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References

  1. 1.
    http://www.smprobotics.ru/ustroystvo-lidarov/.Google Scholar
  2. 2.
    http://robotgeeks.ru/blogs/articles/sravnenie-harakteristik-lazernyh-skanerov-sick-lms-200-i-hokuyo-urg-04-ug01.Google Scholar
  3. 3.
    Lemtyuzhnikov, D.S., Elementarnyi kurs optiki i dal’nomerov (Elementary Course of Optics and Range Finders), Moscow: Voenizdat, 1938.Google Scholar
  4. 4.
    Lysenko, O.N., Using laser scanners SICK AG for navigation of mobile robots, Komponenty Tekhnol., 2008, no. 1, pp. 56–59.Google Scholar
  5. 5.
    http://www.youtube.com/watch?v=NDKSG2v8His.Google Scholar
  6. 6.
    http://www.rusnauka.com/20_PNR_2011/Tecnic/11_90438. doc.htm.Google Scholar
  7. 7.
    Dickmanns, E.D. and Zapp, A., Autonomous high speed road vehicle guidance by computer vision, Proc. of International Federation of Automatic Control. World Congress (10th), 1988, pp. 221–226. https://trid.trb. org/view.aspx?id=494179.Google Scholar
  8. 8.
    https://rg.ru/2015/06/04/kamaz-site-anons.html.Google Scholar
  9. 9.
    http://www.tartanracing.org/blog/.Google Scholar
  10. 10.
    Pavlovskii, V.E., Ogol’tsov, V.N., and Ogol’tsov, N.S., Microcomputer control system of the lower level for a car with a manual transmission, Mekhatronika Avtom. Upr., 2014, no. 6, pp. 29–36.Google Scholar
  11. 11.
    Pavlovskii, V.E., Ogol’tsov, V.N., Spiridonova, I.A., and Pavlovskii, E.V., Problems of unmanned vehicle control, experimental implementation in the AvtoNIVA project, in Robototekhnika i tekhnicheskaya kibernetika (Robotics and Technical Cybernetics), St. Petersburg: GNTs TsNII RTK, 2015, no. 4, pp. 41–46.Google Scholar
  12. 12.
    Pavlovskii, V.E., Ogol’tsov, V.N., and Spiridonova, I.A., Problems of unmanned vehicle control in the AvtoNIVA project, Tr. Mezhdunarodnoi nauchno-tekhnicheskoi konferentsii “Ekstremal’naya robototekhnika” /Tr. II Vserossiiskogo seminara “Bespilotnye transportnye sredstva s elementami iskusstvennogo intellekta (BTS II)” (2015) (Proc. Int. Sci. Tech. Conf. Extreme Robotics /Proc. II All-Russian Seminar Unmanned Vehicles with Artificial Intelligence Elements (BTS II) (2015)), St. Petersburg, 2015, pp. 107–114.Google Scholar
  13. 13.
    Al-Sultan, K.S. and Aliyu, M.D.S., A new potential field-based algorithm for path planning, J. Intell. Rob. Syst., 1996, vol. 17, no. 3, pp. 265–282. doi 10.1007/BF00339664CrossRefGoogle Scholar
  14. 14.
    Hart, P.E., Nilsson, N.J., and Raphael, B., A formal basis for the heuristic determination of minimum cost paths, IEEE Trans. Syst. Sci. Cybern., 1968, vol. 4, no. 2, pp. 100–107. doi 10.1109/TSSC.1968.300136CrossRefGoogle Scholar

Copyright information

© Allerton Press, Inc. 2017

Authors and Affiliations

  1. 1.Keldysh Institute of Applied MathematicsRussian Academy of SciencesMoscowRussia

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