Neural-fuzzy controller for real-time mobile robot navigation

Kim C. Ng; Mohan M. Trivedi

doi:10.1117/12.243253

14 June 1996 Neural-fuzzy controller for real-time mobile robot navigation

Kim C. Ng, Mohan M. Trivedi

Proceedings Volume 2761, Applications of Fuzzy Logic Technology III; (1996) https://doi.org/10.1117/12.243253
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

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.

Citation Download Citation

Kim C. Ng and Mohan M. Trivedi "Neural-fuzzy controller for real-time mobile robot navigation", Proc. SPIE 2761, Applications of Fuzzy Logic Technology III, (14 June 1996); https://doi.org/10.1117/12.243253

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