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Fuzzy Logic Controlled Miniature LEGO Robot for Undergraduate Training System

机译:模糊逻辑控制的小型LEGO机器人,用于大学训练系统

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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.
机译:与传统方法相比,模糊逻辑使设计人员能够更有效地控制复杂系统,因为模糊逻辑提供了一种简单的方法来根据模糊,不精确或嘈杂的信息得出确定的结论。在本文中,我们描述了两种微型乐高机器人的发展,即直线跟踪和光搜索移动机器人,以更好地理解模糊逻辑控制理论和实际应用在大学训练系统中。本研究分为两个部分。在第一部分中,构建了一个对象分类机器人,以执行拾取和放置任务,以将不同颜色的对象加载到跟随机器人的模糊逻辑控制行上,然后通过遵循白线将预加载的对象运送到目标。在第二部分中,开发了一种具有迷宫导航功能的智能模糊逻辑控制光搜索机器人。所有机器人均使用LEGO Mindstorms套件构建。交互式C编程语言用于对模糊逻辑机器人进行编程。实验结果表明,在模糊逻辑控制器的影响下,机器人能够成功地跟踪预定路径并向光源导航。因此可以用作本科生模糊逻辑课的培训系统。

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