This paper describes position control of autonomous mobile robot using combination of Kalman filter and Fuzzy logic techniques. Both techniques have been used to fuse information from internal and external sensors to navigate a typical mobile robot in an unknown environment. An obstacle avoidance algorithm utilizing stereo vision technique has been implemented for obstacle detection. The odometry errors due to systematic-errors (such as unequal wheel diameter, the effect of the encoder resolution etc.) and/or non-systematic errors (ground plane, wheel-slip etc.) contribute to various motion control problems of the robot. During the robot moves, whether straight-line and/or arc, create the position and orientation errors which depend on systematic and/or non-systematic odometry errors. The main concern in most of the navigating systems is to achieve the real-time and robustness performances to precisely control the robot movements. The objective of this research is to improve the position and the orientation of robot motion. From the simulation and experiments, we prove that the proposed mobile robot moves from start position to goal position with greater accuracy avoiding obstacles.

In the small version of the RoboCup competition, some hardware designers utilize off-the-shelf controller such as: LM629 etc. to setup a PID control loop for each individual motor on the wheel. The drawback of such approach is the unbalanced synchronization of the controllers because the programmer has to send a series of command bytes to each controller individually one by one. This creates a time lag of at approximately 12+ µS for each controller setup. The time lag for such controllers varies depending on the number of controllers involved with the move. For a typical 4-wheels robot, such the time lag can vary up to 3 setup times lag (36+ µS) between the setup of the 1 st to the setup of the 4 th motor. The purpose of this paper is to use the proposed field programmable gate array (FPGA) implementation to eliminate such problem.

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