In order to improve the performance of obstacle avoidance for mobile robots in continuous obstacle environment, a fuzzy algorithm of obstacle avoidance with speed feedback was proposed. Ultrasonic sensors were utilized to perceive the surroundings, and based on fuzzy control, the mobile robot adjusted its speed according to the distribution of obstacles. Then the graceful degradation was introduced combined with the improved fuzzy obstacle avoidance to enhance the robustness of the mobile robot. The experimental results show that the method can adjust the speed through interaction with the environment, control the robot in a collision-free way and optimize the obstacle avoidance path. Simultaneously, the method shows good effectiveness.

Concerning the problem that existing blind road recognition method has low recognition rate, simplistic handling, and is easily influenced by light, or shadow, an improved blind road recognition method was proposed. According to the color and texture features of blind road, the algorithm used two segmentation methods respectively including color histogram feature threshold segmentation combined with improved region growing segmentation and fuzzy C-means clustering segmentation for gray level co-occurrence matrix feature. And combined with Canny edge detection and Hough transform algorithm, the proposed algorithm made the blind area separated from the pedestrian area and determines the migration direction for the blind. The experimental results show that the proposed algorithm can segment several kinds of blind road more accurately, detect the boundary and direction of blind road and solve the light and shadow problem partly. It can choose the fastest and the most effective segmentation method adoptively, and can be used in a variety of devices, such as electronic guide ones.