Kalman filter implementation on localization of mobile robot
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 key in order to operate autonomously, which commonly known as localization. Localization of mobile robot are commonly aff...
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ump-16308 |
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eprints |
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ump-163082017-01-24T03:49:04Z http://umpir.ump.edu.my/id/eprint/16308/ Kalman filter implementation on localization of mobile robot Nabil Zhafri, Mohd Nasir T Technology (General) 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 key in order 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 uses 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. 2016-06 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/16308/1/Kalman%20filter%20implementation%20on%20localization%20of%20mobile%20robot-Table%20of%20contents-FKP-Nabil%20Zhafri%20Mohd%20Nasir-CD%2010417.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/16308/2/Kalman%20filter%20implementation%20on%20localization%20of%20mobile%20robot-Abstract-FKP-Nabil%20Zhafri%20Mohd%20Nasir-CD%2010417.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/16308/3/Kalman%20filter%20implementation%20on%20localization%20of%20mobile%20robot-Chapter%201-FKP-Nabil%20Zhafri%20Mohd%20Nasir-CD%2010417.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/16308/4/Kalman%20filter%20implementation%20on%20localization%20of%20mobile%20robot-References-FKP-Nabil%20Zhafri%20Mohd%20Nasir-CD%2010417.pdf Nabil Zhafri, Mohd Nasir (2016) Kalman filter implementation on localization of mobile robot. Faculty of Manufacturing Engineering, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:98398&theme=UMP2 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Universiti Malaysia Pahang |
| building |
UMP Institutional Repository |
| collection |
Online Access |
| language |
English English English English |
| topic |
T Technology (General) TS Manufactures |
| spellingShingle |
T Technology (General) TS Manufactures Nabil Zhafri, Mohd Nasir Kalman filter implementation on localization of mobile robot |
| 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 key in order 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 uses 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 |
Undergraduates Project Papers |
| author |
Nabil Zhafri, Mohd Nasir |
| author_facet |
Nabil Zhafri, Mohd Nasir |
| author_sort |
Nabil Zhafri, Mohd Nasir |
| title |
Kalman filter implementation on localization of mobile robot |
| title_short |
Kalman filter implementation on localization of mobile robot |
| title_full |
Kalman filter implementation on localization of mobile robot |
| title_fullStr |
Kalman filter implementation on localization of mobile robot |
| title_full_unstemmed |
Kalman filter implementation on localization of mobile robot |
| title_sort |
kalman filter implementation on localization of mobile robot |
| publishDate |
2016 |
| url |
http://umpir.ump.edu.my/id/eprint/16308/ http://umpir.ump.edu.my/id/eprint/16308/ http://umpir.ump.edu.my/id/eprint/16308/1/Kalman%20filter%20implementation%20on%20localization%20of%20mobile%20robot-Table%20of%20contents-FKP-Nabil%20Zhafri%20Mohd%20Nasir-CD%2010417.pdf http://umpir.ump.edu.my/id/eprint/16308/2/Kalman%20filter%20implementation%20on%20localization%20of%20mobile%20robot-Abstract-FKP-Nabil%20Zhafri%20Mohd%20Nasir-CD%2010417.pdf http://umpir.ump.edu.my/id/eprint/16308/3/Kalman%20filter%20implementation%20on%20localization%20of%20mobile%20robot-Chapter%201-FKP-Nabil%20Zhafri%20Mohd%20Nasir-CD%2010417.pdf http://umpir.ump.edu.my/id/eprint/16308/4/Kalman%20filter%20implementation%20on%20localization%20of%20mobile%20robot-References-FKP-Nabil%20Zhafri%20Mohd%20Nasir-CD%2010417.pdf |
| first_indexed |
2023-09-18T22:21:51Z |
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2023-09-18T22:21:51Z |
| _version_ |
1777415708904259584 |