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Indoor Mobile Robot Heading Detection Using MEMS Gyro North Finding Approach

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This paper presents a new approach for mobile robot heading detection using MEMS Gyro north finding method in the indoor environment. Based on this, the robot heading angle measurement scheme is proposed; improved north finding theory and algorithm are also explained. Several approaches are applied to confirm system’s precision and effectiveness. In order to find out the heading angle, a single axis MEMS gyroscope to sense the angle between the robot heading direction and the north is used. To reach enough estimation accuracy and reduce detection time,the least square method (LSM) for the signal fitting and parameter estimation is applied. Through a turn‐table, we setup a carouseling system to decrease the substantial bias effect on gyroscope’s heading angle. For the evaluation of the proposed method, this system is implemented to the Pioneer robot platform. The performance and heading error are analyzed after the test. From the simulation and experimental results, system’s accuracy, usefulness and adaptability are shown.

Abstract

This paper presents a new approach for mobile robot heading detection using MEMS Gyro north finding method in the indoor environment. Based on this, the robot heading angle measurement scheme is proposed; improved north finding theory and algorithm are also explained. Several approaches are applied to confirm system’s precision and effectiveness. In order to find out the heading angle, a single axis MEMS gyroscope to sense the angle between the robot heading direction and the north is used. To reach enough estimation accuracy and reduce detection time,the least square method (LSM) for the signal fitting and parameter estimation is applied. Through a turn‐table, we setup a carouseling system to decrease the substantial bias effect on gyroscope’s heading angle. For the evaluation of the proposed method, this system is implemented to the Pioneer robot platform. The performance and heading error are analyzed after the test. From the simulation and experimental results, system’s accuracy, usefulness and adaptability are shown.

참고문헌 (16)

  1. R. Siegwart, "Autonomou s Mobile Robots, Introduction" (2004), The MIT Press
  2. S. HERNA’ NDEZ, "A New Low Cost System for Autonomous Robot Heading and Position Localization in a Closed Area", Autonomous Robots, 15, pp.99~110 (2003)
  3. "Where am I? Sensors and Methods for Mobile Robot Positioning"[URL]
  4. Taeyeon Kim, "Indoor Navigation of Skid Steering Mobile Robot Using Ceiling Landmarks", IEEE International Symposium on Industrial Electronics (2009)
  5. Ching-Chih Tsai, "A Localization System of a Mobile Robot by FusingDead-Reckoning and Ultrasonic Measurements", IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 47(5) (1998)
  6. Lindsay Kleeman, "Optimal Estimation of Position and Heading for Mobile Robots Using Ultrasonic Beacons and Dead-reckoning", Proceedings of the 1992 IEEE International Conference on Robotics and Automation (1992)
  7. Hakyoung Chung, "Sensor fusion for Mobile Robot Dead-reckoningWith a Precision-calibrated Fiber Optic Gyroscope", Proceedings of the 2001 IEEEInternational Conference on Robotics 8 Automation (2001)
  8. Marc S. Weinberg, "Error Sources in In-Plane Silicon Tuning-Fork MEMS Gyroscopes", Journal of Microelectromechanical Systems (2006)
  9. Burgess R. Johnson, "Development of a MEMS Gyroscope forNorthfinding Applications" (2010), Honeywell International
  10. Lucian loan lozan, "North Finding System Using a MEMS Gyroscope", Symposium Gyro Technology (2010)
  11. "VTI MEMS Gyro website about north finding"[URL]
  12. B. M. Renkoski, "The effect of carouseling on MEMS IMU performance for gyrocompassing applications", 석사, Dept. of Aeronautics and Astronautics, pp.2008
  13. Li Denghua, "Research on two-position north-seeking method based on the Single axis FOG", Advanced Materials Research, 301 (2011)
  14. "Datasheet for VTI SCC1300-D02 MEMS Gyro package"[URL]
  15. "StephenButterworth, the Butterworth filter"[URL]
  16. "the Allan variance"[URL]

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