Robot Obstacle Avoidance Simulation Using Fuzzy Controller in MATLAB

This project demonstrates robot obstacle avoidance simulation using a fuzzy controller in MATLAB. The implementation employs a fuzzy logic-based control system to guide robots in navigating through environments while avoiding obstacles. The fuzzy controller operates on fuzzy rules that process input parameters such as sensor readings and environmental data to determine optimal robot movements. Key implementation aspects include designing membership functions for input variables (distance measurements, angle deviations) and output variables (velocity, steering angle), along with developing rule bases using MATLAB's Fuzzy Logic Toolbox. The simulation incorporates real-time processing of sensor data through fuzzy inference systems, where Mamdani or Sugeno inference methods can be applied to generate intelligent decisions. By simulating various obstacle configurations and environmental conditions, we evaluate the controller's performance metrics including path efficiency, collision avoidance success rate, and response time. This simulation framework allows for parameter tuning and rule optimization, providing valuable insights for improving real-world robotic applications through code-based testing and validation. The MATLAB implementation typically involves functions like fuzzy(), addvar(), addmf(), and evalfis() for creating and evaluating the fuzzy inference system.