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

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Aerospace Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2020

Author dc:creator

• Hascaryo, Rodra Wikan

Contributors dc:contributor

• Norris, William R.
• Tran, Huy T.

• Extended Kalman Filter
• Vehicle Dynamics
• Autonomous Underwater Vehicle
• Unmanned Underwater Vehicle
• Marine Vehicle Dynamics
• Kalman Filter

Statement dc:rights

• Copyright 2019 Rodra Hascaryo

Language dc:language

en

Handle dc:identifier

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

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/106250