Fuzzy Reinforcement Learning Rectilinear Follow-up of Trajectory per Robot

International Arab Journal of Information Technology (IAJIT)

Maryam Madani¹  Shadpour Mallakpour² 

[1] Department of Chemistry, Isfahan University of Technology, Isfahan 84156-83111, I. R. Iran$$Department of Chemistry, Isfahan University of Technology, Isfahan 84156-83111, I. R. IranDepartment of Chemistry, Isfahan University of Technology, Isfahan 84156-83111, I. R. Iran$$;Department of Chemistry, Isfahan University of Technology, Isfahan 84156-83111, I. R. Iran$$Nanotechnology and Advanced Materials Institute, Isfahan University of Technology, Isfahan 84156-83111, I. R. Iran$$Department of Chemistry, Isfahan University of Technology, Isfahan 84156-83111, I. R. IranDepartment of Chemistry, Isfahan University of Technology, Isfahan 84156-83111, I. R. Iran$$Nanotechnology and Advanced Materials Institute, Isfahan University of Technology, Isfahan 84156-83111, I. R. Iran$$Nanotechnology and Advanced Materials Institute, Isfahan University of Technology, Isfahan 84156-83111, I. R. IranDepartment of Chemistry, Isfahan University of Technology, Isfahan 84156-83111, I. R. Iran$$Nanotechnology and Advanced Materials Institute, Isfahan University of Technology, Isfahan 84156-83111, I. R. Iran$$

关键词: Mobile robot;    navigation;    reinforcement learning;    fuzzy logic;    fuzzy Q-learning;    fuzzy inference system.;   

DOI  :  

学科分类：计算机科学（综合）

来源: Zarqa University

【 摘 要 】

Knowing the action space of an order, the objective consists in distributing this space in a certain set of actions equitably in order to choose the famous action among the candidate ones. This process is ensured by reinforcement learning aided by fuzzy logic. We have established an algorithm applying the fuzzy Q-learning with a fuzzy limited lexicon. We have applied it to a robot for the training of the follow-up of a rectilinear trajectory from a starting point “D” at an unspecified arrival point "A", while avoiding with the robot butting against a possible obstacle. The goal of this work tries to answer the question, in what the reinforcement learning applied to fuzzy logic can be of interest in the field of the reactive navigation of a mobile robot. Keywords: Mobile robot, navigation, reinforcement learning, fuzzy logic, fuzzy Q-learning, fuzzy inference system.Received April 21, 2004; accepted August 21, 2004Full Text

【 授权许可】

Unknown