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首页> 外文会议>International Conference on Quality in Research >Hand typist robot modelling for quadriplegic person using extreme learning machine
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Hand typist robot modelling for quadriplegic person using extreme learning machine

机译：使用极端学习机的四元互联人的手写型式机器人建模

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摘要

This paper will present an implementation of Extreme Learning Machine (ELM) in Prototype of Hand Typist Robot (HTR). HTR is Typist Robot which is designed for quadriplegic people. HTR consists of two robotic arms with three dynamixel AX-12 that mounted on each arm. It is mean that each arm has 3 DOF. To operate HTR, user has to equipped with compass sensor (CMPS10), installed on the part of body that has good function. In this paper ELM is used to map and make decision between the signal which sending by CMPS10 and position of alphabet that will be reached by Robot Arm. The advantage of ELM is superior in training process and easy to implement. Using ELM, the relationship between input and output can be present only using one simple matrix. From the experiment result shown that 73 keys of computer keyboard can be reached by HTR with an error 5%. The error is accumulated errors which is caused by vibration of dynamixel AX-12 when it is moving. To minimize the error the HTR need to reset regularly.
机译：本文将在手写型机器人（HTR）原型中实现极端学习机（ELM）的实施。 HTR是典型的机器人，专为四元人民而设计。 HTR由两个机器人臂组成，其中三个用于每个臂上的三个ydewixel Ax-12。这意味着每个臂都有3个DOF。要操作HTR，用户必须配备罗盘传感器（CMPS10），安装在具有良好功能的主体上。在本文中，ELM用于映射并在通过CMPS10发送的信号之间进行映射和决定，该信号将由机器人手臂达到的字母表。 ELM的优势在训练过程中优越，易于实施。使用ELM，输入和输出之间的关系可以仅使用一个简单的矩阵存在。从实验结果表明，HTR返回73个计算机键盘的键，错误5 ％。误差是累积的错误，该错误是由动轴轴-1移动时的振动引起的。最小化HTR需要定期重置的错误。

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