# Digital Twin for Human-Robot Collaboration in Manufacturing: Review and Outlook
## 3 Review of DT in HRC
### SBA vs. LBA Development
#### 3.4.1 Software for the Development of Digital Twin for HRC
These features can be obtained in open-source platforms but must be programmed to visualise the plot. The final concern regards the transferability and modularity of the DT model. Use of commercial software tends to restrict the ability to import the data from one platform to another. This can be a potential bottleneck in the development of a complex DT model for the manufacturing process involving several partners. The development of the DT modelin an open-source platform can potentially avoid this problem. However, some companies may be reluctant to use non-commercial or open source platforms due to cyber-security and data protection concerns. In addition, there is the issue of computer operating system compatibility, with DT software such as Visual Components, KUKA.Sim, and Siemens Tecnomatix only available on the Microsoft Windows platform. Example of DT-HRC Simulation vs. Visualisation  
For many years, the open-source platform ROS has played an important role in developing various robotic applications and has been widely used in HRC scenarios. For example, a ROS-based architecture of coordinated assembly tasks involving a human and a robot is presented in [117] and a DT involving a dual-arm collaborative mobile robotic platform based on ROS is shown in [118]. There are various open-source or cost-efficient simulation software packages that are compatible with ROS, such as CoppeliaSim, Gazebo, RViz, Unity3D and Blender. Some of these platforms are game engines rather than a simulation environment, but can be used to perform simulation tasks. An example of such an approach has been shown in [109]. A combination of Blender and Unity3D was used to create a DT for a use case to optimize the planning and commissioning process of a production process [119]. While most of these software tools require additional plugins or packages for set-up and visualization, RViz and Gazebo are widely used due to their compatibility with the ROS communication framework. Therefore, for DT-HRC with ROS, simulation and visualization should be considered as two separate tasks. In order to visualize robotic simulation in RViz or in Gazebo, the model of the robot needs to be imported in Unified Robot Descriptive Format (URDF) or in Xacro (an XML macro language) format. In terms of robotic simulation, each of the joints in the URDF are interconnected as revolute or prismatic joints depending on the type of robot. This facilitates the rotation of each of the individual joints as a single degree of freedom (DoF). However, there are no standard URDF or human mannequins available for RViz or Gazebo. Capturing the motion of operators in real-time using sensors, such as IMU or vision system in Gazebo or RViz is very complex [45]. This is mainly due to the issues related to the creation of floating joints which are typically six DoF joints. The creation of such joints is not supported in Gazebo or RViz. Multi DoF motion cannot be performed without floating joints [120]. Hence, real-time operator tracking in RViz using the multi-DoF URDF model is complex. This is one of the significant drawbacks of real-time monitoring and simulation of human motion in ROS. An approach to overcome this limitation and visualize the operator's real-time motion may be the digital twin of a human avatar created using open-source platforms, such as MakeHuman, Blender and RViz. An overview of the proposed approach is shown in Figure 8. Here, a human model is created using the open-source platform MakeHuman. This model is then imported into Blender in STL CAD format. The human model is sliced into several parts, as shown in Figure 9, using Blender to create a URDF model. This model is then imported into RViz in the ROS environment for visualization. The proposed approach also involves multiple Azure Kinect sensors, which provide real-time tracking information about the operator. Sensor fusion approaches were used to combine data from these Azure Kinect sensors.