Comparative Studies of Robot Navigation

  • Zhan XuEmail author
  • Anxin Zhao
  • Bo Zhai
  • Anyi Wang
  • Lina Zhang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)


At present, robot intelligence is an important direction for the development of robots. The intelligent robot is a highly self-planning, self-organizing and adaptive robot suitable for working in complex unstructured environments. Navigation technology is the core of intelligent robot research, and it is also the key technology for robots to achieve intelligence. Intelligent mobile robots can work safely and effectively only by knowing their own position, the position of obstacles in the work space, and the movement of obstacles. Therefore, the problem of navigation and positioning of the robot is particularly important. The main contents of robot navigation technology research include: navigation method, positioning method and multi-sensor information fusion technology research. This paper will discuss the robot navigation technology from these aspects, introduce the development and application of navigation technology, and finally forecast the development trend of robot navigation technology in the future.


Navigation Positioning method Multi-sensor information fusion 



The project supported by National Key R&D Program of China (Program No. 2018YFC0808301) and Key Research and Development Program of Shaanxi (Program No. 2019GY-107).


  1. 1.
    Wei, G., Bo, C.D.: The behavior planning method of the “Yutu” patrol device on the No. 3. J. Beijing Univ. Aeronaut. Astronaut. 43(02), 277–284 (2017)Google Scholar
  2. 2.
    Boley, D.L., Sutherland, K.T.: A rapidly converging recursive method for mobile robot localization. Int. J. Robot. Res. 17(10), 1021–1093 (1998)CrossRefGoogle Scholar
  3. 3.
    Rigatos, G.G.: Extended Kalman and particle filtering for sensor fusion in motion control. Math. Comput. Simul. 81(3), 590–607 (2010)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Aini, F.R.Q., Jati, A.N., Sunarya, U.: A study of Monte Carlo localization on robot operating system. In: International Conference on Information Technology Systems and Innovation 2017, pp. 1–13. IEEE, Indonesia (2017)Google Scholar
  5. 5.
    Khoshelham, K., Elberink, S.O.: Accuracy and resolution of Kinect depth data for indoor mapping applications. Sensors 12(2), 1437 (2012)CrossRefGoogle Scholar
  6. 6.
    Zhang, T., Zhu, Y., Song, J.: Real-time motion planning for mobile robots by means of artificial potential field method in unknown environment. Ind. Robot. 34(4), 384–400 (2010)CrossRefGoogle Scholar
  7. 7.
    Yu, H., Zhu, J., Wang, Y.: Obstacle classification and 3D measurement in unstructured environments based on ToF cameras. Sensors 14(6), 10753–10782 (2014)CrossRefGoogle Scholar
  8. 8.
    Na, Y., Li, H.: Navigation of service robot: a review of recent developments. Journal of Mechanical & Electrical Engineering 32(12), 1641–1648 (2015)Google Scholar
  9. 9.
    Moller, R., Vardy, A., Kreft, S.: Visual homing in environments with anisotropic landmark distribution. Auton. Robots 23(3), 231–245 (2007)CrossRefGoogle Scholar
  10. 10.
    Chiang, C.-H.: Robot navigation in dynamic environments based on fuzzy controller and the A Algorithm. J. Comput. (Taiwan) 29(01), 21–39 (2018)Google Scholar
  11. 11.
    Wang, C., Sun, W., Dexu, B., ZhouBo, Z.: A method of simultaneous positioning and map creation for duct cleaning robot based on inertial navigation and stereo vision. J. Mech. Eng. 49(23), 56–57 (2013)Google Scholar
  12. 12.
    Algabri, M., Mathkour, H., Ramdane, H., Alsulaiman, M.: Comparative study of soft computing techniques for mobile robot navigation in an unknown environment. Comput. Hum. Behav. 10(16), 42–56 (2015)CrossRefGoogle Scholar
  13. 13.
    Zhang, S., ZhaoBo, Z.: An experimental study on the positioning of wheeled mobile robot based on Kalman filter. Electromech. Eng. Technol. 39(02), 44–84 (2010)CrossRefGoogle Scholar
  14. 14.
    Liu, J., Hao, J.: Research on navigation and positioning technology of autonomous mobile robot. Sens. World 01(01), 23–26 (2005)Google Scholar
  15. 15.
    Boley, D.L., Sutherland, K.T.: A rapidly converging recursive method for mobile robot localization. Int. J. Robot. Res. 17(10), 1027–1039 (1998)CrossRefGoogle Scholar
  16. 16.
    Akyildiz, I.F., Su, W., Sankarasubramanian, Y., Cayirei, E.: A survey on sensor networks. IEEE Commun. Mag. 40(08), 102–114 (2002)CrossRefGoogle Scholar
  17. 17.
    DeSouza, G.N., Kak, A.C.: Vision for mobile robot navigation: a survey. IEEE Trans. PAMI 24(02), 237–266 (2002)CrossRefGoogle Scholar
  18. 18.
    Li, S., Hayashi, A.: Robot navigation in outdoor environments by using GPS information and panoramic views, pp. 570–575 (1998)Google Scholar
  19. 19.
    Diego, L.D.I., Mendonca, P.R.S., Hopper, A.: TRIP: a low-cost vision-based location system for ubiquitous computing. Pers. Ubiquitous Comput. 06(03), 206–219 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zhan Xu
    • 1
    Email author
  • Anxin Zhao
    • 1
  • Bo Zhai
    • 2
  • Anyi Wang
    • 1
  • Lina Zhang
    • 1
  1. 1.Xi’an University of Science and TechnologyXi’anChina
  2. 2.Shandong Energy Zibo Mining Group Co., Ltd.JinanChina

Personalised recommendations