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Position Estimation of Mobile Robots using Multiple Active Sensors with Network

  • Jin, Tae-Seok (Dept. of Mechatronics Engineering, Dongseo University)
  • Received : 2011.07.07
  • Accepted : 2011.11.07
  • Published : 2011.12.25

Abstract

Recently, with the development of service robots and the concept of ubiquitous, the position estimation of mobile objects has received great interest. Some of the localization schemes are introduced, which provide the relative location of the moving objects subjected to accumulated errors. To implement a real time localization system, a new absolute position estimation method for a mobile robot in indoor environment is proposed. Design and implementation of the localization system comes from the usage of active beacon systems (based upon RFID technology). The active beacon system is composed of an RFID receiver and an ultra-sonic transmitter. The RFID receiver gets the synchronization signal from the mobile robot and the ultra-sonic transmitter sends out the traveling signal to be used for measuring the distance. Position of a mobile robot in a three dimensional space can be calculated basically from the distance information from three beacons and the absolute position information of the beacons themselves. In some case, the mobile robot can acquire the ultrasonic signals from only one or two beacons, due to the obstacles located along the moving path. In this paper, a position estimation scheme using fewer than three sensors is developed. Also, the extended Kalman filter algorithm is applied for the improvement of position estimation accuracy of the mobile robot.

Keywords

References

  1. A. Galstyan, B. Krishnamachari, K. Lerman, and S. Pattern, "Distributed online localization in sensor networks using a moving target," in Proc. Int. Symp. Information Processing Sensor Networks (IPSN), pp. 61-70, 2004.
  2. J. Borenstein and L. Feng, "UMBmark-A Method for Measuring, Comparing, and Correcting Dead-reckoning Errors in Mobile Robots," The University of Michigan, Technical Report UM-MEAM-94-22, December, 1994.
  3. Iowa State University GPS page. Web site at http://www.cnde.iastate.edu/gps.html.
  4. T. Arai and E. Nakano, "Development of measuring equipment for location and direction (MELODI) using ultrasonic waves," Trans. ASME, Journal of dynamic systems, Measurement and control, vol 105, pp. 152-156, 1983. https://doi.org/10.1115/1.3140649
  5. L. Kleeman, "Optimal estimation of position and heading for mobile robots using ultrasonic beacons and Dead-reckoning", Proc. Of IEEE int. Conf. on Robotics and Automaion, pp. 2582-2587, 1992.
  6. Z. Wang; E. Bulut, B.K. Szymanski, "Distributed Target Tracking with Directional Binary Sensor Networks" 2009 IEEE Global Telecommunications Conference, pp. 1-6, 2009
  7. S. M. Bozic, "Digital and Kalman Filtering," Edward Arnold, 1979.
  8. Greg Welch and Gary Bishop, "An Introduction to the Kalman Filter," 2004.
  9. D. Fox, W. Burgard and S. Thrun, "The dynamic window approach to collision avoidance", IEEE Robotics and Automation Magazine, pp. 23-33, March, 1997.
  10. Soo-Yeong Yi, Jae-Ho Jin, "Self-localization of a Mobile Robot using Global Ultrasonic Sensor System," Journal of Control, Automation and systems Engineering, vol. 9, no 2, pp. 145-151, 2003. https://doi.org/10.5302/J.ICROS.2003.9.2.145
  11. A. So and Y. Ye, "Theory of semidefinite programming for sensor network localization," Mathematical Programming, vol. 109, no. 2, pp. 367-384, 2007. https://doi.org/10.1007/s10107-006-0040-1
  12. C. Taylor, A. Rahimi, J. Bachrach, H. Shrobe, and A. Grue, "Simultaneous localization, calibration, and tracking in an ad hoc sensor network," Proc. of IPSN, pp. 27-33, 2006.
  13. S. Sarkka, A. Vehtari, and 1. Lampinen, "Rao-Blackwellized particle filter for multiple target tracking," Information Fusion, vol. 8, no. I, pp. 2-15, 2007. https://doi.org/10.1016/j.inffus.2005.09.009

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