This paper discusses a digital twin-based approach for designing and redesigning flexible assembly systems. The digital twin allows modeling the parameters of the production system at different levels including assembly process, production station, and line level. The approach allows dynamically updating the digital twin in runtime, synthesizing data from multiple 2D–3D sensors in order to have up-to-date information about the actual production process. The model integrates both geometrical information and semantics. The model is used in combination with an artificial intelligence logic in order to derive alternative configurations of the production system. The overall approach is discussed with the help of a case study coming from the automotive industry. The case study introduces a production system integrating humans and autonomous mobile dual arm workers.

This paper presents an Augmented Reality software suite aiming at supporting the operator’s interaction with flexible mobile robot workers. The developed AR suite, integrated with the shopfloor’s Digital Twin, provides the operators’ a) virtual interfaces to easily program the mobile robots b) information on the process and the production status, c) safety awareness by superimposing the active safety zones around the robots and d) instructions for recovering the system from unexpected events. The application’s purpose is to simplify and accelerate the industrial manufacturing process by introducing an easy and intuitive way of interaction with the hardware, without requiring special programming skills or long training from the human operators. The proposed software is developed for the Microsoft’s HoloLens Mixed Reality Headset 2, integrated with Robotic Operation System (ROS), and it has been tested in a case study inspired from the automotive industry.

Robotic flexibility in industry is becoming more and more relevant nowadays, especially with the rise of the Industry 4.0 concept. This paper presents a smart execution control framework for enabling the autonomous operation of flexible mobile robot workers. These robot resources are able to autonomously navigate the shopfloor, undertaking multiple operations while acting as assistants to human operators. To enable this autonomous behavior, the proposed framework integrates robot perception functions for the real-time shopfloor and process understanding while orchestrating the process execution. A Digital World Model is deployed synthesizing the sensor data coming from multiple 2D and 3D sensors from the shopfloor. This model is consumed for the perception functions enabling the real-time shopfloor and process perception by the robot workers. This smart control system has been applied and validated in a case study from the automotive sector.

The role of Artificial intelligence in achieving high performance in manufacturing systems has been explored over the years. However, with the increasing number of variants in the factories and the advances in digital technologies new opportunities arise for supporting operators in the factory. The hybrid production systems stipulate the efficient collaboration of the workers with the machines. Human action recognition is a major enabler for intuitive machines and robots to achieve more efficient interaction with workers. This paper discusses a software framework called Praxis, aiming to facilitate the deployment of human action recognition (HAR) in assembly. Praxis is designed to provide a flexible and scalable architecture for implementing human action recognition in assembly lines. The framework has been implemented in a real-world case study originating for showcasing and validating the effectiveness of Praxis in real-life applications. It is deployed in an assembly use case for an air compression production industry. This study highlights the potential of the Praxis framework for promoting efficient human–robot collaboration (HRC) in modern manufacturing environments through HAR.