Title:
Anomaly detection based on the estimation of speed and flow mapping for controlled Lagrangian particles
Anomaly detection based on the estimation of speed and flow mapping for controlled Lagrangian particles
Authors
Cho, Sungjin
Authors
Advisors
Zhang, Fumin
Edwards, Catherine R.
Edwards, Catherine R.
Advisors
Person
Associated Organizations
Organizational Unit
Organizational Unit
Series
Collections
Supplementary to
Permanent Link
Abstract
The main contribution of this dissertation is a set of algorithms that detect anomaly of autonomous underwater vehicles (AUVs) without sensors monitoring vehicle components. Only using trajectory information, the proposed strategy detects abnormal vehicle motion under unknown ocean flow. It has the potential for mitigating abnormal vehicle motion with path-planning and controller design of AUVs. The experimental results of the Georgia Tech Miniature Autonomous Blimp (GT-MAB) and Georgia Tech Wind Measuring Robot (GT WMR) in an indoor test bed verify the proposed strategy.
Sponsor
Date Issued
2017-11-13
Extent
Resource Type
Text
Resource Subtype
Dissertation