Hybrid type-2 fuzzy logic obstacle avoidance system based on horn-schunck method

Abstract

This paper is concerned with a visual navigation method based on type-2 fuzzy logic controllers (T2FLC) and optical flow (OF) approach. A Takagi-Sugeno fuzzy logic controller is used for obstacle avoidance task based on video acquisition and image processing algorithm. To extract information about the environment, the captured image is divided into two parts, the control system uses optical flow values calculated by a Horn-Shunk algorithm to detect and estimate the positions of obstacles. The efficiency of the proposed structure is simulated using Visual Reality Toolbox. The obtained simulation results demonstrate the effectiveness of this autonomous visual navigation system