The motion planning goal is finding a sequence of robots' configuration in an obstacle-free space, which reaches the robot from an arbitrary configuration to a desired one. Motion planning it self sometimes is the goal and sometimes is a tool for another purpose. Robotics' uncertainties challenge the motion planning problem. In this thesis, the most important type of uncertainties which is uncertainty in robots' motion model is completely considered. To solve this challenge, the problem is divided into subchallenges. At first, the Extended Kalman Filter is used for robots' localization problem. Then, motion planning by considering motion model uncertainty is modeled as a Markov Decision Process, and for solving this part a graph-based algorithm is used. Finally, to traverse the paths between configurations a switching controller, and also a Dynamic Feedback Linearization controller is used. At the end, the results of all the simulation and real implementation are presented, furthermore to finds the importance of considering uncertainties, these algorithms are compared by an A* algorithms without considering uncertainties.