Enhancing navigation accuracy of Turtlebot3 Burger mobile robot
This project aims to identify the initial covariance value of a ROS-based mobile robot, specifically the Turtlebot3 Burger. The basic navigation of the robot requires a significant amount of data and resources to process the output path. To address this challenge, the Kalman Filter algorithm is impl...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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Springer
2024
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| Online Access: | https://umpir.ump.edu.my/id/eprint/45279/ |
| _version_ | 1848827373210828800 |
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| author | Muhammad Haniff, Gusrial Muhammad Luqman Hakim, Abdullah Nur Aqilah, Othman Hamzah, Ahmad |
| author_facet | Muhammad Haniff, Gusrial Muhammad Luqman Hakim, Abdullah Nur Aqilah, Othman Hamzah, Ahmad |
| author_sort | Muhammad Haniff, Gusrial |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | This project aims to identify the initial covariance value of a ROS-based mobile robot, specifically the Turtlebot3 Burger. The basic navigation of the robot requires a significant amount of data and resources to process the output path. To address this challenge, the Kalman Filter algorithm is implemented in this robot, as it is widely used for mobile robot navigation and system integration. One crucial parameter for implementing the Kalman Filter is the covariance matrix, which needs to be determined. Understanding the specifications of the robot is essential for programming and operating it effectively. The system model of this robot is developed based on the kinematic model of a two-wheeled mobile robot. To execute this project, an experimental setup consisting of a laptop and a robot, serving as the ROS Master and ROS Slave respectively, is required. Furthermore, the project aims to comprehend the function and efficiency of the robot's performance, including the LiDAR sensor, Inertial Measurement Unit (IMU) sensor, and Odometry sensor. These sensors are mounted on the robot to achieve accurate localization. An indoor experiment was conducted to determine the covariance value. Different sources of sensor information are fused into a single representational format called sensor fusion. By using an extended Kalman filter (EKF), data from Odometry and IMU sensors were combined to estimate the position and orientation of the mobile robot. The value 0.014405 represent x, y and z for position covariance matrix and value 0.004974 represent roll, pitch and yaw for orientation covariance matrix. This identified covariance value will serve as the initial covariance matrix for the implementation of the Kalman Filter-based system using this robot. The experimental results indicate that the proposed method is suitable and practical for real-world applications. |
| first_indexed | 2025-11-15T03:59:41Z |
| format | Conference or Workshop Item |
| id | ump-45279 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:59:41Z |
| publishDate | 2024 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-452792025-08-07T01:07:27Z https://umpir.ump.edu.my/id/eprint/45279/ Enhancing navigation accuracy of Turtlebot3 Burger mobile robot Muhammad Haniff, Gusrial Muhammad Luqman Hakim, Abdullah Nur Aqilah, Othman Hamzah, Ahmad T Technology (General) TK Electrical engineering. Electronics Nuclear engineering This project aims to identify the initial covariance value of a ROS-based mobile robot, specifically the Turtlebot3 Burger. The basic navigation of the robot requires a significant amount of data and resources to process the output path. To address this challenge, the Kalman Filter algorithm is implemented in this robot, as it is widely used for mobile robot navigation and system integration. One crucial parameter for implementing the Kalman Filter is the covariance matrix, which needs to be determined. Understanding the specifications of the robot is essential for programming and operating it effectively. The system model of this robot is developed based on the kinematic model of a two-wheeled mobile robot. To execute this project, an experimental setup consisting of a laptop and a robot, serving as the ROS Master and ROS Slave respectively, is required. Furthermore, the project aims to comprehend the function and efficiency of the robot's performance, including the LiDAR sensor, Inertial Measurement Unit (IMU) sensor, and Odometry sensor. These sensors are mounted on the robot to achieve accurate localization. An indoor experiment was conducted to determine the covariance value. Different sources of sensor information are fused into a single representational format called sensor fusion. By using an extended Kalman filter (EKF), data from Odometry and IMU sensors were combined to estimate the position and orientation of the mobile robot. The value 0.014405 represent x, y and z for position covariance matrix and value 0.004974 represent roll, pitch and yaw for orientation covariance matrix. This identified covariance value will serve as the initial covariance matrix for the implementation of the Kalman Filter-based system using this robot. The experimental results indicate that the proposed method is suitable and practical for real-world applications. Springer 2024-09 Conference or Workshop Item PeerReviewed pdf en https://umpir.ump.edu.my/id/eprint/45279/1/Enhancing%20navigation%20accuracy%20of%20Turtlebot3%20Burger%20mobile%20robot.pdf Muhammad Haniff, Gusrial and Muhammad Luqman Hakim, Abdullah and Nur Aqilah, Othman and Hamzah, Ahmad (2024) Enhancing navigation accuracy of Turtlebot3 Burger mobile robot. In: Proceedings of the 7th International Conference on Electrical, Control and Computer Engineering—Volume 2. Lecture Notes in Electrical Engineering; 7th International Conference on Electrical, Control, and Computer Engineering, InECCE 2023 , 22 - 22 August 2023 , Kuala Lumpur. pp. 525 -542., 1213. ISBN 978-981973850-2 (Published) https://doi.org/10.1007/978-981-97-3851-9_45 |
| spellingShingle | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Muhammad Haniff, Gusrial Muhammad Luqman Hakim, Abdullah Nur Aqilah, Othman Hamzah, Ahmad Enhancing navigation accuracy of Turtlebot3 Burger mobile robot |
| title | Enhancing navigation accuracy of Turtlebot3 Burger mobile robot |
| title_full | Enhancing navigation accuracy of Turtlebot3 Burger mobile robot |
| title_fullStr | Enhancing navigation accuracy of Turtlebot3 Burger mobile robot |
| title_full_unstemmed | Enhancing navigation accuracy of Turtlebot3 Burger mobile robot |
| title_short | Enhancing navigation accuracy of Turtlebot3 Burger mobile robot |
| title_sort | enhancing navigation accuracy of turtlebot3 burger mobile robot |
| topic | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | https://umpir.ump.edu.my/id/eprint/45279/ https://umpir.ump.edu.my/id/eprint/45279/ |