Industry 4.0 aims at supporting smarter and autonomous processes while improving agility, cost efficiency and user experience. To fulfill its promises, properly processing the data of the industrial processes and infrastructures is required. Artificial Intelligence (AI) appears as a strong candidate to handle all generated data, and to help in the automation and smartification process. This article overviews the Digital Twin as a true embodiment of a Cyber-Physical System (CPS) in Industry 4.0, showing the mission of AI in such concept. It presents the key enabling technologies of the Digital Twin such as Edge, Fog and 5G, where the physical processes are integrated with computing and network domains. The role of AI in each technology domain is identified by analyzing a set of AI agents at the application and infrastructure level. Finally, movement prediction is selected and experimentally validated using real data generated by a Digital Twin for robotic arms with results showcasing its potential.

Digital Twin is one of the use cases targeted by the fourth industrial revolution (Industry 4.0), which, through the digitalization of the robotic systems, will enable enhanced automation and remote controlling capabilities. Building upon this concept, this work proposes a solution for an Edge-based Digital Twin for robotic systems, which leverages on the cloudto-things continuum to offload computation and intelligence from the robots to the network. This imposes stringent requirements over the communication technologies which are fulfilled in the proposed solution by relying on 5G. This solution is implemented in an E2E scenario and validated through a set of experimental evaluations. Results show that offloading the robot's functions to the edge is feasible when supported by the 5G connectivity. Moreover, the benefits of introducing intelligence and automation are also assessed.

Cloud robotics aims at endowing robot systems with powerful capabilities by leveraging the computing resources available in the Cloud. To that end, the Cloud infrastructure consolidates services and information among the robots, enabling a degree of centralization which has the potential to improve operations. Despite being very promising, Cloud robotics presents two critical issues: (i) it is very hard to control the network between the robots and the Cloud (e.g., long delays, high jitter), and (ii) local context information (e.g., on the access network) is not available in the Cloud. This makes hard to achieve deterministic performance for robotics applications. Over the last few years, Edge computing has emerged as a trend to provide services and computing capabilities directly in the access network. This is so because of the additional benefits enabled by Edge computing: (i) it is easier to control the network end-to-end, and (ii) local context information (e.g., about the wireless channel) can be made available for use by applications.The goal of this paper is to showcase, by means of real-life experimentation, the benefits of residing at the Edge for robotics applications, due to the possibility of consuming context information locally available. In our experimentation, an application running in the Edge controls over a Wi-Fi link the movement of a robot. Information related to the wireless channel is made available via a service at the Edge, which is then consumed by the application.Results show that a smoother driving of the robot can be achieved when wireless quality information is considered as input of the movement control algorithm.

Recent advances on Edge computing, Network Function Virtualization (NFV) and 5G are stimulating the interest of the industrial sector to satisfy the stringent and real-time requirements of their applications. Digital Twin is a key piece in the industrial digital transformation and its benefits are very well studied in the literature. However, designing and implementing a Digital Twin system that integrates all the emerging technologies and meets the connectivity requirements (e.g., latency, reliability) is an ambitious task. Therefore, prototyping the system is required to gradually validate and optimize Digital Twin solutions. In this work, an Edge Robotics Digital Twin system is implemented as a prototype that embodies the concept of Digital Twin as a Service (DTaaS). Such system enables real-time applications such as visualization and remote control, requiring low-latency and high reliability. The capability of the system to offer potential savings by means of computation offloading are analyzed in different deployment configurations. Moreover, the impact of different wireless channels (e.g., 5G, 4G and WiFi) to support the data exchange between a physical device and its virtual components are assessed within operational Digital Twins. Results show that potentially 16% of CPU and 34% of MEM savings can be achieved by virtualizing and offloading software components in the Edge. In addition, they show that 5G connectivity enables remote control of 20 ms, appearing as the most promising radio access technology to support the main requirements of Digital Twin systems.

The 5 th generation (5G) of mobile communications introduces improvements on many fronts when compared to its previous generations. Besides the performance enhancements and new advances in radio technologies, it also integrates other technological domains, such as cloud-to-things continuum and artificial intelligence. In this work, the 5G-DIVE Elastic Edge Platform (DEEP) is proposed as the linking piece for the integration of these technological domains, making available an Intelligent Edge and Fog 5G End-to-End (E2E) solution. Such solution brings numerous benefits to the vertical industries by enabling a streamlined, abstracted, and automated management of their vertical services, thus contributing to the introduction of novel services, cost savings, and improved time-to-market. Preliminary validation of the proposed platform is performed through a proof-of-concept, along with a qualitative analysis of its benefits to Industry 4.0 and Autonomous Drone Scouting vertical industries.