Developing a real time navigation for the mobile robots at unknown environments

Abstract

Mobile robot needs to navigate at unknown environments and constructing its maps at the same time. Therefore, we proposed to use an algorithm named simultaneous localization and mapping (SLAM). Then, we suggested the extended kalman filter algorithm (EKF) to solve the SLAM problem which is implemented at different unknown environments containing a different number of landmarks where the detectable landmarks will play an important role in controlling the overall navigation process and on EKF-SLAM technique's performance. MATLAB simulation results show that the performance of EKF-SLAM path is enhanced as the number of landmarks increased, so the performance becomes better as compared with an odometry path depending on the value of mean square error. After that, we simulated mobile robot platform named TurtleBot2e in Gazebo simulator to achieve the SLAM algorithm for different environments based on G-mapping algorithm which was built on robot operating system (ROS). The main contribution that comes with this work is the simulation of SLAM technique is done by using two different software platforms separately (MATLAB and ROS). Finally, the execution time to build a map is computed for each environment in Gazebo simulator, and we concluded that it is increased when the landmarks are increased.