<title>Neural-fuzzy controller for real-time mobile robot navigation</title>

Abstract

A neural integrated fuzzy controller (NiF-T), which integrates the fuzzy logic representation of human knowledge with the learning capability of neural networks, is developed for nonlinear dynamic control problems. The NiF-T architecture comprises three distinct parts: (1) fuzzy logic membership functions (FMF), (2) rule neural network (RNN), and (3) output-refinement neural network (ORNN). FMF are utilized to fuzzify input parameters. RNN interpolates the fuzzy rule set; after defuzzification, the output is used to train ORNN. The weights of the ORNN can be adjusted on-line to fine-tune the controller. NiF-T can be applied for a wide range of sensor-driven robotics applications, which are characterized by high noise levels and nonlinear behavior, and where system models are unavailable or are unreliable. In this paper, real-time implementations of autonomous mobile robot navigation utilizing the NiF-T are realized. Only five rules were used to train the wall following behavior, while nine were used for the hall centering. With learning capability, the robot, SMAR-T, successfully and reliably hugs wall, and locks onto hall center. For all of the described behaviors, their RNNs are trained only for a few hundred iterations and so are their ORNNs trained only for less than one hundred iterations to learn their parent rule sets.