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2017 | 24 | Special Issue S3 |
Tytuł artykułu

Low cost integrated navigation system for unmanned vessel

Autorzy
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Large errors of low-cost MEMS inertial measurement unit (MIMU) lead to huge navigation errors, even wrong navigation information. An integrated navigation system for unmanned vessel is proposed. It consists of a low-cost MIMU and Doppler velocity sonar (DVS). This paper presents an integrated navigation method, to improve the performance of navigation system. The integrated navigation system is tested using simulation and semi-physical simulation experiments, whose results show that attitude, velocity and position accuracy has improved awfully, giving exactly accurate navigation results. By means of the combination of low-cost MIMU and DVS, the proposed system is able to overcome fast drift problems of the low cost IMU
Słowa kluczowe
EN
Wydawca
-
Rocznik
Tom
24
Opis fizyczny
p.110-115,fig.,ref.
Twórcy
autor
  • School of Information and Control, Nanjing University of Information Science and Technology, Nanjing, 210044, China
autor
  • School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Bibliografia
  • 1. J. M. Daly, M. J. Tribou and S. L. Waslander, 2012. A nonlinear path following controller for an underactuated unmanned surface vessel. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vilamoura. pp. 82-87.
  • 2. M. H. Ghani, L. R. Hole, Ilker Fer, etal., 2014. The SailBuoy remotely-controlled unmanned vessel: Measurements of near surface temperature, salinity and oxygen concentration in the Northern Gulf of Mexico. Methods in Oceanography, (10), 104-121.
  • 3. P. W. Pritchett, 2015. Ghost Vessels: Why the Law Should Embrace Unmanned Vessel Technology. Tulane Maritime Law Journal, 40(1), 197.
  • 4. Y. Man, M. Lundh, T. Porathe, etal., 2015. From desk to field - Human factor issues in remote monitoring and controlling of autonomous unmanned vessels. Procedia Manufacturing, (3), 2674-2681.
  • 5. J. M. Larrazabal, M. S. Peñas, 2016. Intelligent rudder control of an unmanned surface vessel. Expert Systems with Applications, (55), 106–117.
  • 6. L. Zhang, Z. Xiong, J. Lai, etal., 2016. Optical flowaided navigation for UAV: A novel information fusion of integrated MEMS navigation system. Optik, (127), 447-451.
  • 7. M. Morgado, P. Oliveira, and C. Silvestre, 2010. Design and experimental evaluation of an integrated USBL/INS system for AUVs. In: IEEE International Conference on Robotics and Automation, Alaska, pp.4264-4269.
  • 8. Y. Geng, R. Martins, J. Sousa, 2010. Accuracy Analysis of DVL/IMU/Magnetometer Integrated Navigation System using Different IMUs in AUV. In: IEEE International Conference on Control and Automation, Xiamen, pp. 516-521.
  • 9. C. Eling, L. Klingbeil and H. Kuhlmann, 2015. RealTime Single-Frequency GPS/MEMS-IMU Attitude Determination of Lightweight UAVs. Sensors, (15): 26212-26235.
  • 10. Y. Qin, 2006. Inertial navigation. Science Press, Beijing, 355-361.
  • 11. M. S. Grewal, A. P. Andrews, and C. G. Bartone, 2013. Global navigation satellite systems, inertial navigation, and integration, Third edition. John Wiley & Sons, Inc., Hoboken, New Jersey.
  • 12. V. Awale, H. B. Hablani, 2015. Fusion of Redundant Aided-inertial Sensors with Decentralised Kalman Filter for Autonomous Underwater Vehicle Navigation. Defence Science Journal, 65(6), 425-430.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.agro-2ca2d0d0-b369-4936-a5a4-33dbaee97b06
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