基于web的基于深度神经网络的机器人物联网三维可视化分布式路径规划分类

Z. Iklima, T. M. Kadarina
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引用次数: 1

摘要

机器人物联网(IoRT)在设备和平台之间分布异构智能。将基于web的三维可视化技术与三自由度机械臂的分布式控制相结合。采用异步协议在平台间广播三自由度机器人的运动数据。然而,直接使用运动学逆方程计算的运动学数据无法识别机器人机械手的奇异性问题。避免奇点是防止机器人部件或关节损坏的必要条件。因此,本研究提出了一种基于深度神经网络的机械手路径规划方法,以避免奇异性问题。深度神经网络(DNN)经过500次迭代,训练时间为12分52秒。训练精度测量值为96,23 %,验证精度测量值为96,13 %,测试精度测量值为96,48 %。此外,使用Web图形库(WebGL)实现了基于Web的3自由度机械手机器人三维可视化。该分布式平台测试成功,每秒可对2352个运动进行分配和分类。
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Distributed Path Planning Classification with Web-based 3D Visualization using Deep Neural Network for Internet of Robotic Things
Internet of Robotic Things (IoRT) distributes heterogeneous intelligences among devices and platforms. A distributed control of a three-degree-of-freedom (3-DOF) robot manipulator is integrated with web-based 3D visualization. An asynchronous protocol was utilized to broadcast kinematic data of a 3-DOF robot manipulator between platforms. However, kinematic data computed using inverse kinematic equations directly cannot identify the singularity issue of robot manipulator. Singularity avoidance required to prevent robot component or joint from damage. Therefore, this study proposed a deep neural network approach as a classification-based of manipulator robot path planning to avoid singularity issues. Deep neural network (DNN) was trained in 12 minutes, 52 seconds in 500 iterations. Training accuracy measured with value 96,23 percent, validation accuracy measured with value 96,13 percent, and testing accuracy measured with value 96,48 percent Additionally, 3 DOF manipulator robot web-based 3D visualization was made using Web Graphics Library (WebGL). The distributed platform was tested successfully and can distribute and classify 2352 motions per second.
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来源期刊
Songklanakarin Journal of Science and Technology
Songklanakarin Journal of Science and Technology Multidisciplinary-Multidisciplinary
CiteScore
1.10
自引率
0.00%
发文量
0
审稿时长
25 weeks
期刊介绍: Songklanakarin Journal of Science and Technology (SJST) aims to provide an interdisciplinary platform for the dissemination of current knowledge and advances in science and technology. Areas covered include Agricultural and Biological Sciences, Biotechnology and Agro-Industry, Chemistry and Pharmaceutical Sciences, Engineering and Industrial Research, Environmental and Natural Resources, and Physical Sciences and Mathematics. Songklanakarin Journal of Science and Technology publishes original research work, either as full length articles or as short communications, technical articles, and review articles.
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