Covariance Inflation Method In Ros Based Mobile Robot Navigation

Mobile robot research has risen tremendously, because of their ability in movements, With the highly dynamic environment for robot applications, there has been an increasing demand on mobile robot movement and capabilities of robot motions. In any areas, robotics is an important field of study that...

Full description

Bibliographic Details
Main Author: Ezzah Naziha, Roslim
Format: Undergraduates Project Papers
Language:English
Published: 2022
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/39939/
http://umpir.ump.edu.my/id/eprint/39939/1/EC18065_EzzahNaziha_ThesisV2%20-%20Ezzah%20naziha.pdf
_version_ 1848825908196016128
author Ezzah Naziha, Roslim
author_facet Ezzah Naziha, Roslim
author_sort Ezzah Naziha, Roslim
building UMP Institutional Repository
collection Online Access
description Mobile robot research has risen tremendously, because of their ability in movements, With the highly dynamic environment for robot applications, there has been an increasing demand on mobile robot movement and capabilities of robot motions. In any areas, robotics is an important field of study that applies knowledge from a variety of professions, including mechanics, electronics, and engineering in order to make the robot move in a way that the user desired with addition of a degree of an autonomy. This thesis presented the investigation of state covariance mobile robot with different condition where it illustrates the important of cross-correlation in the case of mobile robot localization. This thesis deals with the Extended Kalman Filter (EKF) as an alternative technique to overcome issues in mobile robot especially in term of localization. of robot, the mobile robot should manage to map and has their own path planning. Moreover, the algorithm of SLAM (Simultaneous Localization and Mapping) is also used together in order to generate map while locate itself in one environment. Therefore, by considering the method used is covariance inflation, it focuses more on decorrelating week subsets to the state covariance is proved to have better performance while reducing computational cost. Then, this thesis also considers the ROS software for the detection and avoidance of obstacles. In mobile robots, navigation is a difficult problem. A mobile robot is compulsory to recognize its specific position and orientation in either a known or unfamiliar environment in order to move and perform its activities. The result has been recorded and analyze for future recommendation and support our theoretical study.
first_indexed 2025-11-15T03:36:24Z
format Undergraduates Project Papers
id ump-39939
institution Universiti Malaysia Pahang
institution_category Local University
language English
last_indexed 2025-11-15T03:36:24Z
publishDate 2022
recordtype eprints
repository_type Digital Repository
spelling ump-399392024-01-09T08:13:48Z http://umpir.ump.edu.my/id/eprint/39939/ Covariance Inflation Method In Ros Based Mobile Robot Navigation Ezzah Naziha, Roslim TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Mobile robot research has risen tremendously, because of their ability in movements, With the highly dynamic environment for robot applications, there has been an increasing demand on mobile robot movement and capabilities of robot motions. In any areas, robotics is an important field of study that applies knowledge from a variety of professions, including mechanics, electronics, and engineering in order to make the robot move in a way that the user desired with addition of a degree of an autonomy. This thesis presented the investigation of state covariance mobile robot with different condition where it illustrates the important of cross-correlation in the case of mobile robot localization. This thesis deals with the Extended Kalman Filter (EKF) as an alternative technique to overcome issues in mobile robot especially in term of localization. of robot, the mobile robot should manage to map and has their own path planning. Moreover, the algorithm of SLAM (Simultaneous Localization and Mapping) is also used together in order to generate map while locate itself in one environment. Therefore, by considering the method used is covariance inflation, it focuses more on decorrelating week subsets to the state covariance is proved to have better performance while reducing computational cost. Then, this thesis also considers the ROS software for the detection and avoidance of obstacles. In mobile robots, navigation is a difficult problem. A mobile robot is compulsory to recognize its specific position and orientation in either a known or unfamiliar environment in order to move and perform its activities. The result has been recorded and analyze for future recommendation and support our theoretical study. 2022-02 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39939/1/EC18065_EzzahNaziha_ThesisV2%20-%20Ezzah%20naziha.pdf Ezzah Naziha, Roslim (2022) Covariance Inflation Method In Ros Based Mobile Robot Navigation. College of Engineering, Universiti Malaysia Pahang Al-Sultan Abdullah.
spellingShingle TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Ezzah Naziha, Roslim
Covariance Inflation Method In Ros Based Mobile Robot Navigation
title Covariance Inflation Method In Ros Based Mobile Robot Navigation
title_full Covariance Inflation Method In Ros Based Mobile Robot Navigation
title_fullStr Covariance Inflation Method In Ros Based Mobile Robot Navigation
title_full_unstemmed Covariance Inflation Method In Ros Based Mobile Robot Navigation
title_short Covariance Inflation Method In Ros Based Mobile Robot Navigation
title_sort covariance inflation method in ros based mobile robot navigation
topic TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
url http://umpir.ump.edu.my/id/eprint/39939/
http://umpir.ump.edu.my/id/eprint/39939/1/EC18065_EzzahNaziha_ThesisV2%20-%20Ezzah%20naziha.pdf