## 4 Mixed Reality for Human-Robot Interaction
### Mixed Reality-Enhanced Robotic Tele-Welding  
#### 4.4.1 Robotic Tele-Welding  
Welding has been used extensively in the maintenance of nuclear plants, the construction of underwater structures, and the repair of spacecraft in outer space [138]. In these hazardous situations, in which human welders have no effective access, the judgment and intervention of the human operators is still required [139]. Customized production is also an application scenario for tele-welding, where welders often work in environments with dust, strong light, radiation, and explosion hazards [140]. Human-in-the-loop (HITL) robotic tele-welding strategies have become a feasible approach for removing humans from these dangerous, harmful, and unpleasant environments while performing welding operations [141]. Robotic tele-welding systems (RTWSs) combine the advantages of humans and robotics and coordinate the functions of all system components efficiently and safely [142]. For example, RTWSs can address geographical limitations for scarce welding professionals and bring a remote workforce into manufacturing. Welding training is a time-consuming and costly process. Intensive instruction and training are usually required to bring unskilled welders to an intermediate skill level [143]. It is important to analyze the differences between the operating skills of professional and novice welders to facilitate the professional welding level of unskilled welders and to further improve the feasibility, efficiency, and welding quality of RTWSs used by novice welders during remote welding operations. Thus, the expertise and skill extraction of professional welders as well as the application of robot assistance in on-site welding operations have become popular research topics [144]. The implementation of interactive robots can stabilize the hand movements of novice welders for improved welding quality. However, robot-assisted welding has not been studied in teleoperated welding scenarios. Welding motion capture systems were used in [145] and [146] to differentiate between professional and unskilled welders in terms of operational behavior in the gas tungsten arc welding (GTAW) process, providing an experimental basis for the development of robot-assisted tele-welding schemes. The experiments in [147] revealed the differences between professional and unskilled welders in the trajectory of the GTAW hand movements and indicated the main cause of unsatisfactory welding results is that novice welders make abrupt movements in the direction perpendicular to the weld surface. However, the operational difference of gas metal arc welding (GMAW) between professional and novice users was not well examined [148]. In industrial teleoperation contexts, control algorithms play a critical role in managing communication latency during human-robot interactions [149]. These algorithms employ predictive modeling and adaptive strategies to compensate for the delay between the operator's input and the robot's response [66; 150]. By anticipating the robot's movements based on the operator's commands and real-time feedback, these algorithms help synchronize actions despite the latency [151]. The impact of communication latency on welding precision is noticeable. In tele-welding tasks, precision is paramount, requiring accurate control of the welding tool. Latency can lead to misalignments and imprecise movements, affecting the quality and integrity of the weld [152]. Control algorithms work to minimize this impact, ensuring that the robot's actions closely align with the operator's intentions, thus maintaining welding precision even in the presence of communication delays [153]. However, achieving real-time synchronization remains a significant challenge, necessitating continuous advancements in control algorithms to enhance both precision and efficiency in teleoperated robotic welding scenarios. Welding automation has advanced significantly with the integration of teleoperation and artificial intelligence (AI).