Self-learning Mobile Robot Navigation in Unknown Environment Using Evolutionary Learning

被引：0
|
作者：
Algabri, Mohammed [1 ]
Mathkour, Hassan [1 ]
Ramdane, Hedjar [1 ]
Alsulaiman, Mansour [1 ]
Al-Mutib, Khalid [1 ]
机构：
[1] King Saud Univ, Riyadh, Saudi Arabia
来源：
JOURNAL OF UNIVERSAL COMPUTER SCIENCE | 2014年 / 20卷 / 10期
关键词：
Robotics; Fuzzy Logic controller; soft computing; genetic algorithm; genetic-fuzzy algorithm;
D O I：
暂无
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
An autonomous mobile robot operating in an unstructured environment must be able to learn with dynamic changes to that environment. Learning navigation and control of mobile robot in an unstructured environment is one of the most challenging problems. Fuzzy logic control is a useful tool in the field of navigation of mobile robot. In this research, we optimized a performance of fuzzy logic controller by evolutionary learning technique. Two proposed approaches have been designed and implemented: Fuzzy Logic Controller (FLC) and Genetic-Fuzzy Controller (GA-FLC). The Genetic Algorithm is used for automatically learning to tune the membership function parameters for mobile robot motion control. Moreover, the performance of these approaches are compared through simulation.
引用
收藏
页码：1459 / 1468
页数：10
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