# Digital Twin for Human-Robot Collaboration in Manufacturing: Review and Outlook
## 5 Outlook and Conclusions  
The growth of DT indicates its potential to be an integral part of HRC to support safety, maintenance and task planning. In this paper, we have focused on the impact of DT technologies in the manufacturing domain and various approaches used for creating DT. The research trend shows that the DT is in the technology growth phase and will take many more years to reach maturity. Research on the creation of DT of the industrial robotic process is increasing exponentially, while that of DT HRC is lagging behind. This is mainly due to the fact that it is much easier to create a digital model of an industrial robot than a collaborative robot, due to the uncertainty pertaining to modelling the environment involving the human operator. There are research efforts ongoing to demonstrate the DT of simple robotic tasks with limited sensing capability without considering factors such as ageing and the wear and tear of components. Running a real-time simulation and DT is still a challenge. Different modelling methods are available for creating a digital model of a task or a process. The integration of models of such processes under one umbrella is essential to create a realistic model of the physical system. Photo-realistic simulation in conjunction with physics-inspired neural networks can be used for DT-HRC applications. In recent years, the advancement of photo-realistic simulation environments such as Nvidia Isaac platforms and physics-inspired neural networks could potentially overcome the limitation faced in the area of DT-HRC. Process simulation and visualization platforms, such as Visual Components (VC), Siemens Simatic WinCC and KUKA.Sim Pro, CoppeliaSim, Gazebo, Siemens Technomatix, programming and computing platforms such as MATLAB and communication frameworks such as ROS/ROS2 could be used for creating the DT of human-robot collaboration. Cloud platforms such as OpenWhisk, WSO2, or OpenShift can be used for data processing. In technologies related to AR and VR, such as real-time communication, the necessity of realistic representation of the physical system and the environment are also essential for creating a DT. Developments in these fields could bring radical changes in DT technology and its implementation. Game engines are an option worth considering for the visualisation of DT-HRC, with simulation and visualisation of DT-HRC considered as two different aspects. An instance of the DT-HRC process can be created to perform simulations in the background, while the visualisation through game engine tools may provide a real-time update of the process. There are various concepts and frameworks currently available to develop a DT model. However, a greater focus on creating a generic approach that can serve as a common base for all DT platforms could produce interesting findings as the technology matures. With the advancement of Edge AI or AI-enabled hardware graphical processing units (GPU) such as the Nvidia Jetson series or AI-accelerators such as Intel Movidius products, the DT of a factory level process can be decomposed into smaller DT processes in a modular manner. This allows the processing of information at the lower system level, rather than utilising data at a higher level for parameterizing/tuning the operations. This can unlock different new approaches for building a factory level DT. The challenge and potential of DT within the manufacturing domain were also discussed in this paper. The challenges around CPS and IoT need to be addressed together with advances in simulation environments with physics engines to achieve the expected benefits of DT. There is also a need for better modelling of complex tasks to build more accurate DTs. In addition, better data transfer between different modules of the DT is essential. Continued progress in the fields of communication and semiconductor processing technologies are key enablers in this regard. Furthermore, the notion and understanding of DT varies from person to person.