University of Illinois Urbana-Champaign

Reduced order extended kalman filter incorporating dynamics of an autonomous underwater vehicle for motion prediction

Hascaryo, Rodra Wikan

Content Files
HASCARYO-THESIS-2019.pdf

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https://hdl.handle.net/2142/106250

Description

Title
Reduced order extended kalman filter incorporating dynamics of an autonomous underwater vehicle for motion prediction
Author(s)
Hascaryo, Rodra Wikan
Issue Date
2019-12-09
Director of Research (if dissertation) or Advisor (if thesis)
  • Norris, William R.
  • Tran, Huy T.
Department of Study
Aerospace Engineering
Discipline
Aerospace Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2020-03-02T21:58:26Z
Keyword(s)
  • Extended Kalman Filter
  • Vehicle Dynamics
  • Autonomous Underwater Vehicle
  • Unmanned Underwater Vehicle
  • Marine Vehicle Dynamics
  • Kalman Filter
Abstract
Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs) are used for a wide variety of missions such as exploration and scientific research. One key challenge for these autonomous vehicles is the creation of a reliable motion prediction. Kinematic Extended Kalman Filters (EKF) have been applied for AUV motion prediction in the context of Simultaneous Localization and Mapping (SLAM) [1]. It has been suggested that a dynamics based EKF would produce more accurate predictions as it considers forces acting on the AUV. Presented in this thesis is an motion prediction EKF for AUVs using a simplified dynamic model. First, the dynamic model is presented and then the simplification process is shown. The filter was implemented with a simulator vehicle in an open-source marine vehicle simulator called UUV Simulator and the results were tested against those obtained through dead reckoning. Results show good predictions, although there are improvements needed before the EKF could be used on manned operational system.
Graduation Semester
2019-12
Type of Resource
text
Permalink
http://hdl.handle.net/2142/106250
Copyright and License Information
Copyright 2019 Rodra Hascaryo

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