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Development of Knowledge Base Sharing Technologies for Cloud Service Robot

机译:云服务机器人知识库共享技术的开发

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Businesses related to artificial intelligence are forming ecosystems in areas such as the development of core technologies and the efficiency of jobs in enterprises. These technologies are expected to achieve sustained growth in the future. In the future, deep learning will be differentiated according to the level of practical use for each applicable field, and research and development will be needed from a long-term perspective. In terms of existing technologies, deep learning is an artificial neural network model, a field of machine learning, and is used for information collection, classification, prediction, recognition, and control functions in almost all industries including business. In terms of product specificity, it will be used to build robotic industry ecosystem such as infrastructure of cloud sourcing based on knowledge and intelligence data of robot in future by developing knowledge base sharing technology of service robot. In terms of commercialization status, deep learning technology is being used in speech recognition, image processing, autonomous driving car, artificial intelligence computer, virtual personal assistant, etc. Currently, the cloud robot industry does not have a common interface or communication standard between robots, unlike other computing industries. Since the operating system uses various operating systems such as Windows, Linux, RTOS, and Android, technology development is slowing down. Therefore, it is necessary to standardize not only the operating system but also the robot application, the common interface and the communication standard in order to promote the robot industrial technology and activate the robot software market. Currently, there are VWNS robot software frameworks such as OPRoS (Open Platform for Robotics Services) in Korea and ROS (Robot Operation System) in USA. The interfaces of application programs are standard of RoIS (Robot Interaction Service) and RLS (Robot Localization Service). However, since it is still in the development stage, it is necessary to build a common infrastructure that is more widespread and to standardize and globalize it. In this presentation, we will present technologies and standardization methods for knowledge base sharing in cloud service robots.
机译:与人工智能相关的业务正在诸如核心技术的发展和企业工作效率之类的领域中形成生态系统。这些技术有望在未来实现持续增长。将来,深度学习将根据每个适用领域的实际使用水平而有所不同,并且需要长期研究和开发。就现有技术而言,深度学习是一种人工神经网络模型,是机器学习的领域,并且用于几乎所有行业(包括商业)的信息收集,分类,预测,识别和控制功能。就产品的特殊性而言,未来将通过开发服务机器人的知识库共享技术,基于机器人的知识和智能数据来构建机器人行业生态系统,例如云采购的基础设施。在商业化状态方面,深度学习技术正在语音识别,图像处理,自动驾驶汽车,人工智能计算机,虚拟个人助理等中使用。当前,云机器人行业在机器人之间没有通用的接口或通信标准与其他计算行业不同。由于操作系统使用各种操作系统,例如Windows,Linux,RTOS和Android,因此技术开发正在放缓。因此,不仅需要标准化操作系统,还需要标准化机器人应用程序,通用接口和通信标准,以促进机器人工业技术和激活机器人软件市场。当前,有VWNS机器人软件框架,例如韩国的OPRoS(机器人服务开放平台)和美国的ROS(机器人操作系统)。应用程序的接口是RoIS(机器人交互服务)和RLS(机器人本地化服务)的标准。但是,由于它仍处于开发阶段,因此有必要构建更广泛的通用基础结构并使之标准化和全球化。在本演讲中,我们将介绍云服务机器人中知识库共享的技术和标准化方法。

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