# Digital Twin for Designing and Reconfiguring Human-Robot Collaborative Assembly Lines
###### Abstract  
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. digital twin; human robot collaboration; reconfiguration +
Footnote †: journal:systems [9]. Over the last decades, DT based decision-making frameworks have been suggested advancing multiple aspects in Smart Manufacturing: (a) business data integration [10], surveillance and predictive maintenance [11], offline robot work cell design [12] and programming [13], and online robot behavior adaptation [14]. Over the last few years, several definitions for the term \"Digital Twin\" have been presented [15]. Different simulation-based techniques for the designing and the performance assessment of flexible manufacturing systems have been presented [16; 17]. The connection of a DT with the sustainability validation of an intelligent manufacturing system has been highlighted, and a framework to achieve this has been proposed [18]. An ISO standard for designing and implementing DTs is available to help SMEs develop a DT for their use cases [19]. A detailed documentation on the deployment of a DT for discrete manufacturing systems has been provided [20]. Digital Twinning techniques involve the deployment of multiple sensors for virtual reconstructing the real-time status of the factory [3; 21]. To capture and process the data coming from multiple sensing devices, various sensor fusion techniques have been developed [22]. Sensor fusion methods in the field of wearable robotics (prostheses and exoskeletons) have been investigated [23]. Additionally, the navigation ability of mobile and aerial robots has been developed based on data fusion techniques [24; 25]. The use of DT for the design and control of Human-Robot Collaborative (HRC) operations has also been investigated [26]. However, the existing approaches are limited only in design and planning for HRC systems that incorporate stationary robots. Therefore, they do not consider the use of mobile robots and its effect in layout reconfigurability. Furthermore, there is a lack of end-to-end integrated systems to monitor the workplace and resources status and dynamically re-design the system in case of unexpected events. This work introduces a DT-based system to close the loop between the design and operation phase enabling the dynamic behavior of HRC reconfigurable systems. The DT is integrated with an AI decision-making logic to design the layout and the task plans of the system. During runtime, the DT smart models are dynamically updated through unified interfaces with the physical assets synthesizing the real-time planning scene that is used when re-design is needed. The goal of the proposed approach is to overcome the existing limitations in terms of execution orchestration and online reconfiguration of hybrid production systems by implementing;  
* Simplified Robot Operating System (ROS) [27] based control integration and sensor data sharing through services. This results in an easier distribution of acquired data and tasks to all relevant resources such as mobile robots and human operators via Human-Machine Interfaces, etc.,
* Virtual representation of the shopfloor by combining multiple sensor data with resource-related information and CAD models.