Design Membership Functions of a Fuzzy Logic Controller based on Experimental Study for an Obstacle Avoidance Algorithm Rudzuan M.Nor 1, a , Hazry, D 2,b , Khairunizam WAN 3,c , M. Saifizi 3 M. Nasir Ayob 3 and W.M. Nooriman 3 1 AiCoS Research Group, School of Mechatronic Engineering, UniMAP, Malaysia 2 AutoMAV Research Cluster, Universiti Malaysia Perlis, Malaysia 3 School of Mechatronic Engineering, Universiti Malaysia Perlis, Malaysia a rudzuan@unimap.edu.my, b hazry@unimap.edu.my, c khairunizam@unimap.edu.my Keywords: Membership function, Fuzzy Logic Controller, Obstacle avoidance Abstract. This paper details the design of Fuzzy Logic Controller (FLC) for an autonomous mobile robot that is functionally operated with the minimum human intervention in an unstructured environment. FLC controller is designed to change the wheels speed and the steering angle of the mobile robot according to the obstacle’s distance detected by the ultrasonic sensors attached in front of driving direction of the mobile robot. Membership functions and Fuzzy rules designing process for FLC controller are the most important elements in designing an effective obstacle avoidance controller for an autonomous mobile robot. A set of experimental studies was conducted in order to define the average, the minimum and maximum value for each membership function of the FLC controller. Based on experimental study, the highest level of each membership function is quantified by the average value, while the min and the max crisp values of membership function are defined from the standard deviation value obtained from collection data of actual conducted experiments. The FLC controller then examined in virtual instrumentation and graphical system design software developed by LabVIEW™ and the results are verified and recorded. Several membership function designs are compared to confirm and obtain the most effective controller for obstacle avoidance mobile robot. Introduction For a better understanding in reading this article, the definition of each important term from the article’s title and main keywords will be, first roughly defined in this section. Design membership function of a Fuzzy Logic Controller could be started with defining or selecting the inputs and the outputs parameter for the controller. The next step will be to define the range (the possible minimum and maximum value) of each parameter. The steps are then continued in how to design the membership function and how to decide the range or level of the weight for input parameter, and the level of action need to be taken by the controller for the output parameter. All details regards to designing process of Fuzzy Logic Controller Membership function will be further detailed in the Designing Membership Function section. Fuzzy Logic Controller is been develop by L.A. Zadeh in 1965 [1] and since last 30 years after its first application in process control [2] , it had been reported to be used for various of applications such as motion planning control (Lee, L., et. at, 2003), intelligent gantry crane (Wahyudi and Jalani, J., 2005), social relationship (Araujo, E. et. al, 2008), engine fault diagnosis (Zhoungze, F., et. al, 2009), quality management (Wang, L., et. al, 2009), pattern recognition (Cni Yan, et. al, 2009), washer control (Zhen. Y, et. al, 2011), diabetes decision support (Lee, CS., et. al, 2011), inventory system (De, PK., et. al, 2012), flight control system (Gau, L., et. al, 2012), agriculture robot (Prema, et. al, 2012) and many more [3]. From these, we know that Fuzzy Logic Controller is applicable to numerous applications in different fields including social science, health science and business management studies. Detail of Fuzzy Logic Controller components are briefly explained in Section Fuzzy Logic Controller.