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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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A neuro-fuzzy controller for mobile robot navigation and multirobot convoying.

K C Ng1, M M Trivedi

  • 1Dept. of Electr. & Comput. Eng., California Univ., San Diego, La Jolla, CA.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 8, 2008
PubMed
Summary

A novel Neural integrated Fuzzy conTroller (NiF-T) effectively controls nonlinear systems, integrating human knowledge with neural network learning for autonomous robot navigation and multi-robot coordination with minimal rules and training.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Traditional control methods struggle with complex nonlinear dynamic systems.
  • Integrating human expertise (fuzzy logic) with adaptive learning (neural networks) offers a promising approach.

Purpose of the Study:

  • To develop and implement a Neural integrated Fuzzy conTroller (NiF-T) for nonlinear dynamic control problems.
  • To demonstrate the real-time application of NiF-T in autonomous mobile robot navigation and multi-robot convoying.

Main Methods:

  • The NiF-T architecture combines Fuzzy logic Membership Functions (FMF), a Rule Neural Network (RNN), and an Output-Refinement Neural Network (ORNN).
  • FMF fuzzify sensory inputs, RNN interpolates fuzzy rules, and ORNN refines the output, with adjustable weights for online tuning.
  • The system was tested on wall following, hall centering, and multi-robot convoying behaviors.

Main Results:

  • NiF-T successfully enabled autonomous mobile robot navigation and multi-robot convoying behaviors.
  • Complex behaviors like wall following and hall centering were achieved with a minimal number of fuzzy rules (5 and 9, respectively).
  • Both RNN and ORNN components required limited training iterations (hundreds and less than one hundred, respectively) to learn the control rules.

Conclusions:

  • The Neural integrated Fuzzy conTroller (NiF-T) provides an efficient and effective solution for nonlinear dynamic control problems.
  • NiF-T demonstrates strong adaptability and learning capabilities, achieving complex robotic behaviors with minimal rules and training.
  • This approach offers a powerful framework for developing intelligent control systems in robotics and other dynamic applications.