Smart factories are complex environments equipped with both production machinery and computing devices that collect, share, and analyze data. For this reason, the modeling of today's factories can no longer rely on traditional methods, and computer engineering tools, such as SysML, must be employed. At the same time, the current SysML v1. * standard does not provide the rigorousness required to model the complexity and the criticalities of a smart factory. Recently, SysML v2 has been proposed and is about to be released as the new version of the standard. Its release candidate version shows the new version aims at providing a more rigorous and complete modeling language, able to fulfill the requirements of the smart factory domain. In this paper, we explore the capabilities of the new SysML v2 standard by building a rigorous modeling strategy, able to capture the aspects of a smart factory related to the production process, the computation and the communication. We apply the proposed strategy to model a fully-fledged smart factory, and we rely on models to automatically configure the different pieces of equipment and software components in the factory.

Human-centered robotic applications are becoming pervasive in the context of robotics and smart manufacturing, and such a pervasiveness is even more expected with the shift to Industry 5.0. The always increasing level of autonomy of modern robotic platforms requires the integration of software applications from different domains to implement artificial intelligence, cognition, and human-robot/robot-robot interaction. Developing and (re)configuring such a multi-domain software to meet functional constraints is a challenging task. Even more challenging is customizing the software to satisfy non-functional requirements such as real-time, reliability, and energy efficiency. In this context, the concept of Edge-Cloud continuum is gaining consensus as a solution to address functional and non-functional constraints in a seamless way. Containerization and orchestration are becoming a standard practice, as they allow for better information flow among different network levels as well as increased modularity in the use of multi-domain software components. Nevertheless, the adoption of such a practice along the design flow, from simulation to the deployment of complex robotic applications by addressing the de facto development standards (e.g., ROS - Robotic Operating System) is still an open problem. We present a design methodology based on Docker and Kubernetes that enables containerization and orchestration of ROS-based robotic SW applications for heterogeneous and hierarchical HW architectures. The methodology aims at (i) integrating and verifying multi-domain components since early in the design flow, (ii) mapping software tasks to containers to minimize the performance and memory footprint overhead, (iii) clustering containers to efficiently distribute load across the edge-cloud architecture by minimizing resource utilization, and (iv) enabling multi-domain verification of functional and non-functional constraints before deployment. The article presents the results obtained with a real case of study, in which the design methodology has been applied to program the mission of a Robotnik RB-Kairos mobile robot in an industrial agile production chain. We have obtained reduced load on the robot’s HW with minimal performance and network overhead, thanks to the optimized distributed system.

This paper presents a software architecture extending the classical automation pyramid to control and reconfigure flexible, service-oriented manufacturing systems. At the Planning level, the architecture requires a Manufacturing Execution System (MES) consistent with the International Society of Automation (ISA) standard. Then, the Supervisory level is automated by introducing a novel component, called Automation Manager. The new component interacts upward with the MES, and downward with a set of servers providing access to the manufacturing machines. The communication with machines relies on the OPC Unified Architecture (OPC UA) standard protocol, which allows exposing production tasks as “services”. The proposed software architecture has been prototyped to control a real production line, originally controlled by a commercial MES, unable to fully exploit the flexibility provided by the case study manufacturing system. Meanwhile, the proposed architecture is fully exploiting the production line's flexibility.

Programming modern Robots' missions and behavior has become a very challenging task. The always increasing level of autonomy of such platforms requires the integration of multi-domain software applications to implement artificial intelligence, cognition, and human-robot/robot-robot interaction applications. In addition, to satisfy both functional and nonfunctional requirements such as reliability and energy efficiency, robotic SW applications have to be properly developed to take advantage of heterogeneous (Edge-Fog-Cloud) architectures. In this context, containerization and orchestration are becoming a standard practice as they allow for better information flow among different network levels as well as increased modularity in the use of software components. Nevertheless, the adoption of such a practice along the design flow, from simulation to the deployment of complex robotic applications by addressing the de-facto development standards (i.e., robotic operating system - ROS - compliancy for robotic applications) is still an open problem. We present a design methodology based on Docker and Kubernetes that enables containerization and orchestration of ROS-based robotic SW applications for heterogeneous and hierarchical HW architectures. The design methodology allows for (i) integration and verification of multi-domain components since early in the design flow, (ii) task-to-container mapping techniques to guarantee minimum overhead in terms of performance and memory footprint, and (iii) multi-domain verification of functional and non-functional constraints before deployment. We present the results obtained in a real case of study, in which the design methodology has been applied to program the mission of a Robotnik RB-Kairos mobile robot in an industrial agile production chain. The source code of the mobile robot is publicly available on GitHub.

