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首页> 外文会议>2017 15th International Conference on Quality in Research : International Symposium on Electrical and Computer Engineering >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：四肢瘫痪者使用极端学习机的手打字机器人建模

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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：本文将介绍手打字机器人原型（HTR）中的极限学习机（ELM）的实现。 HTR是专为四肢瘫痪者设计的打字机器人。 HTR由两个机械臂组成，每个臂上装有三个dynamixel AX-12。这意味着每个手臂具有3个自由度。要操作HTR，用户必须配备指南针传感器（CMPS10），该传感器安装在功能良好的身体部位。在本文中，ELM用于在CMPS10发送的信号与Robot Arm将到达的字母位置之间进行映射和做出决定。 ELM的优势是训练过程中优越且易于实施。使用ELM，只能使用一个简单矩阵来表示输入和输出之间的关系。从实验结果可以看出，HTR可以达到73个电脑键盘按键，错误率为5％。该错误是由于dynamixel AX-12移动时的振动引起的累积错误。为了最大程度地减少错误，HTR需要定期重置。

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