Precise vision-based navigation is very important in applications of robotics. In spite of many research efforts in the visual guidance of autonomous robotic wheelchairs have been devoted to road edge detection, the after-detection process, especially the physical interpretation of what had been detected, needs more investigation. In fact, there is a wide gap between the scene model built based on image processing algorithms and the physical model of the environment where the robotic wheelchair progress. In this paper, the subjective interaction between the scene model and the world model is investigated, and a visual control scheme for robot guidance is proposed that minimizes the model error induced by processing raw image data. The involved control system includes a fuzzy control system which uses the knowledge base information and the scene model to control the robot motion. On the other hand, the fuzzy control system is finely tuned through feed-backing mean square errors between the scene model parameters and the knowledge-base data. Finally, the fuzzy controller uses results of these calculations to home the robot on the planned path. This paper also shows the principle of this system and the simulation results confirming the feasibility of the approach.
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