Fuzzy classifier system architectures for mobile robotics: An experimental comparison

Pipe, Anthony G.; Carse, Brian

Abstract

We present an experimental comparison between two approaches to optimization of the rules for a fuzzy controller. More specifically, the problem is autonomous acquisition of an "investigative" obstacle avoidance competency for a mobile robot. We report on results from investigating two alternative approaches to the use of a Learning Classifier System (LCS) to optimize the fuzzy rule base. One approach operates at the level of whole rule bases, the "Pittsburgh" LCS. The other approach operates at the level of individual rules, the "Michigan" LCS. In this work, both of these Fuzzy Classifier Systems were designed to operate only on the rules of fuzzy controllers, with predefined fuzzy membership functions. There are two main results from this work. First, both approaches were capable of producing fuzzy controllers with subtle interactions between rules leading to competencies exceeding that of the hand-coded fuzzy controller presented in this article. Second, the Michigan approach suffered more seriously than the Pittsburgh approach from the well-known LCS "cooperation/competition" problem, which is accentuated here by the structural combination of Evolutionary Computation and a fuzzy system. This problem was alleviated a little by the combination of a clustered subpopulation niche system and a fitness-sharing scheme applied to the Michigan approach, but still remains. © 2007 Wiley Periodicals, Inc.

• Journal Article Type: Article
• Publication Date: Sep 1, 2007
• Journal: International Journal of Intelligent Systems
• Print ISSN: 0884-8173
• Electronic ISSN: 1098-111X
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 22
• Issue: 9
• Pages: 993-1019
• DOI: https://doi.org/10.1002/int.20235
• Keywords: fuzzy classifier system architectures, mobile robotics
• Public URL: https://uwe-repository.worktribe.com/output/1052076
• Publisher URL: http://dx.doi.org/10.1002/int.20235
• Additional Information: Additional Information : Learning Classifier Systems (LCSs) are proposed as the universal learning system, and could therefore be used in a wide range of applications. They represent powerful linguistically interpretable rule-based structures, combined with versatile self-adaptive algorithms. However, in standard form, they lack interfaces to continuously varying inputs and outputs. Combining them with Fuzzy Logic solves this problem, but leads to other internal difficulties for the learning system. This article presents an experimental comparison of the two main types of LCS applied to mobile robot control, and suggests some underlying reasons for the remaining learning difficulties.