HOME JOURNALS CONTACT

Journal of Applied Sciences

Year: 2013 | Volume: 13 | Issue: 12 | Page No.: 2213-2216
DOI: 10.3923/jas.2013.2213.2216
Simulation Design of Robot Localization and Navigation System
Qiang Song and Lingxia Liu

Abstract: In order to effectively solve the problems of robot navigation, positioning and accumulative error correction, this study presents a kind of improved robot localization and navigation algorithm. Robot navigation is an important research field of robotics, it is the key of mobile robot to completely realize the independence of technology and accurate localization is the important problem which must be solved at first to complete navigation tasks. The experimental results show that the error of the improved method is smaller; therefore the formulation of robotic strategy route is more convenient and reliable.

Fulltext PDF

How to cite this article
Qiang Song and Lingxia Liu, 2013. Simulation Design of Robot Localization and Navigation System. Journal of Applied Sciences, 13: 2213-2216.

Keywords: Robot, obstacle avoidance, navigation system and localization algorithm

REFERENCES
  • Neda, Z., 2000. The sound of many bands clapping. Nature, 403: 849-850.

  • Olfati-Saber, R. and R.M. Murray, 2004. Consensus problems in networks of agents with switching topology and time-delays. IEEE Trans. Autom. Control, 49: 1520-1533.
    CrossRef    Direct Link    

  • Olfati-Saber, R., J.A. Fax and R.M. Murray, 2007. Consensus and cooperation in networked multi-agent systems. Proc. IEEE, 95: 215-233.
    CrossRef    Direct Link    

  • Ren, W., R.W. Beard and E.M. Atkins, 2005. A survey of consensus problems in multi-agent coordination. Proceedings of the American Control Conference, June 8-10, 2005, Baltimore, MD., pp: 1859-1864.

  • Lei, B., 2006. The research of multi mobile robot path planning in dynamic environment. Wuhan University of Technology.

  • Lei, B. and W. Li, 2007. A fuzzy behaviours fusion algorithm for mobile robot real-time path planning in unknown environment. Proceedings of the IEEE International Conference on Integration Technology, March 20-24, 2007, Shenzhen, pp: 173-177.

  • Yi, H. and B. Zhang, 2010. The graphical method of the path planning problem based on the topology. Robot, 12: 20-24.

  • Sun, Z., 2007. Intelligent Control Theory and Technology. Tsinghua University Press, Beijing

  • Khatib, O., 1986. Real-time obstacle avoidance for manipulators and mobile robots. Int. J. Robot. Res., 5: 90-98.
    CrossRef    Direct Link    

  • Yung, N.H.C. and Y. Cang, 1999. An intelligent mobile vehicle navigator based on fuzzy logic and reinforcement learning. IEEE Trans Syst. Man Cybernetics Part B: Cybernetic, 29: 314-320.
    CrossRef    

  • Li, S., 1999. Fuzzy Control, Nerve Control and Intelligent Control Theory. Harbin Industrial University Press, Harbin

  • Rui, Y., X. Jiang and X. Liu, 2007. Research on AS-R mobile robot path planning based on ant colony algorithm. Mech. Electrical Eng., 35: 73-75.

    • © Science Alert. All Rights Reserved