Environmental recognition using RAM-network based type-2 fuzzy neural for navigation of mobile robot

Abstract

Reactive autonomous mobile robot navigating in real time environment is one of the most important requirements. Most of the systems have some common drawbacks such as, large computation, expensive equipment, hard implementation, and the complexity of the system. The work presented in this paper deals with a type-2 fuzzy-neural controller using RAM-based network to make navigation decisions. The proposed architecture can be implemented easily with low cost range sensor and low cost microprocessor. To minimize the execution time, we used a look-up table and that output stored into the robot RAM memory and becomes the current controller that drives the robot. This functionality is demonstrated on a mobile robot using a simple, 8 bit microcontroller with 512 bytes of RAM. The experiment results show that source code is efficient, works well, and the robot was able to successfully avoid obstacle in real time.