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Go to Editorial ManagerThis paper presents a study of a nonholonomic differential drive wheeled mobile robot (WMR) of the type (BOE-Bot). In this paper, two aims are presented: the first is the study of the WMR movement on a specific trajectories to get the desired goals positions and the second is the evaluation of the kinematic performance factor of the WMR movement. The kinematic model of the robot movement in terms of the robot wheels velocity is studied by making the robot to move on the desired trajectories. The determination of the actual robot centre position in two dimensions (X) and (Y) is done by tracking the movement of a red point located above the robot by using a fixed camera attached to the ceiling. The position error between the theoretical and actual WMR position vectors is studied and calculated in global and local coordinates' frames. The values of the position error percentage ratios when the robot moved on a (S-shape) trajectory were higher than its values when the robot moved on a (straight-line) trajectory because of the existence of a gyroscopic torque resulted from the WMR circular movement around an axis perpendicular to the axis of the WMR wheels rotation. Finally, the kinematic performance factor of the WMR movement is evaluated depending on the position error in the global coordinate.
Mobile robots use simultaneous localization and mapping (SLAM) techniques for generating maps of unknown environments through navigating its. In this work, firstly SLAM technique was considered based on extended Kalman filter (EKF) which it was implemented and evaluated at unknown environments with different number of landmarks to estimate mobile robot’s position and build a map for navigated environment at the same time. Then, the detectable landmarks will play an important role in controlling the overall navigation process as well EKF-SLAM technique’s performance. After that, three intelligent optimization algorithms are proposed to enhance the performance of the EKF-SLAM trajectory for the mobile robot, these algorithms are: particle swarm optimization (PSO), chaotic particle swarm optimization (CPSO) and genetic optimization (GA). MATLAB simulation results show that CPSO algorithm outperforms PSO and GA algorithms in terms of minimizing the mean square error (MSE1) with increasing the number of landmarks, where MSE1 is the mean square error of EKF-SLAM according to the actual trajectory. The simulation results show also the performance of EKF-SLAM trajectory is better than the performance of the Odometry trajectory and becomes best with using intelligent optimization algorithms.