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.

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.

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