# Extended Reality Application Framework for a Digital-Twin-Based Smart Crane
## 5 Conclusions  
In this work, we presented a systematical XR application development framework based on the DT-integrated overhead crane. We started this work by providing an explanation of the four functional layers, including the perceptional layer with the laboratory-based physical crane and ROS-based simulated crane; the machine communication layer with the middleware that addresses the simulated data; the network layer with the standard interface provided to the external client; as well as the service layer with Unity-based XR applications. Among those, we first developed an ROS-based crane model integrating the virtual sensors in the Gazebo. The setup of the simulated platform provided a complete test environment for the performance of the developed XR applications. Later, the network layer enabled a bridge between ROS and OPC UA corresponding to the same structure as the Siemens PLC-OPC UA server. We then developed three communication middleware of the network layer (i.e., OPC UA-MQTT wrapper, OPC UA-GraphQL wrapper, and OPC UA-Unity client) for different scenarios. Subsequently, we set up the measurement for the responsiveness test of the whole framework. The quantitative performance and the possible applied scenarios were illustrated. Furthermore, based on the discussion of the different communication middleware, we developed Unity-based cross-platform industrial XR applications under the provided framework that runs on the various terminal devices. Finally, we elaborately discussed each layer of the development framework and the potential areas that can be further addressed.  
In summary, our research empowered the interactivity between humans and machines. Enabled by the combination of digital twin and XR technologies, the XR-based digital services are expected to increase manufacturing efficiency and human satisfaction.  
Conceptualization, C.Y., X.T., J.A. and K.T.; methodology, C.Y., X.T., J.A. and R.A.-L.; software, C.Y., X.T. and J.M.; resources, C.Y., X.T., J.A., R.A.-L., P.S. and K.T.; writing--original draft preparation, C.Y.; writing--review and editing, C.Y., X.T., J.A., R.A.-L., J.M., P.S. and K.T.; visualization, C.Y.; supervision, K.T.; project administration, C.Y., X.T., J.A., P.S. and K.T.; funding acquisition, K.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Business Finland under Grant 3508/31/2019 and ITEA 3 Call 5 \"MACHINAIDE\".  
Not applicable.  
The measurement data in the \"The Performance of the Framework\" section is openly available in Zenodo at [[https://doi.org/10.5281/zenodo.6620934](https://doi.org/10.5281/zenodo.6620934)]([https://doi.org/10.5281/zenodo.6620934](https://doi.org/10.5281/zenodo.6620934)) (created and accessed on 7 June 2022).  
The authors would like to thank all \"MACHINAIDE\" consortium members and those who presented or participated in discussions of this work.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AIIC & Aalto Industrial Internet Campus \\ API & Application Programming Interface \\ AR & Augmented Reality \\ CPS & Cyber-Physical Systems \\ DDS & Data Distribution Service \\ DT & Digital Twin \\ FOV & Field of View \\ GraphQL & Graph Query Language \\ HMI & Human-Machine Interaction \\ \end{tabular}