Abstract
This paper proposes a logistic planner with supplementary 3D spatial representations to enhance and interact with traditional logistic systems on the context of mobile manipulators performing internal logistics operations. By defining a hierarchical structure, the logistic world model, as the central entity synchronized between multiple system components, the reliability and accuracy of the logistic system is strengthened. The proposed approach aims at implementing a robust and intuitive solution for the set-up of mobile manipulator based logistic systems. The logistic planner includes a web based interface for fast setup of the warehouse layout based on robot sensing, as well as the definition of missions for the fleet of robotic systems.

Abstract
In recent years, cognitive robots have started to find their way into manufacturing halls. However, the full potential of these robots can only be exploited through (a) an integration of the robots with the Manufacturing Execution System (MES), (b) a new and simpler way of programming based on robot skills, automated task planning, and knowledge modeling, and (c) enabling the robots to function in a shared human/robot workspace with the ability to handle unexpected situations. The STAMINA project has built a robotic system that meets these objectives for an automotive kitting application, which has also been tested, validated, and demonstrated in a relevant environment (TRL6). This paper describes the STAMINA robot system and the evaluation of this system on a series of realistic kitting tasks. The structure of the system, evaluation methodology, and experimental results, are presented along with the insights and experiences gained from this work.

Abstract
Cognitive robots have started to find their way into manufacturing halls. However, the full potential of these robots can only be exploited through an integration into the automation pyramid so that the system is able to communicate with the manufacturing execution system (MES). Integrating the robot with the MES allows the robot to get access to manufacturing environment and process data so that it can perform its task without human intervention. This paper describe the mobile robotic manipulator developed in the EU project STAMINA, its has been integrated with an existing MES and its application in a kitting task from the automotive industry.

Abstract
This paper presents an integrated cognitive robotics system for industrial kitting operations in a modern factory setting. The robot system combines low-level robot control and execution monitoring with automated mission and task planning, and a logistics planner which communicates with the factory's manufacturing execution system. The system has been implemented and tested on a series of automotive kitting problems, where collections of parts are picked from a warehouse and delivered to the production line. The system has been empirically evaluated and the complete framework shown to be successful at assembling kits in a small factory environment.

Abstract
The manufacturing industry faces a new revolution, grounded on the intense digitalization of assets and industrial processes and the increasing computation capabilities imposed by the new data-driven digital architectures. This reality has been promoting the Digital Twin (DT) concept and its importance on the industrial companies' business models. However, with these new opportunities, also new threads may rise, mainly related to industrial data protection and sovereignty. Therefore, this research paper will demonstrate the International Data Spaces (IDS) reference model's application to overcome these limitations. Following a pilot study with a Portuguese machine manufacturing company, this paper will demonstrate the development of a cutting and bending machines DT, leveraged on an IDS infrastructure for interoperability, for the plastic and metal industry and its importance to introduce this machine manufacturing company in a new B2B marketplace from the EU project Market 4.0.

Abstract
The principles and tools made available by the Industry 4.0, smart factories, or the Internet of Things (IoT), along with the adoption of more comprehensive simulation models, can significantly help the industry to face the current, huge external and internal challenges. This paper presents a new simulation-based approach to support decision making in the design and operational management of manufacturing systems. This approach is used to evaluate different layouts and resources allocation, and help managing operations, by integrating a simulation software with real-time data collected from the production assets through an IoT platform. The developed methodology uses a digital representation of the real production system (that may be viewed as a form of a digital twin) to assess different production scenarios. A set of key performance indicators (e.g. productivity) provided by the simulation can be used by the Manufacturing Execution System (MES) to generate production schedules. The developed approach was implemented and assessed in a real case study, showing its robustness and application potential. Its extension to other industrial contexts and sectors seems, therefore, quite promising. Copyright (C) 2021 The Authors.