Article 43
Author/s
  • M. Mucientes, J. Casillas
DOI
Source
  • IEEE Transactions on Fuzzy Systems, 2006
Research Areas

Quick design of fuzzy controllers with good interpretability in mobile robotics

This paper presents a methodology for the design of fuzzy controllers with good interpretability in mobile robotics. It is composed of a technique to automatically generate a training data set plus an efficient algorithm to learn fuzzy controllers. The proposed approach obtains a highly interpretable knowledge base in a very reduced time, and the designer only has to define the number of membership functions and the universe of discourse of each variable, together with a scoring function. In addition, the learned fuzzy controllers are general because the training set is composed of a number of automatically generated examples that cover the universe of discourse of each variable uniformly and with a predefined precision. The methodology has been applied to the design of a wall-following and moving object following behavior. Several tests in simulated environments using the Nomad 200 robot software and a comparison with another learning method show the performance and advantages of the proposed approach.
Keywords: Ant colony optimization, behavior design, fuzzy control, learning, mobile robot navigation
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