Hybrid fastslam approach using genetic algorithm and particle swarm optimization for robotic path planning

Simultaneous Localization and Mapping (SLAM) is an algorithmic technique being used for mobile robot to build and create a relative map in an unknown environment. FastSLAM is one of the SLAM algorithms, which is capable of speeding up convergence in robot’s path planning and environment map estimati...

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Main Author:	Khairuddin, Alif Ridzuan
Format:	Thesis
Language:	English
Published:	2016
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/78454/1/AlifRidzuanKhairuddinMFC2017.pdf
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spelling	my-utm-ep.784542018-08-26T11:56:26Z Hybrid fastslam approach using genetic algorithm and particle swarm optimization for robotic path planning 2016-10 Khairuddin, Alif Ridzuan QA75 Electronic computers. Computer science Simultaneous Localization and Mapping (SLAM) is an algorithmic technique being used for mobile robot to build and create a relative map in an unknown environment. FastSLAM is one of the SLAM algorithms, which is capable of speeding up convergence in robot’s path planning and environment map estimation. Besides, it is popular for its higher accuracy compared to other SLAM algorithms. However, the FastSLAM algorithm suffers from inconsistent results due to particle depletion problem over time. This research study aims to minimize the inconsistency in FastSLAM algorithm using two soft computing techniques, which are particle swarm optimization (PSO) and genetic algorithm (GA). To achieve this goal, a new hybrid approach based on the mentioned soft computing techniques is developed and integrated into the FastSLAM algorithm to improve its consistency. GA is used to optimize particle weight while PSO is used to optimize the robot’s estimation in generating an environment map to minimize particle depletion in FastSLAM algorithm. The performance of the proposed hybrid approach is evaluated using root mean square error (RMSE) analysis to measure degree of error during estimation of robot and landmark position. The results are verified using margin error analysis. With the percentage error analysis results, the new hybrid approach is able to minimize the problems in FastSLAM algorithm and managed to reduce the errors up to 33.373% for robot position and 27.482% for landmark set position. 2016-10 Thesis http://eprints.utm.my/id/eprint/78454/ http://eprints.utm.my/id/eprint/78454/1/AlifRidzuanKhairuddinMFC2017.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:109000 phd doctoral Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Khairuddin, Alif Ridzuan Hybrid fastslam approach using genetic algorithm and particle swarm optimization for robotic path planning
description	Simultaneous Localization and Mapping (SLAM) is an algorithmic technique being used for mobile robot to build and create a relative map in an unknown environment. FastSLAM is one of the SLAM algorithms, which is capable of speeding up convergence in robot’s path planning and environment map estimation. Besides, it is popular for its higher accuracy compared to other SLAM algorithms. However, the FastSLAM algorithm suffers from inconsistent results due to particle depletion problem over time. This research study aims to minimize the inconsistency in FastSLAM algorithm using two soft computing techniques, which are particle swarm optimization (PSO) and genetic algorithm (GA). To achieve this goal, a new hybrid approach based on the mentioned soft computing techniques is developed and integrated into the FastSLAM algorithm to improve its consistency. GA is used to optimize particle weight while PSO is used to optimize the robot’s estimation in generating an environment map to minimize particle depletion in FastSLAM algorithm. The performance of the proposed hybrid approach is evaluated using root mean square error (RMSE) analysis to measure degree of error during estimation of robot and landmark position. The results are verified using margin error analysis. With the percentage error analysis results, the new hybrid approach is able to minimize the problems in FastSLAM algorithm and managed to reduce the errors up to 33.373% for robot position and 27.482% for landmark set position.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Khairuddin, Alif Ridzuan
author_facet	Khairuddin, Alif Ridzuan
author_sort	Khairuddin, Alif Ridzuan
title	Hybrid fastslam approach using genetic algorithm and particle swarm optimization for robotic path planning
title_short	Hybrid fastslam approach using genetic algorithm and particle swarm optimization for robotic path planning
title_full	Hybrid fastslam approach using genetic algorithm and particle swarm optimization for robotic path planning
title_fullStr	Hybrid fastslam approach using genetic algorithm and particle swarm optimization for robotic path planning
title_full_unstemmed	Hybrid fastslam approach using genetic algorithm and particle swarm optimization for robotic path planning
title_sort	hybrid fastslam approach using genetic algorithm and particle swarm optimization for robotic path planning
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2016
url	http://eprints.utm.my/id/eprint/78454/1/AlifRidzuanKhairuddinMFC2017.pdf
_version_	1747817992945664000

Hybrid fastslam approach using genetic algorithm and particle swarm optimization for robotic path planning

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