In partially automated manufacturing, humans work together with mobile robots. Trajectory prediction, i.e. predicting future positions of human workers, improves collaboration and coexistence between humans and robots on the shop floor. In this paper, we discuss the interrelated research questions of how human motion trajectories can be predicted and how mobile robots such as Autonomous Mobile Robots and Automated Guided Vehicles can take such predictions into account in their pathfinding and navigation. On the robot side, advanced D* pathfinding algorithms allow robots to take dynamic obstacles into account. For trajectory prediction, the position of human workers is determined by an Ultra-Wideband-based Real-Time Locating System. A trajectory prediction framework is introduced to support the implementation and use of pattern- and planning-based trajectory prediction algorithms. The evaluation is based on scenarios from the addressed problem area of manufacturing.

Manufacturing companies are facing two major trends affecting their business operations: "automatization" and "collaboration". Companies have realized that they still need humans on the shop floor beside the availability of high levels of automation solutions in the market. This realization has created a new Industrial Revolution known as "Industry 5.0". While the primary concern in Industry 4.0 is about achieving high levels of full automation, Industry 5.0 focuses on creating synergies between humans and autonomous machines in semi-automated manufacturing systems toward flexible, resilient, and sustainable systems. The critical element of human-automation synergies is a better understanding of the excellent cooperation between humans and making a better collaboration between humans and autonomous machines inspired by it. The proposed Intelligent Collaborative Manufacturing Space (ICMS) aims to create a framework for supporting collaborations based on smart sensor networks and data science techniques. Four main elements or sub-spaces characterize this "Intelligent Workspace": (i) the Working Space, (ii) the Monitoring Space, (iii) the Modelling Space, and (iv) the Decision Space. The ICMS is a framework envisioned for supporting the effective collaboration between humans and automated and semi-automated production assets based on activity recognition and prediction paired with machine learning optimization algorithms. A methodology for developing ICMSs is described in detail in this paper.

Indoor positioning systems are an enabling technology for many current developments in the manufacturing field like digital twins and robot fleet management. Utilizing 5G for positioning promises high accuracy, reliability, and cost-efficiency due to shared hardware usage for communication and positioning. Which positioning technique suits 5G-bases positioning best for manufacturing is still an open research question. This paper presents a deep learning approach for 5G-based positioning. The first results of our research work in progress obtained at the research factory ARENA 2036 indicate a positioning accuracy in the centimeter range.

Digital Twins will change how systems and products are engineered and operated. Individual virtual representations of assets help to develop, maintain and change single components or whole factories. Aerospace engineering, product design and intelligent manufacturing are hot spots for the use of Digital Twins. Simultaneously, globalized markets lead to a growing awareness of dependability and quality, which increases the importance of verification and validation. The Digital Twin could prove to be key enabler for efficient verification and validation processes. This paper presents the results of the literature review of approaches that use Digital Twins for verification and validation purposes. Many solutions have been found for a wide range of challenges in various fields of application. This survey discusses the underlying methods and the elements of Digital Twins already in use. Most research approaches focus on simulations and three methodological clusters of approaches sharing similar ideas were identified.

Flexible intralogistics systems use automated guided vehicles (AGV) to transport goods. In assembly and warehouses, AGVs and human workers often work side by side. For optimal navigation, AGVs must consider human movement and estimate future positions of workers. Using real-time locating systems (RTLS) to improve human-robot collaboration enables more energy-efficient and safer AGV wayfinding strategies. This paper gives a summary on the topics RTLS, AGV wayfinding and trajectory prediction and introduces the momentum-based approach to predicting future worker positions in factories and warehouses. The results show that ultra-wideband-based RTLS are very well suited for trajectory prediction in the production sector.