A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search
Volume 1 Issue 1
Jan.  2014

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2014  >  1(1): 68-76
Sen Wang, Ling Chen, Dongbing Gu and Huosheng Hu, "Cooperative Localization of AUVs Using Moving Horizon Estimation," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 1, pp. 68-76, 2014.
Citation: Sen Wang, Ling Chen, Dongbing Gu and Huosheng Hu, "Cooperative Localization of AUVs Using Moving Horizon Estimation," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 1, pp. 68-76, 2014.
shu

Cooperative Localization of AUVs Using Moving Horizon Estimation

  • School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK

Funds:

This work was supported by British Council: Sino-UK Higher Education Research Partnership for Ph. D. Studies and EU FP7-PEOPLE-2012-IRSES, ECROBOT: European and Chinese Platform for Robotics and Applications.

    Abstract
  • This paper studies the localization problem of autonomous underwater vehicles (AUVs) constrained by limited size, power and payload. Such AUVs cannot be equipped with heavy sensors which makes their underwater localization problem difficult. The proposed cooperative localization algorithm is performed by using a single surface mobile beacon which provides range measurement to bound the localization error. The main contribution of this paper is twofold: 1) The observability of single beacon based localization is first analyzed in the context of nonlinear discrete time system, deriving a sufficient condition on observability. It is further compared with observability of linearized system to verify that a nonlinear state estimation is necessary. 2) Moving horizon estimation is integrated with extended Kalman filter (EKF) for three dimensional localization using single beacon, which can alleviate the computational complexity, impose various constraints and make use of several previous range measurements for each estimation. The observability and improved localization accuracy of the localization algorithm are verified by extensive numerical simulation compared with EKF.

     

    • FullText(HTML)
  • loading
    • References(23)
  • [1]
    Chen L, Wang S, McDonald-Maier K, Hu H S. Towards autonomous localization and mapping of AUVs:a survey. International Journal of Intelligent Unmanned Systems, 2013, 1(2):97-120
    [2]
    Papadopoulos G, Fallon M F, Leonard J J, Patrikalakis N M. Cooperative localization of marine vehicles using nonlinear state estimation. In:Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. Taipei, China:IEEE, 2010. 4874-4879
    [3]
    Fallon M F, Papadopoulos G, Leonard J J, Patrikalakis N M. Cooperative AUV navigation using a single maneuvering surface craft. The International Journal of Robotics Research, 2010, 29(12):1461-1474
    [4]
    Webster S E, Eustice R M, Singh H, Whitcomb L L. Preliminary deep water results in single-beacon one-way-travel-time acoustic navigation for underwater vehicles. In:Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. St. Louis, USA:IEEE, 2009. 2053-2060
    [5]
    Webster S E, Eustice R M, Singh H, Whitcomb L L. Advances in singlebeacon one-way-travel-time acoustic navigation for underwater vehicles. In:Proceedings of the 2010 IEEE/OES Autonomous Underwater Vehicles. Monterey, CA:IEEE, 2010. 1-8
    [6]
    Lu B W, Oyekan J, Gu D B, Hu H S, Nia H F G. Mobile sensor networks for modelling environmental pollutant distribution. International Journal of Systems Science, 2011, 42(9):1491-1505
    [7]
    Wang Z D, Dong H L, Shen B, Gao H J. Finite-horizon H∞ filtering with missing measurements and quantization effects. IEEE Transactions on Automatic Control, 2013, 58(7):1707-1718
    [8]
    Simonetto A, Balzaretti D, Keviczky T. A distributed moving horizon estimator for mobile robot localization problems. In:Proceedings of the 18th IFAC World Congress. Milan, Italy:IFAC, 2011. 8902-8907
    [9]
    Pillonetto G, Aravkin A, Carpin S. The unconstrained and inequality constrained moving horizon approach to robot localization. In:Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. Taipei, China:IEEE, 2009. 3830-3835
    [10]
    Gadre A S, Stilwell D J. A complete solution to underwater navigation in the presence of unknown currents based on range measurements from a single location. In:Proceedings of the 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. Edmonton, Canada:IEEE, 2005. 1420-1425
    [11]
    Gadre A. Observability Analysis in Navigation Systems with an Underwater Vehicle Application[Ph. D. dissertation], Virginia Polytechnic Institute and State University, Virginia, 2007
    [12]
    Huang G P, Trawny N, Mourikis A I, Roumeliotis S I. Observabilitybased consistent EKF estimators for multi-robot cooperative localization. Autonomous Robots, 2011, 30(1):99-122
    [13]
    Martinelli A, Siegwart R. Observability analysis for mobile robot localization. In:Proceedings of the 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. Edmonton, Canada:IEEE, 2005. 1471-1476
    [14]
    Antonelli G, Arrichiello F, Chiaverini S, Sukhatme G. Observability analysis of relative localization for AUVs based on ranging and depth measurements. In:Proceedings of the 2010 IEEE International Conference on Robotics and Automation. Alaska, USA:IEEE, 2010. 4276-4281
    [15]
    Xu X M, Chen Y, Xu W Y, Gong F. An efficient algorithm for mobile localization in sensor networks. International Journal of Automation and Computing, 2012, 9(6):594-599
    [16]
    Wang S, Chen L, Hu H S, Xue Z B, Pan W. Underwater localization and environment mapping using wireless robots. Wireless Personal Communications, 2013, 70(3):1147-1170
    [17]
    Hernández E, Ridao P, Ribas D, Batlle J. MSISPIC:a probabilistic scan matching algorithm using a mechanical scanned imaging sonar. Journal of Physical Agents, 2009, 3(1):3-12
    [18]
    Ribas D, Ridao P, Neira J, Tardos J. SLAM using an imaging sonar for partially structured underwater environments. In:Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. Beijing, China:IEEE, 2006. 5040-5045
    [19]
    Albertini F, DAlessandro D. Observability and forward-backward observability of discrete-time nonlinear systems. Mathematics of Control, Signals and Systems, 2002, 15(4):275-290
    [20]
    Antonelli G, Caiti A, Calabro V, Chiaverini S. Designing behaviors to improve observability for relative localization of AUVs. In:Proceedings of the 2010 IEEE International Conference on Robotics and Automation. Alaska, USA:IEEE, 2010. 4270-4275
    [21]
    Chen Z. Local observability and its application to multiple measurement estimation. IEEE Transactions on Industrial Electronics, 1991, 38(6):491-496
    [22]
    Hartley R, Zisserman A. Multiple View Geometry in Computer Vision. Cambridge:Cambridge University Press, 2000
    [23]
    Rao C V, Rawlings J B, Mayne D Q. Constrained state estimation for nonlinear discrete-time systems:stability and moving horizon approximations. IEEE Transactions on Automatic Control, 2003, 48(2):246-258
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article views (1320) PDF downloads(37) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return