Cooperative localization of AUVs using moving horizon estimation

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.

[1]  Hanumant Singh,et al.  Advances in single-beacon one-way-travel-time acoustic navigation for underwater vehicles , 2012, Int. J. Robotics Res..

[2]  A. S. Gadre,et al.  OBSERVABILITY ANALYSIS IN NAVIGATION SYSTEMS WITH AN UNDERWATER VEHICLE APPLICATION , 2007 .

[3]  Gaurav S. Sukhatme,et al.  Observability analysis of relative localization for AUVs based on ranging and depth measurements , 2010, 2010 IEEE International Conference on Robotics and Automation.

[4]  Roland Siegwart,et al.  Observability analysis for mobile robot localization , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Hanumant Singh,et al.  Preliminary deep water results in single-beacon one-way-travel-time acoustic navigation for underwater vehicles , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Domenico D'Alessandro,et al.  Observability and Forward–Backward Observability of Discrete-Time Nonlinear Systems , 2002, Math. Control. Signals Syst..

[7]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[8]  Pere Ridao,et al.  SLAM using an Imaging Sonar for Partially Structured Underwater Environments , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Ling Chen,et al.  Underwater Localization and Environment Mapping Using Wireless Robots , 2013, Wirel. Pers. Commun..

[10]  T. Keviczky,et al.  A Distributed Moving Horizon Estimator for Mobile Robot Localization Problems , 2011 .

[11]  Huijun Gao,et al.  Finite-Horizon $H_{\infty} $ Filtering With Missing Measurements and Quantization Effects , 2013, IEEE Transactions on Automatic Control.

[12]  Stefano Carpin,et al.  The unconstrained and inequality constrained moving horizon approach to robot localization , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Wenyao Xu,et al.  An efficient algorithm for mobile localization in sensor networks , 2012, Int. J. Autom. Comput..

[14]  Gianluca Antonelli,et al.  Designing behaviors to improve observability for relative localization of AUVs , 2010, 2010 IEEE International Conference on Robotics and Automation.

[15]  Stergios I. Roumeliotis,et al.  Observability-based consistent EKF estimators for multi-robot cooperative localization , 2011, Auton. Robots.

[16]  Pere Ridao Rodriguez,et al.  MSISpIC: A Probabilistic Scan Matching Algorithm Using a Mechanical Scanned Imaging Sonar , 2009 .

[17]  Daniel J. Stilwell,et al.  Underwater navigation in the presence of unknown currents based on range measurements from a single location , 2005, Proceedings of the 2005, American Control Conference, 2005..

[18]  David Q. Mayne,et al.  Constrained state estimation for nonlinear discrete-time systems: stability and moving horizon approximations , 2003, IEEE Trans. Autom. Control..

[19]  John J. Leonard,et al.  Cooperative AUV Navigation using a Single Maneuvering Surface Craft , 2010, Int. J. Robotics Res..

[20]  Dongbing Gu,et al.  Mobile sensor networks for modelling environmental pollutant distribution , 2011, Int. J. Syst. Sci..

[21]  Huosheng Hu,et al.  Towards autonomous localization and mapping of AUVs: a survey , 2013 .

[22]  John J. Leonard,et al.  Cooperative localization of marine vehicles using nonlinear state estimation , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[23]  P HuangGuoquan,et al.  Observability-based consistent EKF estimators for multi-robot cooperative localization , 2011 .

[24]  Zhe Chen Local observability and its application to multiple measurement estimation , 1991 .