Autonomous Mobile Robot Localization using Kalman Filter

Autonomous mobile robot field has gain interest among researchers in recent years. The ability of a mobile robot to locate its current position and surrounding environment is the fundamental in order for it to operate autonomously, which commonly known as localization. Localization of mobile robot a...

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Main Authors:	Nabil Zhafri, Mohd Nasir, Muhammad Aizzat, Zakaria, Saifudin, Razali, Mohd Yazid, Abu
Format:	Conference or Workshop Item
Language:	English
Published:	EDP Sciences 2017
Subjects:	TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/18974/ http://umpir.ump.edu.my/id/eprint/18974/ http://umpir.ump.edu.my/id/eprint/18974/1/fkp-2017-aizzat-Autonomous%20Mobile%20Robot%20Localization.pdf

id	ump-18974
recordtype	eprints
spelling	ump-189742018-02-27T07:58:05Z http://umpir.ump.edu.my/id/eprint/18974/ Autonomous Mobile Robot Localization using Kalman Filter Nabil Zhafri, Mohd Nasir Muhammad Aizzat, Zakaria Saifudin, Razali Mohd Yazid, Abu TS Manufactures Autonomous mobile robot field has gain interest among researchers in recent years. The ability of a mobile robot to locate its current position and surrounding environment is the fundamental in order for it to operate autonomously, which commonly known as localization. Localization of mobile robot are commonly affected by the inaccuracy of the sensors. These inaccuracies are caused by various factors which includes internal interferences of the sensor and external environment noises. In order to overcome these noises, a filtering method is required in order to improve the mobile robot’s localization. In this research, a 2- wheeled-drive (2WD) mobile robot will be used as platform. The odometers, inertial measurement unit (IMU), and ultrasonic sensors are used for data collection. Data collected is processed using Kalman filter to predict and correct the error from these sensors reading. The differential drive model and measurement model which estimates the environmental noises and predict a correction are used in this research. Based on the simulation and experimental results, the x, y and heading was corrected by converging the error to10 mm, 10 mm and 0.06 rad respectively. EDP Sciences 2017 Conference or Workshop Item PeerReviewed application/pdf en cc_by http://umpir.ump.edu.my/id/eprint/18974/1/fkp-2017-aizzat-Autonomous%20Mobile%20Robot%20Localization.pdf Nabil Zhafri, Mohd Nasir and Muhammad Aizzat, Zakaria and Saifudin, Razali and Mohd Yazid, Abu (2017) Autonomous Mobile Robot Localization using Kalman Filter. In: MATEC Web of Conferences: The 2nd International Conference on Automotive Innovation and Green Vehicle (AiGEV 2016), 2-3 August 2016 , Malaysia Automotive Institute, Cyberjaya, Selangor. pp. 1-9., 90 (01069). ISSN 2261-236X https://doi.org/10.1051/matecconf/20179001069
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TS Manufactures
spellingShingle	TS Manufactures Nabil Zhafri, Mohd Nasir Muhammad Aizzat, Zakaria Saifudin, Razali Mohd Yazid, Abu Autonomous Mobile Robot Localization using Kalman Filter
description	Autonomous mobile robot field has gain interest among researchers in recent years. The ability of a mobile robot to locate its current position and surrounding environment is the fundamental in order for it to operate autonomously, which commonly known as localization. Localization of mobile robot are commonly affected by the inaccuracy of the sensors. These inaccuracies are caused by various factors which includes internal interferences of the sensor and external environment noises. In order to overcome these noises, a filtering method is required in order to improve the mobile robot’s localization. In this research, a 2- wheeled-drive (2WD) mobile robot will be used as platform. The odometers, inertial measurement unit (IMU), and ultrasonic sensors are used for data collection. Data collected is processed using Kalman filter to predict and correct the error from these sensors reading. The differential drive model and measurement model which estimates the environmental noises and predict a correction are used in this research. Based on the simulation and experimental results, the x, y and heading was corrected by converging the error to10 mm, 10 mm and 0.06 rad respectively.
format	Conference or Workshop Item
author	Nabil Zhafri, Mohd Nasir Muhammad Aizzat, Zakaria Saifudin, Razali Mohd Yazid, Abu
author_facet	Nabil Zhafri, Mohd Nasir Muhammad Aizzat, Zakaria Saifudin, Razali Mohd Yazid, Abu
author_sort	Nabil Zhafri, Mohd Nasir
title	Autonomous Mobile Robot Localization using Kalman Filter
title_short	Autonomous Mobile Robot Localization using Kalman Filter
title_full	Autonomous Mobile Robot Localization using Kalman Filter
title_fullStr	Autonomous Mobile Robot Localization using Kalman Filter
title_full_unstemmed	Autonomous Mobile Robot Localization using Kalman Filter
title_sort	autonomous mobile robot localization using kalman filter
publisher	EDP Sciences
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/18974/ http://umpir.ump.edu.my/id/eprint/18974/ http://umpir.ump.edu.my/id/eprint/18974/1/fkp-2017-aizzat-Autonomous%20Mobile%20Robot%20Localization.pdf
first_indexed	2023-09-18T22:27:08Z
last_indexed	2023-09-18T22:27:08Z
_version_	1777416041835528192

Autonomous Mobile Robot Localization using Kalman Filter

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