Design and Implementation of Fuzzy Parallel-Parking Control for a Car-Type Mobile Robot
Journal of Intelligent and Robotic Systems
An Introduction to Learning Fuzzy Classifier Systems
Learning Classifier Systems, From Foundations to Applications
A fuzzy logic based hierarchical driver aid for parallel parking
AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
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Presents three novel techniques for enhancing the power of a genetic algorithm (GA) used to design fuzzy systems: a new context-dependent coding (CDC) technique, a simple chromosome reordering operator to maximize efficiency, and the coevolution of controller set tests to force competence in all areas of state space. These measures are shown to lead to a considerable improvement over conventional GAs when used to design controllers for a standard problem, such as the cart-pole problem. We use an analysis of GAs by L. Altenberg (1994) to determine a performance measure that demonstrates that our coding scheme and reordering operator improve the ability of the GA to organize itself and evolve chromosomal structures that not only produce high scores, but improve the search efficiency of the genetic operators. We investigate the algorithm in a controller to provide parallel parking maneuvers for mobile robots. It is shown that the controllers developed are robust to the systematic errors that inevitably arise when controllers are transferred from a simulated environment to the real world