Building maps for mobile robot navigation using fuzzy classification of ultrasonic range data

For a mobile robot to navigate in a completely unknown environment, it has to perceive its environment locally or globally, reason about its perceptions, and act accordingly. This paper presents a method for building environmental maps for mobile robot navigation using fuzzy classification of ultrasonic range data. The paper concentrates on the classification method. A fuzzy classifier is built to classify the ultrasonic data while the robot is exploring its environment. The generated map will be a graph-representation (topological) of the environment in which nodes represent situation areas and edges represent transitions between these areas. The fuzzy classification is compared with self-organizing feature map neural network classifier. It is shown that when using fuzzy classification, the generated nodes in the map are reduced. This will reduce the time to build the path between the start and target positions. Simulation results prove the success of the fuzzy classification in building environmental maps in comparison with neural networks.