A neuro-fuzzy controller for mobile robot navigation and multirobot convoying

—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. NiF-T architecture comprises of three distinct parts: 1) Fuzzy logic Membership Functions (FMF), 2) a Rule Neural Network (RNN), and 3) an Output-Refinement Neural Network (ORNN). FMF are utilized to fuzzify sensory inputs. 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. In this paper, real-time implementations of autonomous mobile robot navigation and multirobot convoying behavior utilizing the NiF-T are presented. Only five rules were used to train the wall following behavior, while nine were used for the hall centering. Also, a robot convoying behavior was realized with only nine rules. For all of the described behaviors—wall following, hall ce...

• Type: Journal
• Year: 1998
• Where: TSMC
• Authors: Kim C. Ng, Mohan M. Trivedi
• Keywords: Fuzzy Logic, Fuzzy Logic Representation, Neural Network, TSMC 1998