Covariance Inflation Method In Ros Based Mobile Robot Navigation - Ezzah Naziha, Roslim (2022)
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.