This article presents a methodology to formalize the behavior of the machines composing a production line, and to automatically generate their virtual prototypes for efficient and correct plant simulation. The approach exploits assume-guarantee reasoning through contracts to model the interaction between the different components of a production line. The approach is guided by a well-known taxonomy of industrial machines and associated manufacturing processes to identify each elementary action related to a specific machine. Contracts enable to build executable models of all the machines available in the production line by using automatic synthesis. The generated models can be integrated into a state-of-the-practice industrial plant simulation software to estimate and validate the production line's behavior. The presentation of the methodology is supported by a running example based on a real production line, showing the step-by-step application of the approach to a concrete scenario.

The advent of Industry 4.0 is making production processes every day more complicated. As such, early process validation is becoming crucial to avoid production errors thus decreasing costs. In this paper, we present an approach to validate production recipes. Initially, the recipe is specified according to the ISA-95 standard, while the production plant is described using AutomationML. These specifications are formalized into a hierarchy of assume-guarantee contracts. Each contract specifies a set of temporal behaviors, characterizing the different machines composing the production line, their actions and interaction. Then, the formal specifications provided by the contracts are systematically synthesized to automatically generate a digital twin for the production line. Finally, the digital twin is used to evaluate, and validate, both the functional and the extra-functional characteristics of the system.The methodology has been applied to validate the production of a product requiring additive manufacturing, robotic assembling and transportation.

This paper proposes two approaches for the integration of cyber-physical systems in a production line in order to obtain predictions concerning the actual production, core operation in the context of Industry 4.0. The first approach relies on the Multi-Level paradigm where multiple descriptions of the same CPS are modeled with different levels of details. Then, the models are switched at runtime. The second approach relies on abstraction techniques of CPS maintaining a certain levels of details. The two approaches are validated and compared with a real use case scenario to identify the most effective simulation strategy.

The approach we present in this paper exploits assume-guarantee reasoning through contracts to model a production line, and to generate its virtual prototype for efficient and correct plant simulation. Contracts are used to model the different parts composing the line; the modeling is guided by a well-known taxonomy associating industrial machines to manufacturing processes and their elementary actions, each represented by a contract. The composition of contracts representing the actions of a machine specifies each possible manufacturing process implemented by the machine. Then, automatic synthesis from contracts is used to generate an executable model of the machines composing the plant. The generated models are finally integrated into a state-of-the-practice industrial plant simulation software to validate the execution of the production line.The entire methodology is presented by showing its step-by-step application to a concrete scenario.

Customizing computer vision applications for embedded systems is a common and widespread problem in the cyber-physical systems community. Such a customization means parametrizing the algorithm by considering the external environment and mapping the Software application to the heterogeneous Hardware resources by satisfying non-functional constraints like performance, power, and energy consumption. This work presents a framework for the design and simulation of embedded vision applications that integrates the OpenVX standard platform with the Robot Operating System (ROS). The paper shows how the framework has been applied to tune the ORB-SLAM application for an NVIDIA Jetson TX2 board by considering different environment contexts and different design constraints.

Digital Twin represents a simulated model of a production line which allows making analyses of future states concerning the real factory. More in details, these analyses are related to the variability of production quality, prediction of the maintenance cycle, the accurate estimation of energy consumption and other extra-functional properties of the system. This is the core of what is so called Industry 4.0. Every single node of the manufacturing process needs to be modelled as a Cyber-physical system to be able to make the mentioned analyses. However, Manufacturing simulators represent these systems with a high level of abstraction, making impossible precise analyses. In the state of the art, some solutions try to solve the problem connecting multiple domain-specific simulators, to preserve details but requiring complex co-simulation environments. This paper presents a methodology for the integration of Cyber-Physical Systems in production line simulators, avoiding these issues. The proposed solution is based on a new promising technology: the Function Mockup Interface (FMI). This standard defines an interface to exports models as blocks called Functional Mockup Units (FMUs). These FMUs can be easily integrated together composing heterogeneous systems. The methodology is composed of two steps: 1) Exporting Digital and Physical systems as different FMUs 2) Integration of the FMUs into a production line simulator. An example is used to validate our solution which clearly shows the limitations of the production line simulator without the integration of CPSs. This paper aims at producing Cyber-Physical Production Systems (CPPS) to make more accurate simulations, and hence more accurate analysis of the production line.