Fuzzy logic controlled miniature LEGO robot for undergraduate training system

Fuzzy logic enables designers to control complex systems more effectively than traditional approaches as it provides a simple way to arrive at a definite conclusion upon ambiguous, imprecise or noisy information. In this paper, we describe the development of two miniature LEGO robots, which are the...

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Bibliographic Details
Main Authors: Zainul Azlan, Norsinnira, Zainudin, F., Md. Yusof, Hazlina, Toha, Siti Fauziah, Yusoff, S. Z. S., Osman, N. H.
Format: Conference or Workshop Item
Language:English
Published: 2007
Subjects:
Online Access:http://irep.iium.edu.my/7129/
http://irep.iium.edu.my/7129/
http://irep.iium.edu.my/7129/
http://irep.iium.edu.my/7129/1/NZAzlan_ICIEA_2007.pdf
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Summary:Fuzzy logic enables designers to control complex systems more effectively than traditional approaches as it provides a simple way to arrive at a definite conclusion upon ambiguous, imprecise or noisy information. In this paper, we describe the development of two miniature LEGO robots, which are the line following and the light searching mobile robots to provide a better understanding of fuzzy logic control theory and real life application for an undergraduate training system. This study is divided into two parts. In the first part, an object sorter robot is built to perform pick and place task to load different colour objects on a fuzzy logic controlled line following robot which then carries the preloaded objects to a goal by following a white line. In the second part, an intelligent fuzzy logic controlled light searching robot with the capability to navigate in a maze is developed. All of the robots are constructed by using the LEGO Mindstorms kit. Interactive C programming language is used to program fuzzy logic robots. Experimental results show that the robots has successfully track the predefined path and navigate towards light source under the influence of the fuzzy logic controller; and therefore can be used as a training system in undergraduate fuzzy logic class.