# A Voice-Enabled ROS2 Framework for Human-Robot Collaborative Inspection
## 1 Introduction
### Progress beyond the State of the Art  
Despite the fact that HRC frameworks for quality inspection operations are available in the literature, the authors have identified several limitations in them, as presented in Table 1.  
Summarizing the previous analysis, there is a gap in a modular solution for quality inspection operations' execution in HRC industrial shopfloors supporting easy integration with key human-robot interaction modules. In this paper, a modular framework is presented based on a core orchestration module, including several interfaces for connection with quality inspection, visualization, robot controlling and robot voice-based interaction modules. This framework design and implementation focuses on the automated quality inspection of parts in human-robot collaborative environments where the human can also observe the assembly product and request new inspection tasks by the robot on demand. The novelty of the proposed solution relies on developing an end-point-based framework consisting of different modules for the following:  
* Human--robot interaction techniques based on voice capturing solutions towards the efficient and easy collaboration of the resources during the tasks' execution;
* Vision-based quality inspection solutions using AI techniques; \begin{table}
\begin{tabular}{c c c} \hline \hline
**Existing Quality** & **Main Topic** & **Limitations Compared to** \\
**Inspection Solutions** & & **Proposed Framework** \\ \hline
[42] & Augmented Reality and & No robotic manipulator to alleviate \\  & Remote Assistances & the repetitive task from the human \\  & & and no in-line inspection \\
[43] & Inline Inspection with an & Focused on a robot-centric approach \\  & Industrial Robot & and excluded the human factor and \\
[44] & Framework for HRC & benefits of HRC \\  & through Simulation & Results are based on simulated \\  & & environments and the human has \\  & & minimal control over the process \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Existing HRC inspection frameworks and gap identification. * Robotic resource monitoring and controlling solutions for the successful execution of the required tasks;
* Visualization of the manufacturing shopfloor layout, robotic resource motions, identified voice commands and quality inspection results. The proposed solution is based on the ROS framework, with the integration of the ROS2 version [45] also being investigated. After evaluating the pros and cons of ROS2, the authors selected the ROS2 version, even for the designing phase, due to the following points:  
* ROS2 is based on the Data Distribution Service (DDS) [46] standard architecture, enabling the framework to have quality of service parametrization;
* ROS2 has no single point of failure, so the system will not fail if one ROS node malfunctions (typical in unstable settings like the industrial one);
* ROS2 consistently delivers data over lossy links so the usage of multiple machines connected through Wi-Fi is enabled, running the required modules of the framework;
* ROS 1 st version distributions are about to be discontinued, receiving no updates after 2025 and being replaced by ROS2 distributions. A robust quality inspection module has been developed and proposed alongside a voice-based interaction mechanism in order to facilitate accurate collaborative inspection under HRC workspaces. A brief comparison between the existing ROS2 based solutions in manufacturing is being presented in Table 2, highlighting their main limitations compared to the proposed voice-enabled ROS2 framework for HRC operations.