<![CDATA[Localization in an autonomous mobile robot allows it to operate autonomously in an unknown and unpredictable environment with the ability to determine its position and heading. Simultaneous localization and mapping (SLAM) are introduced to solve the problem where no prior in formation about the environment is available either static or dynamic to achieve standard map-based localization. The primary focus of this research is autonomous mobile robot navigation using the extended Kalman filter ( EKF )-SLAM environment modeling tech nique which provides higher accuracy and reliability in mobile robot localization and mapping result s . In this paper , EKF-SLAM performance is verified by simulations performed in a static and dynamic environment designed in V-REP i.e. , 3D Robot simulation environment. In this work SLAM problem of two-wheeled differential drive robot i.e. , Pioneer 3-DX in indoor static and dynamic environment integrated with Laser range finder i.e. , H okuyo URG-04LX-UG01, LIDAR , and Ultrasonic sensors is solved. EKF-SLAM scripts are developed using MATLAB that is linked to V-REP via r emote API f eature to evaluate EKF-SLAM performance. The reached results confirm the EKF-SLAM is a reliable approach for r eal-time autonomous navigation for mobile robots in comparison to other techniques.]]>
Copyrights © 2021
