Automatic Mobile Robot Obstacles Avoidance in a Static Environment by Using a Hybrid Technique Based on Fuzzy Logic and Artificial Neural network

This project focuses on testing the performances of E-puck mobile robot to avoid obstacles in a static environment. Fuzzy Logic (FL) and Artificial Neural Network (ANN) are used to improve the performances of the robot in terms of time and smoothness of its path. The objective of this project is to design an autonomous mobile robot that can avoid obstacles in three different complexities of environment. Fuzzy logic is used to create rules of avoiding obstacles. The rules consist of input from Infrared (IR) sensor and the output is from the speed of robot motor. The output is then fed into Artificial Neural Network for training process to get better performance. The result shows that the objective of this project has been successfully achieved. The overall performances of the E-puck robot shows that integration between these controllers are capable to reduce the time taken by the robot in avoiding the obstacles with a more accurate path.