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Navigation of an Underwater Remotely Operated Vehicle Based on Extended Kalman Filter

[+] Author Affiliations
Blanca V. Martínez, Daniel A. Sierra, Rodolfo Villamizar

Universidad Industrial de Santander, Bucaramanga, Santander, Colombia

Paper No. DSCC2013-3933, pp. V003T45A004; 9 pages
doi:10.1115/DSCC2013-3933
From:
  • ASME 2013 Dynamic Systems and Control Conference
  • Volume 3: Nonlinear Estimation and Control; Optimization and Optimal Control; Piezoelectric Actuation and Nanoscale Control; Robotics and Manipulators; Sensing; System Identification (Estimation for Automotive Applications, Modeling, Therapeutic Control in Bio-Systems); Variable Structure/Sliding-Mode Control; Vehicles and Human Robotics; Vehicle Dynamics and Control; Vehicle Path Planning and Collision Avoidance; Vibrational and Mechanical Systems; Wind Energy Systems and Control
  • Palo Alto, California, USA, October 21–23, 2013
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5614-7
  • Copyright © 2013 by ASME

abstract

An algorithm to estimate positions, orientations, linear velocities and angular rates of an Underwater Remotely Operated Vehicle (UROV), based on the Extended Kalman Filter (EKF), is presented. The complete UROV kinematic and dynamic models are combined to obtain the process equation, and measurements correspond to linear accelerations and angular rates provided by an Inertial Measurement Unit (IMU). The proposed algorithm is numerically validated and its results are compared with simulated UROV states. A discussion about the influence of the covariance matrices on the estimation error and overall filter performance is also included. As a conclusion, the proposed algorithm estimates properly the UROV linear velocities and angular rates from IMU measurements, and the noise in estimated states is reduced in about one order of magnitude.

Copyright © 2013 by ASME

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