Smart Filter Design for the Localization of Robotic Fish Using MEMS Accelerometer

  • Tae Suk Yoo
  • Sang Cheol Lee
  • Sung Kyung Hong
  • Young Sun Ryuh
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 193)

Abstract

This paper presents the design of smart filter for the 2D localization of robotic fish using low-cost MEMS (Micro-Electro Mechanical System) accelerometer. The main purpose of the paper is to minimize the drift error that is inevitable in the double integration process in accelerometer-only navigation system. The proposed approach relies on two parts: 1) an effective calibration method to remove the major part of the deterministic sensor errors and, 2) a novel smart filtering scheme based on fuzzy-logic in order to accurately estimate a 2D position with an accelerometer triad. In addition, we compare the results of the fuzzy logic based on 2D position estimation system with simulation result from a conventional Kalman Filter.

Keywords

robotic fish 2D position estimation MEMS inertial sensor calibration fuzzy filter 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hegrenaes, O., Berglund, E., Hsllingstad, O.: Model-aided Inertial navigation for underwater vehicles. In: Proceedings of the IEEE International Conference on Robotic and Automation, PASADENA, pp. 1069–1076 (2008)Google Scholar
  2. 2.
    Yoo, T.S., Hong, S.K., Yoon, H.M., Park, S.S.: Gain scheduled complementary filter design for a MEMS based attitude and heading reference system, vol. 11, pp. 3816–3830 (2011)Google Scholar
  3. 3.
    Hong, S.K.: Fuzzy logic based closed-loop strapdown attitude system for unmanned aerial vehicle (UAV). Sens. Actuat. A-Phys. 107, 109–118 (2003)CrossRefGoogle Scholar
  4. 4.
    Grewel, M.S., Henderson, V.D., Miysako, R.S.: Application of Kalman Filtering to the Calibration and Alignment of Inertial Navigation Systems. IEEE Trans. Automatic Control 36, 3–13 (1991)CrossRefGoogle Scholar
  5. 5.
    Lauro, O., Giuloi, R., Daniel, C., Johann, B.: The FLEXnav Precision Dead-reckoning System. International Journal of Vehicle Autonomous System 4, 173–195 (2006)CrossRefGoogle Scholar
  6. 6.
    Miniature Attitude Heading Reference System (AHRS) with GPS, 3DM-GX3-35; Micro strain: 459 Hurricane Lane, Suite 102, Williston, VT 05495 USA (2011)Google Scholar
  7. 7.
    Win, T.L., Kalyana, C.V., Wei, T.A.: Drift-free position estimation of periodic or quasi-periodic motion using inertial sensors, vol. 11, pp. 5931–5951 (2011)Google Scholar
  8. 8.
    Park, M.H., Yang, G.: Error analysis and stochastic modeling of low-cost MEMS accelerometer. The Journal of Intelligent and Robotic Systems 46, 27–41 (2006)CrossRefGoogle Scholar
  9. 9.
    Hong, S.K.: A Fuzzy Logic based Performance Augmentation of MEMS Gyroscope. Journal of Intelligent & Fuzzy Systems 19(6), 393–398 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tae Suk Yoo
    • 1
  • Sang Cheol Lee
    • 2
  • Sung Kyung Hong
    • 2
  • Young Sun Ryuh
    • 3
  1. 1.LIG Nex1 CoSeoulKorea
  2. 2.Department of Aerospace EngineeringSejong UniversitySeoulKorea
  3. 3.Korea Institute of Industrial TechnologyCheonanKorea

Personalised recommendations