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This paper develops methodologies and techniques for control architecture design, path tracking laws and posture estimation of a vision-based wheeled mobile robot (WMR). To solve the problem of position/orientation tracking control of the WMR, two kinematical predictive control laws are developed to manipulate the vehicle to asymptotically follow the desired trajectories. A Kalman filtering scheme is used to reduce the bad effect of the imagine nose, thereby improving the accuracy of pose estimation. Simulation and experimental results are included to illustrate the feasibility and effectiveness of the proposed control laws.