Hierarchical self organizing map and focusing inspection strategy for mobile robot novelty detection

Novelty detection is a process of recognizing changes based on learned knowledge. In this research, a novelty detection system was implemented on a mobile robot with an array of sonar sensors for surveillance application. In order to perform novelty detection, a map that stores normal information w...

Full description

Saved in:

Bibliographic Details
Main Author:	Sha'abani, Mohd Nurul Al-Hafiz
Format:	Thesis
Language:	English English
Published:	2014
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://eprints.utem.edu.my/id/eprint/14987/1/Hierarchical%20Self%20Organizing%20Map%20and%20Focusing%20Inpection%20Strategy%20for%20Mobile%20Robot%20Novelty%20Detection%2024pages.pdf http://eprints.utem.edu.my/id/eprint/14987/2/Hierarchical%20self%20organizing%20map%20and%20focusing%20inspection%20strategy%20for%20mobile%20robot%20novelty%20detection.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utem-ep.14987
record_format	uketd_dc
spelling	my-utem-ep.149872022-04-19T09:09:07Z Hierarchical self organizing map and focusing inspection strategy for mobile robot novelty detection 2014 Sha'abani, Mohd Nurul Al-Hafiz TJ Mechanical engineering and machinery Novelty detection is a process of recognizing changes based on learned knowledge. In this research, a novelty detection system was implemented on a mobile robot with an array of sonar sensors for surveillance application. In order to perform novelty detection, a map that stores normal information with respect to any particular robot pose in an environment is required. The map is needed to detect changes and determine the position of novel event. The challenges of mobile novelty detection system are that the false positive rate is usually high whereas the true positive rate is usually low due to mapping and monitoring problems. During mapping, errors due to robot localization and sensor measurement can reduce the quality of the map built. However, available methods in mapping assume perfect localization, hence error in localization is not taken into account in the process of mapping. During monitoring, inspection interval that is too small will consume a lot of time and energy but if the interval is too big, novelty could be missed, hence lower the true positive detection. On top of that, low true positive detection is also caused by the low reliability of sonar sensor measurement. Thus, the objective of this thesis is to utilize mobile novelty detection system by developing a mapping and monitoring strategy that has low false positive detection, high true positive detection and able to estimate the position of a novelty. This thesis proposed two methods regarding to mapping and monitoring process; a hierarchical Self Organizing Map (SOM) and a Focusing Inspection Strategy (FIS). Unlike other mapping methods, hierarchical SOM also consider localization error when associating the normal information with respect to the robot pose. FIS is a multi resolution monitoring strategy which works by changing the frequency of measurement depending on the detection of anomaly. In this thesis, two models were considered; a step (FS) and linear (FL) resolution models. The hierarchical SOM was validated by using simulation and experimentation of the inspection in environment with normal and novel event. False positive rate is measured to determine the map performance. The results show that hierarchical SOM is able to map the normal condition of the environment very well. The inspection results show the false positive rate occurred less than 0.1 at the higher sensitivity setting of 0.9 in either normal or novel condition. The performance of FIS was investigated by using experimentation of the inspection of novel objects of different sizes. The results show that by changing the frequency of measurement using the FS and FL models, the number of true positive detection increases up to 80% when compared to inspection with fix measurement frequency. FIS also reduced the error of position estimation by about 8.8% and 10.9% each for FS and FL and maintained the false positive rate lower than 0.1. 2014 Thesis http://eprints.utem.edu.my/id/eprint/14987/ http://eprints.utem.edu.my/id/eprint/14987/1/Hierarchical%20Self%20Organizing%20Map%20and%20Focusing%20Inpection%20Strategy%20for%20Mobile%20Robot%20Novelty%20Detection%2024pages.pdf text en public http://eprints.utem.edu.my/id/eprint/14987/2/Hierarchical%20self%20organizing%20map%20and%20focusing%20inspection%20strategy%20for%20mobile%20robot%20novelty%20detection.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=92053 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Electrical Engineering
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery Sha'abani, Mohd Nurul Al-Hafiz Hierarchical self organizing map and focusing inspection strategy for mobile robot novelty detection
description	Novelty detection is a process of recognizing changes based on learned knowledge. In this research, a novelty detection system was implemented on a mobile robot with an array of sonar sensors for surveillance application. In order to perform novelty detection, a map that stores normal information with respect to any particular robot pose in an environment is required. The map is needed to detect changes and determine the position of novel event. The challenges of mobile novelty detection system are that the false positive rate is usually high whereas the true positive rate is usually low due to mapping and monitoring problems. During mapping, errors due to robot localization and sensor measurement can reduce the quality of the map built. However, available methods in mapping assume perfect localization, hence error in localization is not taken into account in the process of mapping. During monitoring, inspection interval that is too small will consume a lot of time and energy but if the interval is too big, novelty could be missed, hence lower the true positive detection. On top of that, low true positive detection is also caused by the low reliability of sonar sensor measurement. Thus, the objective of this thesis is to utilize mobile novelty detection system by developing a mapping and monitoring strategy that has low false positive detection, high true positive detection and able to estimate the position of a novelty. This thesis proposed two methods regarding to mapping and monitoring process; a hierarchical Self Organizing Map (SOM) and a Focusing Inspection Strategy (FIS). Unlike other mapping methods, hierarchical SOM also consider localization error when associating the normal information with respect to the robot pose. FIS is a multi resolution monitoring strategy which works by changing the frequency of measurement depending on the detection of anomaly. In this thesis, two models were considered; a step (FS) and linear (FL) resolution models. The hierarchical SOM was validated by using simulation and experimentation of the inspection in environment with normal and novel event. False positive rate is measured to determine the map performance. The results show that hierarchical SOM is able to map the normal condition of the environment very well. The inspection results show the false positive rate occurred less than 0.1 at the higher sensitivity setting of 0.9 in either normal or novel condition. The performance of FIS was investigated by using experimentation of the inspection of novel objects of different sizes. The results show that by changing the frequency of measurement using the FS and FL models, the number of true positive detection increases up to 80% when compared to inspection with fix measurement frequency. FIS also reduced the error of position estimation by about 8.8% and 10.9% each for FS and FL and maintained the false positive rate lower than 0.1.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Sha'abani, Mohd Nurul Al-Hafiz
author_facet	Sha'abani, Mohd Nurul Al-Hafiz
author_sort	Sha'abani, Mohd Nurul Al-Hafiz
title	Hierarchical self organizing map and focusing inspection strategy for mobile robot novelty detection
title_short	Hierarchical self organizing map and focusing inspection strategy for mobile robot novelty detection
title_full	Hierarchical self organizing map and focusing inspection strategy for mobile robot novelty detection
title_fullStr	Hierarchical self organizing map and focusing inspection strategy for mobile robot novelty detection
title_full_unstemmed	Hierarchical self organizing map and focusing inspection strategy for mobile robot novelty detection
title_sort	hierarchical self organizing map and focusing inspection strategy for mobile robot novelty detection
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Electrical Engineering
publishDate	2014
url	http://eprints.utem.edu.my/id/eprint/14987/1/Hierarchical%20Self%20Organizing%20Map%20and%20Focusing%20Inpection%20Strategy%20for%20Mobile%20Robot%20Novelty%20Detection%2024pages.pdf http://eprints.utem.edu.my/id/eprint/14987/2/Hierarchical%20self%20organizing%20map%20and%20focusing%20inspection%20strategy%20for%20mobile%20robot%20novelty%20detection.pdf
_version_	1747833855759351808

Hierarchical self organizing map and focusing inspection strategy for mobile robot novelty detection

Similar Items