A multi-layered multi fuzzy inference systems for autonomous robot navigation and obstacle avoidance

A multilayered multifuzzy logic controllers (MLMFLC) scheme is introduced to navigate the Khepera miniature mobile robot through obstacles and narrow opening hallways. Robots eight infrared proximity sensors are divided into three groups (left, right, and back sensors) and are fed to three fuzzy inference systems (FIS) in the first layer. The output of the first FIS group in the first layer is a representation of the robot´s immediate surrounding obstacles. Three outputs of the three FIS in the first layer are the inputs of the second layer´s FIS. Eventually, the output of the second layer FIS operates two step-motors according to position of the obstacles surrounding the robot. A total of nineteen rules have been employed for all FIS blocks. A real environment with obstacles and a dead-end trap has been used and experimental results on a real mobile robot have been demonstrated. By applying the controller, the robot moves smoothly in the experimental environment without any collision. Since the robot adjusts its speed in response to the environment, the overall speed is desirable. The design process and experimental set up is explained in detail