# A Voice-Enabled ROS2 Framework for Human-Robot Collaborative Inspection
## 6 Discussion and Future Work  
This paper is focused on the execution of voice-enabled quality inspection processes in human-robot collaborative environments. The proposed framework was developed based on the ROS2 concept for the realization of the communication between the quality inspection module, the robot controller and the voice recognition algorithm. Section 2 is focused on the presentation of the proposed framework's modules, namely: (a) voice recognition, (b) quality inspection, (c) robot manipulation and (d) visualization. The implementation of the previously presented modules inside the developed framework is described in detail in Section 3, together with its main sequence diagram, presenting the connection between the software components for the proposed framework's implementation. Finally, the integration of the developed framework in a use case from the automotive industry focused on the quality inspection of the assembled parts on a vehicle engine using a Universal Robot UR10 and a ROBOCEPITION camera is presented in Section 4. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Participant** & **MD** & **PD** & **TD** & **OP** & **EF** & **FR** & **Total** \\ \hline
1 & 0 & 20 & 10 & 10 & 15 & 10 & 9.67 \\
2 & 10 & 20 & 10 & 30 & 10 & 0 & 15.33 \\
3 & 10 & 20 & 15 & 10 & 0 & 0 & 10.67 \\
4 & 15 & 25 & 30 & 10 & 30 & 20 & 21.33 \\
5 & 25 & 10 & 20 & 10 & 30 & 30 & 19.67 \\ Overall Score & & & & & & & 15.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: NASA-TLX scoring for the conducted tests. Figure 18: Process response time measured with CPU and GPU workload variation. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Participant** & **Mental** & **Physical** & **Temporal** & **Performance** & **Effort** & **Frustration** & **Total** \\  & **Demand** & **Demand** & **Demand** & **Performance** & **Effort** & **Levels** & **Total** \\ \hline
5 & 1 & 1 & 4 & 2 & 3 & 2 & 15 \\ Final Weight & 3 & 1 & 3 & 5 & 1 & 2 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: NASA-TLX weighting for the conducted tests. As presented in Section 5, the developed framework for quality inspection was tested in a pre-industrial environment in terms of the following:  
* Process operation cycle time: Reduction of 33.4% compared to the current state of the inspection process;
* Accuracy and robustness of voice recognition module: Based on the conducted experiments, the overall accuracy of the voice recognition module is 97%, and for the remaining 3%, a more detailed analysis on the wrongly identified commands is provided; the wrong identification of audio commands was caused due to wrong pronunciation by the operators or the local dialects of the users; in order to minimize these errors, different voice-capturing devices, but also the creation of filters to analyze the received data, will be investigated in the future;
* Accuracy of inspection process based on end user's requirements: The final version of the inspection algorithm was able to correctly detect and classify 98% of the objects of the validation dataset when the IoU threshold was constant and equal to 0.5, but also 88% of the objects while the IoU threshold was progressing; to further increase the accuracy of the inspection module, the authors will investigate, in the future, the addition of extra light installed either in a static position in the robotic cell or on the robotic manipulator. The proposed solution introduces a modular voice-enabled ROS2 framework for quality inspection, designed to operate in industrial environments. The central point of this approach is to provide human operators with control over the inspection process, minimize downtime and promote efficiency compared to existing methods. By prioritizing modularity, the framework achieves seamless integration with existing systems and provides the flexibility to introduce different technologies related to human-robot interaction and inspection.