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HOW INTRODUCING ARTIFICIAL INTELLIGENT BEHAVIOURS IN EDUCATIONAL ROBOTICS

机译:如何在教育机器人学中引入人工智能行为

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This paper presents a didactical experience using an Artificial Neural Network on a LEGO Mindstorms NXT robot. Artificial Intelligence is an advanced topic with a broad variety of application fields and robotics is one of the most promising both for practical effectiveness and future developments. Robotics particularly attracts pupils but they get even more interested if the robot can show a certain degree of automatic learning. Educational robotics is a new subject where teachers and researchers are involved in providing methodological and practical frameworks to develop robotic-enhanced project-based activities to support in a new way the teaching/learning of scientific and non-scientific disciplines at different levels. The presented experience deals with a simplified version of the Optical Recognition of Characters using standard sensors and firmware on the NXT and the NXC programming language. The user gives a command to the robot on a small stripe of white paper in form of a handwritten sequence of black/empty squares. Each command learnt during a training phase by an Hopfield Neural Network implemented in the robot program, is associated with a simple motion of the robot. This work was made in the framework of European TERECoP (Teacher Education on Robotics-Enhanced Constructivist Pedagogical Methods) project aimed to define a curriculum for teacher training on educational robotics.
机译:本文介绍了使用人工神经网络在乐高思维机器人上的人工神经网络的教学经验。人工智能是一种高级专题,具有广泛的应用领域,机器人是最有希望的实际有效性和未来发展之一。机器人特别吸引学生,但如果机器人可以显示一定程度的自动学习,他们会更感兴趣。教育机器人是一个新的主题,教师和研究人员参与提供方法论和实际框架,以制定机器人增强的项目为基础的活动,以支持在不同层次的科学和非科学学科的教学/学习的新方式。由于NXT和NXC编程语言的标准传感器和固件,所呈现的经验涉及使用标准传感器和固件的字符的光学识别。用户在黑色/空平方体的手写序列的形式上给机器人提供给机器人的机器人。在机器人程序中实现的Hopfield神经网络在训练阶段期间学到的每个命令与机器人的简单运动相关联。这项工作是在欧洲Terecop框架(机器人 - 增强的建筑教学方法)的框架上进行的,该项目旨在为教育机器人学教师培训定义课程。

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