Cooperative Localization for Autonomous Underwater Vehicles in Strong Ocean Currents

MISSING IMAGE

Material Information

Title:
Cooperative Localization for Autonomous Underwater Vehicles in Strong Ocean Currents
Physical Description:
1 online resource (86 p.)
Language:
english
Creator:
Song, Zhuoyuan
Publisher:
University of Florida
Place of Publication:
Gainesville, Fla.
Publication Date:

Thesis/Dissertation Information

Degree:
Master's ( M.S.)
Degree Grantor:
University of Florida
Degree Disciplines:
Mechanical Engineering, Mechanical and Aerospace Engineering
Committee Chair:
MOHSENI,KAMRAN
Committee Co-Chair:
CRANE,CARL D,III
Committee Members:
BAROOAH,PRABIR

Subjects

Subjects / Keywords:
auv -- localization -- robotics
Mechanical and Aerospace Engineering -- Dissertations, Academic -- UF
Genre:
Mechanical Engineering thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract:
Unavailability of GPS (global positioning system) for underwater navigation has created significant challenges for operation and localization of autonomous underwater vehicles (AUVs). This is more pronounced in dynamic ocean flows where significant background flows exist. In this thesis, a collaborative underwater localization hierarchy is introduced to improve the cooperative performance of a small AUV swarm by utilizing vehicles with bounded localization error as moving references in the presence of dominating background flows. Initially represented in probability theory, the problem is then decomposed into a cooperative localization problem and a dynamic simultaneous localization and mapping problem with moving features. To address the incomplete covariance updating issue, which arises when directly applying the extended Kalman filter in fully distributed systems, the modified extended Kalman filter (MEKF) is proposed and a MEKF based algorithm is discussed in detail. A particle filter based algorithm is implemented for comparative purposes due to its advantages in modeling multimodal non-Gaussian distributions. However, it is shown that the particle filter requires greater computational effort than the MEKF when the number of vehicles is small. Both proposed algorithms are verified in three-dimensional background flow simulations. The divergent behavior of localization error, which appears when using solely cooperative localization, is avoided through the implementation of either the MEKF algorithm or the particle algorithm. Significant decreases in localization error are subsequently observed.
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility:
by Zhuoyuan Song.
Thesis:
Thesis (M.S.)--University of Florida, 2014.
Local:
Adviser: MOHSENI,KAMRAN.
Local:
Co-adviser: CRANE,CARL D,III.
Electronic Access:
RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2015-05-31

Record Information

Source Institution:
UFRGP
Rights Management:
Applicable rights reserved.
Classification:
lcc - LD1780 2014
System ID:
UFE0046805:00001