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Self-localization and obstacle avoidance for a mobile robot

  • ISNN 2008
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Abstract

This article presents a fast self-localization method based on ZigBee wireless sensor network and laser sensor, an obstacle avoidance algorithm based on ultrasonic sensors for a mobile robot. The positioning system and positioning theory of ZigBee which can obtain a rough global localization of the mobile robot are introduced. To realize accurate local positioning, a laser sensor is used to extract the features from environment, then the environmental features and global reference map can be matched. From the matched environmental features, the position and orientation of the mobile robot can be obtained. To enable the mobile robot to avoid obstacle in real-time, a heuristic fuzzy neural network is developed by using heuristic fuzzy rules and the Kohonen clustering network. The experiment results show the effectiveness of the proposed method.

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References

  1. Wang HB, Ishimatsu T (2004) Vision-based navigation for an electric wheelchair using ceiling light landmark. J Intell Robot Syst 41:283–314. doi:10.1007/s10846-005-9902-7

    Article  Google Scholar 

  2. Zhang L, Ghosh BK (2000) Line segment based map building and localization using 2D laser rangefinder. IEEE International Conference on Robotics and Automation, pp 2538–2543

  3. Wijk O, Christensen HI (2000) Localization and navigation of a mobile robot using natural point landmarks extracted from sonar data. Robot Auton Syst 31:31–42. doi:10.1016/S0921-8890(99)00085-8

    Article  Google Scholar 

  4. Zong GH, Deng LH, Wang W (2007) Robust localization algorithms for outdoor mobile robot. J Beijing Univ Aeronaut Astronaut 33(4):454–458

    Google Scholar 

  5. Arras KO, Castellanos JA, Siegwart R (2002) Feature based multi-hypothesis localization and tracking using geometric constraints. IEEE International Conference on Robotics and Automation, Washington DC, pp 1371–1377

    Google Scholar 

  6. Haehnel D, Schulz D, Burgard W (2002) Map building with mobile robots in populated environments. IEEE/RSJ International Conference on Intelligent Robots and Systems EPFL, Lausanne, Switzerland, October, pp 496–501

  7. Altermatt M, Martinelli A, Tomatis N, Siegwart R (2004) SLAM with corner features based on a relative map. Proceedings of international conference on intelligent robots and systems. Sendai, Japan, pp 1053–1058

    Google Scholar 

  8. Weber J, Franken L, Jorg KL, Puttkamer E (2000) APR global scan matching using anchor point relationships. The 6th international conference intelligent autonomous systems, Venice, Italy, pp 471–478

  9. Weber J, Franken L, Jorg KL, Puttkamer E (2002) Reference scan matching for global self-localization. Robot Auton Syst 40:99–110. doi:10.1016/S0921-8890(02)00235-X

    Article  Google Scholar 

  10. Wu QX, Bell DA (2002) Rough computational methods on reducing cost of computation in Markov localization for mobile robots. The 4th world congress on intelligent control and automation, pp 1226–1230

  11. Ueda R, Fukase T, Kobayashi Y (2002) Uniform Monte Carlo localization-fast and robust self-localization method for mobile robots. IEEE international conference on robotics and automation, Washington DC, May, pp 1353–1358

  12. Dellaert F, Fox D, Burgard W, Thrun S (1999) Monte Carlo localization for mobile robots. IEEE international conference on robotics and automation, Washington DC, May, pp 1322–1328

  13. Xu ZZ, Liang RH, Liu JL (2003) Global localization based on corner point. IEEE international symposium on computational intelligence in robotics and automation, Kobe, Japan, July, pp 843–847

  14. Baronti P, Pillai P Chook VWC (2007) Wireless sensor networks: a survey on the state of the art and the 802.15.4 and ZigBee standards. Computer Communications, pp 1655–1695

  15. Yang XP, Liu SY (2007) Mobile robot locating and tracking system design based on wireless sensor network. Chin J Electron Devices 30(6):2265–2268

    Google Scholar 

  16. Cang Y, Borenstein J (2002) Characterization of a 2-D laser scanner for mobile robot obstacle negotiation. IEEE international conference on robotics and automation, Washington DC, pp 2512–2518

  17. Chen JY, Wang SZ (2006) Integrated navigation fusion algorithm based on RBF neural network. J Data Acquis Process 21(2):198–202

    Google Scholar 

  18. Yang J, Sun H, Duan P (2005) Application of fuzzy neural networks in information fusion for obstacle avoidance. Tech Autom Appl 24(2):22–24

    Google Scholar 

  19. Ding CJ, Zhang ML, Subramanian D (2004) Application of fuzzy neural networks in information fusion for mobile robot. Control Theory Appl 21(1):59–62

    Google Scholar 

  20. Papadourakis MG, Tsagatakis G (2003) Applications of neural network to robotics. In: Proceedings of 2nd international workshop on embedded systems, internet programming and industrial IT. Kiel, Germany, pp 20–22

  21. Sowell T (1997) Fuzzy logic for just plain folks. Tesco Pub

  22. Wu H, Cong Y, Jiang GY, Wang HY (2005) Study on short-term prediction methods of traffic flow on expressway based on artificial neural network. In: Proceedings of 7th IASTED international conference on signal and image processing. Honolulu, Hawaii, USA, August, pp 479–488

  23. Mills DJ, Harris CJ (1995) Neurofuzzy modeling and control of a six degree of freedom AUV. Technical Report, University of Southampton

  24. Wang HB, Tian XB, Zhang HM, Huang Z (2008) Application of different mechanism in omni-directional mobile robot. Mach Des Res 2:25–26

    Google Scholar 

  25. Huntsberger T, Ajjimarangsee P (1990) Parallel self-organizing feature maps for unsupervised pattern recognition. Int J Gen Syst 16(4):357–372. doi:10.1080/03081079008935088

    Article  Google Scholar 

  26. Kohonen T (1997) Self-organizing maps. Springer, London

    MATH  Google Scholar 

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Acknowledgment

This work was supported by the Hebei Education Department foundation under Grant 2008149.

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Authors and Affiliations

  1. School of Mechanical Engineering, Yanshan University, 066004, Qinhuangdao, China

    Hongbo Wang & Ke Yu

  2. School of Information Science and Engineering, Yanshan University, 066004, Qinhuangdao, China

    Bingyi Mao

Authors
  1. Hongbo Wang
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  2. Ke Yu
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  3. Bingyi Mao
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Corresponding author

Correspondence to Hongbo Wang.

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Wang, H., Yu, K. & Mao, B. Self-localization and obstacle avoidance for a mobile robot. Neural Comput & Applic 18, 495–506 (2009). https://doi.org/10.1007/s00521-009-0247-1

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  • DOI: https://doi.org/10.1007/s00521-009-0247-1

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