Genetic Q-Fuzzy based intelligent control for mobile robot navigation

This paper deals with the design and optimization of a Fuzzy Logic Controller that is used in the obstacle avoidance and path tracking problems of mobile robot navigation. The Fuzzy Logic controller is tuned using reinforcement learning controlled Genetic Algorithm. The operator probabilities of the Genetic Algorithm are adapted using reinforcement learning technique. The reinforcement learning algorithm used in this paper is Q-learning, a recently developed reinforcement learning algorithm. The performance of the Fuzzy-Logic Controller tuned with reinforcement controlled Genetic Algorithm is then compared with the one tuned with uncontrolled Genetic Algorithm. The theory is applied to a two-wheeled mobile robot's path tracking problem. It is shown that the performance of the Fuzzy-Logic controller tuned by Genetic Algorithm controlled via reinforcement learning is better than the performance of the Fuzzy-Logic controller tuned via uncontrolled Genetic Algorithm. Copyright © 2004 by ASME.

Duke Authors

Cited Authors

  • Parimi, VRM; Garg, DP

Published Date

  • 2004

Published In

  • American Society of Mechanical Engineers, Dynamic Systems and Control Division (Publication) DSC

Volume / Issue

  • 73 / 1 PART A

Start / End Page

  • 697 - 705