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In the context of Industry 4.0, industrial robotics such as automated guided vehicles have drawn increased attention due to their automation capabilities and low cost. With the support of cognitive technologies for industrial Internet of Things (IoT), production processes can be significantly optimized and more intelligent manufacturing can be implemented for smart factories. In this paper, for advanced material handling, a cognitive industrial entity called context-aware cloud robotics (CACR) are introduced and analyzed. Compared with the one-time on-demand delivery, CACR is characterized by two features: 1) context-aware services and 2) effective load balancing. First, the system architecture, advantages, challenges, and applications for CACR are introduced. Then, fundamental functions for material handling are articulated, namely, decision-making mechanisms and cloud-enabled simultaneous localization and mapping. Finally, a CACR case study is performed to highlight its energy-efficient and cost-saving material handling capabilities. Simulations indicate the superiority of cognitive industrial IoT and show that using CACR for material handling can significantly improve energy efficiency and save cost.

Due to the recent development of cyber-physical systems, big data, cloud computing, and industrial wireless networks, a new era of industrial big data is introduced. Deep learning, which brought a revolutionary change in computer vision, natural language processing, and a variety of other applications, has significant potential for solutions providing in sophisticated industrial applications. In this paper, a concept of device electrocardiogram (DECG) is presented, and an algorithm based on deep denoising autoencoder (DDA) and regression operation is proposed for the prediction of the remaining useful life of industrial equipment. First, the concept of electrocardiogram is explained. Then, a problem statement based on manufacturing scenario is presented. Subsequently, the architecture of the proposed algorithm called integrated DDA and the algorithm workflow are provided. Moreover, DECG is compared with traditional factory information system, and the feasibility and effectiveness of the proposed algorithm are validated experimentally. The proposed concept and algorithm combine typical industrial scenario and advance artificial intelligence, which has great potential to accelerate the implementation of industry 4.0.

Industry 4.0 has become more popular due to recent developments in cyber-physical systems, big data, cloud computing, and industrial wireless networks. Intelligent manufacturing has produced a revolutionary change, and evolving applications, such as product lifecycle management, are becoming a reality. In this paper, we propose and implement a manufacturing big data solution for active preventive maintenance in manufacturing environments. First, we provide the system architecture that is used for active preventive maintenance. Then, we analyze the method used for collection of manufacturing big data according to the data characteristics. Subsequently, we perform data processing in the cloud, including the cloud layer architecture, the real-time active maintenance mechanism, and the offline prediction and analysis method. Finally, we analyze a prototype platform and implement experiments to compare the traditionally used method with the proposed active preventive maintenance method. The manufacturing big data method used for active preventive maintenance has the potential to accelerate implementation of Industry 4.0.

With the development of cloud computing, big data and other emerging Technology, the integration of cloud technology and multi-robot system makes it possible to make the multi robot system with high performance and high complexity. This paper briefly describes the concept and development process of the cloud robot and the overall architecture of the cloud robot system. In this paper, the major elements of cloud robot are analyzed from the point of view of big data, cloud computing, open source resources and robot cooperative learning. The key problems to be solved in the current cloud robot system are proposed. Finally, we prospect the future development of the cloud robot.

With the development of cloud computing, big data, and other emerging technologies, the integration of cloud technology and multi-robot systems makes it possible to design multi-robot systems with improved energy efficiency, high real-time performance, and low cost. In order to address the potential of clouds in enhancing robotics for industrial systems, this paper describes the basic concepts and development process of cloud robotics and the overall architecture of these systems. Then, the major driving forces behind the development of cloud robotics are carefully analyzed from the point of view of cloud computing, big data, open source resources, robot cooperative learning, and network connectivity. Subsequently, the key issues and challenges in the current cloud robotic systems are proposed, and some possible solutions are also given. Finally, the potential value of cloud robotic systems in different practical applications is discussed.

In recent years, there have been great advances in industrial Internet of Things (IIoT) and its related domains, such as industrial wireless networks (IWNs), big data, and cloud computing. These emerging technologies will bring great opportunities for promoting industrial upgrades and even allow the introduction of the fourth industrial revolution, namely, Industry 4.0. In the context of Industry 4.0, all kinds of intelligent equipment (e.g., industrial robots) supported by wired or wireless networks are widely adopted, and both real-time and delayed signals coexist. Therefore, based on the advancement of software-defined networks technology, we propose a new concept for industrial environments by introducing software-defined IIoT in order to make the network more flexible. In this paper, we analyze the IIoT architecture, including physical layer, IWNs, industrial cloud, and smart terminals, and describe the information interaction among different devices. Then, we propose a software-defined IIoT architecture to manage physical devices and provide an interface for information exchange. Subsequently, we discuss the prominent problems and possible solutions for software-defined IIoT. Finally, we select an intelligent manufacturing environment as an assessment test bed, and implement the basic experimental analysis. This paper will open a new research direction of IIoT and accelerate the implementation of Industry 4.0.