A solution to partial observability in extended Kalman Filter mobile robot navigation

Partial observability in EKF based mobile robot navigation is investigated in this paper to find a solution that can prevent erroneous estimation. By only considering certain landmarks in an environment, the computational cost in mobile robot can be reduced but with an increase of uncertainties to t...

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Main Authors:	Hamzah, Ahmad, Nur Aqilah, Othman, Mohd Syakirin, Ramli
Format:	Article
Language:	English
Published:	Universitas Ahmad Dahlan 2018
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/21076/ http://umpir.ump.edu.my/id/eprint/21076/ http://umpir.ump.edu.my/id/eprint/21076/ http://umpir.ump.edu.my/id/eprint/21076/1/A%20solution%20to%20partial%20observability%20in%20extended%20kalman%20filter%20mobile%20robot%20navigation.pdf

id	ump-21076
recordtype	eprints
spelling	ump-210762018-08-28T09:01:05Z http://umpir.ump.edu.my/id/eprint/21076/ A solution to partial observability in extended Kalman Filter mobile robot navigation Hamzah, Ahmad Nur Aqilah, Othman Mohd Syakirin, Ramli TK Electrical engineering. Electronics Nuclear engineering Partial observability in EKF based mobile robot navigation is investigated in this paper to find a solution that can prevent erroneous estimation. By only considering certain landmarks in an environment, the computational cost in mobile robot can be reduced but with an increase of uncertainties to the system. This is known as suboptimal condition of the system. Fuzzy Logic technique is proposed to ensure that the estimation achieved desired performance even though some of the landmarks were excluded for references. The Fuzzy Logic is applied to the measurement innovation of Kalman Filter to correct the positions of both mobile robot and any observed landmarks during observations. The simulation results shown that the proposed method is capable to secure reliable estimation results even a number of landmarks being excluded from Kalman Filter update process in both Gaussian and non-Gaussian noise conditions. Universitas Ahmad Dahlan 2018-02 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/21076/1/A%20solution%20to%20partial%20observability%20in%20extended%20kalman%20filter%20mobile%20robot%20navigation.pdf Hamzah, Ahmad and Nur Aqilah, Othman and Mohd Syakirin, Ramli (2018) A solution to partial observability in extended Kalman Filter mobile robot navigation. Telkomnika (Telecommunication Computing Electronics and Control), 16 (1). pp. 134-141. ISSN 1693-6930 http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/9025/pdf_588 10.12928/TELKOMNIKA.v16i1.9025
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Hamzah, Ahmad Nur Aqilah, Othman Mohd Syakirin, Ramli A solution to partial observability in extended Kalman Filter mobile robot navigation
description	Partial observability in EKF based mobile robot navigation is investigated in this paper to find a solution that can prevent erroneous estimation. By only considering certain landmarks in an environment, the computational cost in mobile robot can be reduced but with an increase of uncertainties to the system. This is known as suboptimal condition of the system. Fuzzy Logic technique is proposed to ensure that the estimation achieved desired performance even though some of the landmarks were excluded for references. The Fuzzy Logic is applied to the measurement innovation of Kalman Filter to correct the positions of both mobile robot and any observed landmarks during observations. The simulation results shown that the proposed method is capable to secure reliable estimation results even a number of landmarks being excluded from Kalman Filter update process in both Gaussian and non-Gaussian noise conditions.
format	Article
author	Hamzah, Ahmad Nur Aqilah, Othman Mohd Syakirin, Ramli
author_facet	Hamzah, Ahmad Nur Aqilah, Othman Mohd Syakirin, Ramli
author_sort	Hamzah, Ahmad
title	A solution to partial observability in extended Kalman Filter mobile robot navigation
title_short	A solution to partial observability in extended Kalman Filter mobile robot navigation
title_full	A solution to partial observability in extended Kalman Filter mobile robot navigation
title_fullStr	A solution to partial observability in extended Kalman Filter mobile robot navigation
title_full_unstemmed	A solution to partial observability in extended Kalman Filter mobile robot navigation
title_sort	solution to partial observability in extended kalman filter mobile robot navigation
publisher	Universitas Ahmad Dahlan
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/21076/ http://umpir.ump.edu.my/id/eprint/21076/ http://umpir.ump.edu.my/id/eprint/21076/ http://umpir.ump.edu.my/id/eprint/21076/1/A%20solution%20to%20partial%20observability%20in%20extended%20kalman%20filter%20mobile%20robot%20navigation.pdf
first_indexed	2023-09-18T22:30:47Z
last_indexed	2023-09-18T22:30:47Z
_version_	1777416271189508096

A solution to partial observability in extended Kalman Filter mobile robot navigation

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