# A Hybrid Architecture for Safe Human-Robot Industrial Tasks
## 8 Conclusions  
The paper presents a hybrid ROS2-based architecture addressing the challenges encountered when integrating collaborative robots in industrial settings. Specifically, the research introduces a reliable human detection and tracking system using the OptiTrack perception system, alongside an intention estimation algorithm and a robot control module. The integration of these components enables the implementation of a robust SSM collaborative scenario which can be used in industrial robotic workcells. The developed speed scaling algorithm performs a real-time risk assessment, adjusting robot speed based on operators' behaviors. It increases robot speed in low-risk situations and reduces it in higher-risk scenarios by calculating the minimum protective separation distance, considering current safety regulations and by evaluating operator intention predictions. The great advantage of the proposed technology is that the approach ensures operator safety while optimizing production efficiency, aiming at improving the industrial profitability in collaborative workcells. The experimental results demonstrate improved performance in balancing safety and speed. However, the selected OptiTrack tracking system is highly robust in dealing with partial occlusions, but it cannot completely eliminate the challenge of full occlusions where the operator is entirely out of view (i.e., blocked from view by other objects) and this is a drawback of the proposed technology. The technology can be used for any type of collaborative activity, e.g., inspection [21] or assembly [22]. This work represents the starting point for the development of a ready-to-use package that can be easily adapted to different types of robots and vision systems, e.g., depth cameras such as Orbec or Intel RealSense can replace the OptiTrack system, to adapt the cost of the workcell to the application's needs. The proposed control strategy can be integrated with advanced real-time trajectory replanning techniques, adaptable to unstructured environments. In addition, the SSM safety index could be dynamically correlated to the ergonomics of the operator, e.g., analyzing his posture, integrating exoskeletons, and tracking the entire human skeleton to correlate the presumed risk to the most exposed part of the human body,according to the actual regulations. Furthermore, object recognition and manipulation algorithms can be developed for real applications.  
Conceptualization, G.L.; Software, G.L.; Validation, D.C.; Writing--original draft, G.L.; Writing--review & editing, D.C.; Supervision, M.C.; Funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR); Missione 4 \"Istruzione e Ricerca\"--Componente C2; Investimento 1.1, \"Fondo per il Programma Nazionale di Ricerca e Progetti di Rilevante Interesse Nazionale (PRIN)\"--D.D. n. 104 del 2 febbraio 2022, Bando PRIN 2022--CUP I53D23001930006--Cod. prog: 2022 XXH9Z_002/ERC: PE8--Project title: \"DYNAmic Assessment and Mitigation of the Impact of Collaborative Applications (DYNAMICA)\".  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript: