Advertisement

The Methods of Radar Detection of Landmarks by Mobile Autonomous Robots

  • Oleksandr Poliarus
  • Yevhen Poliakov
Chapter

Abstract

The chapter is devoted to the actual problem of navigating mobile autonomous robots on unknown terrain in the absence of GPS. Such a problem is considered solved if the robot is capable to detect landmark and estimate own coordinates relative to the landmark. A reliable method for solving the problem is the simultaneous use of several measuring systems operating on different physical principles. In classical radar, the reliable detection of the echo signals from immovable landmark, which differ little from the echo signals that are reflected from the surrounding area, is impossible. Comparison of such signals is carried out in the chapter for various terrains at different lengths of electromagnetic waves. It is found that the only difference between them is the possible amplitude jump of signal, reflected from the landmark. This jump occurs during the movement of the robot or scanning the space by the robot antenna. The probability of detecting such a jump, the accuracy of the amplitude estimation, and the speed of the device operation are analyzed in the chapter based on the developed system of stochastic differential equations.

Keywords

Radar detection GPS Navigation Mobile autonomous robots 

Abbreviations

GPS

Global Positioning System

MAR

Mobile autonomous robots

EMW

Electromagnetic waves

AP

Antenna pattern

RCS

Radar cross section

References

  1. 1.
    Colle, E., & Galerne, S. (2017). A multihypothesis set approach for mobile robot localization using heterogeneous measurements provided by the internet of things. Robotics and Autonomous Systems, 96, 102–113. Elsevier.CrossRefGoogle Scholar
  2. 2.
    Garulli, A., & Vicino, A. (2001). Set membership localization of mobile robots via angle measurements. IEEE Transactions on Robotics and Automation, 17(4), 450–463.CrossRefGoogle Scholar
  3. 3.
    Lindner, L., Sergiyenko, O., Rivas-Lopez, V., Hernandez-Babluena, D., Flores-Fuentes, W., Rodríguez-Quiňonez, J. C., Murrieta-Rico, F. N., Ivanov, M., Tyrsa, V., & Basaca, L. C. (2017). Exact laser beam positioning for measurement of vegetation vitality. Industrial Robot, 44(4), 532–541.CrossRefGoogle Scholar
  4. 4.
    Sergiyenko, O. Y. (2010). Optoelectronic system for mobile robot navigation. Optoelectronics, Instrumentation and Data Processing, 46(5), 414–428.CrossRefGoogle Scholar
  5. 5.
    Prorok, A., Gonon, L., & Martinoli, A. (2012). Online model estimation of ultra-wideband TDOA measurements for mobile robot localization. In IEEE International Conference on Robotics and Automation (ICRA) (8 p). Saint Paul, USA.Google Scholar
  6. 6.
    Ishimaru, A. (1978). Wave propagation and scattering in random media. Vol. 2: Multiple scattering, turbulence, rough surfaces and remote sensing (317 p). New York: Academic.Google Scholar
  7. 7.
    Rischka, M., & Conrad, S. (2014). Landmark recognition: State-of-the-art methods in a large-scale scenario. In Proceedings of the 16th LWA Workshops: KDML, IR and FGWM (pp. 10–17). Aachen, Germany.Google Scholar
  8. 8.
    Schmid, C., & Mohr, R. (1996). Combining greyvalue invariants with local constraints for object recognition. In Proceedings of the Conference on Computer Vision and Pattern Recognition (pp. 872–877). San Francisco, CA, USA.Google Scholar
  9. 9.
    Hanumante, V., Roy, S., & Maity, S. (2013). Low cost obstacle avoidance robot. International Journal of Soft Computing and Engineering (IJSCE), 3(4), 52–55.Google Scholar
  10. 10.
    Kandylakis, Z., Karantzalos, K., Doulamis, A., & Karagiannidis L. (2017). Multimodal data fusion for effective surveillance of critical infrastructure. In Frontiers in spectral imaging and 3D technologies for geospatial solutions, 25–27 October 2017 (pp. 87–93). Jyväskylä, Finland.Google Scholar
  11. 11.
    Borenstein, J., Everett, H. R., Feng, L., & Wehe, D. (1997). Mobile robot positioning- sensors and techniques. Journal of Robotic Systems, 14(4), 231–249.CrossRefGoogle Scholar
  12. 12.
    Real-Moreno, O., Rodriguez-Quiňonez, J. C., Sergiyenko, O., Basaca-Preciado, L. C., Hemandez-Balbuena, D., Rivas-Lopez, M., & Flores-Fuentes, W. (2017). Accuracy improvement in 3D laser scanner based on dynamic triangulation for autonomous navigation system. In Industrial Electronics (ISIE). 2017 IEEE 26 th International Symposium on IEEE (pp. 1602–1608).CrossRefGoogle Scholar
  13. 13.
    Rodriguez-Quiňonez, J. C., Sergiyenko, O., Basaca-Preciado, L. C., Hemandez-Balbuena, D., Rivas-Lopez, M., Flores-Fuentes, W., & Basaca-Preciado, L. C. (2014). Improve 3D laser scanner measurements accuracy using a FFBP neural network with Widrow-Hoff weight/bias learning function. Opto-Electronics Review, 22(4), 224–235.CrossRefGoogle Scholar
  14. 14.
    Krasiuk, N. P., Koblov, V. L., & Krasiuk, V. N. (1988). Influence of the troposphere and underlying surface on radar. In Radio and communication (216 p). (in Russian).Google Scholar
  15. 15.
    Zubkovich, S. G. (1968). Statistical characteristics of radio signals reflected from the earth’s surface. In Sov radio (224 p). (in Russian).Google Scholar
  16. 16.
    Kulemin, G. P., & Razskazovsky, V. B. (1987). The scattering of millimeter radio waves by the earth at low angles. (Scientific thought) (232 p). (in Russian).Google Scholar
  17. 17.
    Lukianov, D. P., et al. (1981). Laser measuring system. In Radio and communication (456 p). (in Russian).Google Scholar
  18. 18.
    Skolnik, M. I. (1990). Radar handbook (846 p). New York: McGraw-Hill.Google Scholar
  19. 19.
    Grishin, J. P., Ignatov, V. D., Kazarinov, J. M., & Ulianitskiy, J. A. (1990). Radio engineering systems. In High school (496 p). (in Russian).Google Scholar
  20. 20.
    Rajyalakshmi, P., & Raju, G. S. N. (2011). Characteristics of radar cross section with different objects. International Journal of Electronics and Communication Engineering, 4(2), 205–216.Google Scholar
  21. 21.
    Shirman, J. D. (1970). Theoretical foundation of radar. Sov radio (560 p). (in Russian).Google Scholar
  22. 22.
    Sharma, S. N. (2008). A Kolmogorov-Fokker-Planck approach for a stochastic Duffing-van der pol system. Differential Equations and Dynamical Systems, 16, 351–377.MathSciNetCrossRefGoogle Scholar
  23. 23.
    Stratonovich, R. L. (1968). Conditional Markov process and their application to the theory of optimal control (367 p). Amsterdam: Elsevier.Google Scholar
  24. 24.
    Maltsev, A. A., & Silaev, A. V. (1985). Detection of jump-shaped parameter changes and optimal estimation of the state of discrete dynamic systems. Automation and Telemechanic, 45–58. in Russian.Google Scholar
  25. 25.
    Maltsev, A. A., & Silaev, A. V. (1989). Optimal estimation of moments of random jump changes of signal parameters. Radio Engineering and Electronics, 34(5), 1023–1033. in Russian.Google Scholar
  26. 26.
    Poliarus, O. V., Barchan, V. V., Poliakov, Y. O., & Koval, A. O. (2009). The optimal system for detecting and estimating the jumps of amplitudes of dynamic objects vibrations. East European Journal of Advanced Technology, 6/6(42), 21–23. (in Ukrainian).Google Scholar
  27. 27.
    Poliarus, O. V., Poliakov, Y. O., Nazarenko, I. L., Borovyk, Y. T., & Kondratiuk, M. V. (2018). Detection of jumps parameters in economic processes (on the example of modelling profitability). International Journal of Engineering & Technology, 7(4.3), 488–496.CrossRefGoogle Scholar
  28. 28.
    Poliarus, O. V., Poliakov, Y. O., & Lindner, L. (2018). Determination of landmarks by mobile robot’s vision system based on detecting abrupt changes of echo signals. In Proceedings of the 44 th Annual Conference of the IEEE Industrial Electronics Society (pp. 3165–3170). Washington, DC, USA.Google Scholar
  29. 29.
    Poliarus, O., Poliakov, Y., Sergiyenko, O., Tyrsa, V., Hernandez, W., & Nechitailo, Y. (2019). Azimuth estimation of landmarks by mobile autonomous robots using one scanning antenna. In Proceedings of IEEE 28 th International Symposium on Industrial Electronics (pp. 1682–1687). Vancouver, BC, Canada.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Oleksandr Poliarus
    • 1
  • Yevhen Poliakov
    • 1
  1. 1.Kharkiv National Automobile and Highway UniversityKharkivUkraine

Personalised recommendations