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.

Seamless human–robot collaboration requires the equipping of robots with cognitive capabilities that enable their awareness of the environment, as well as the actions that take place inside the assembly cell. This paper proposes an AI-based system comprised of three modules that can capture the operator and environment status and process status, identify the tasks that are being executed by the operator using vision-based machine learning, and provide customized operator support from the robot side for shared tasks, automatically adapting to the operator’s needs and preferences. Moreover, the proposed system is able to assess the ergonomics in human–robot shared tasks and adapt the robot pose to improve ergonomics using a heuristics-based search algorithm. An industrial case study derived from the elevator manufacturing sector using a high payload collaborative robot is presented to demonstrate that collaboration efficiency can be enhanced through the use of the discussed system.