Safe and Efficient SSM Collaborative Strategy Integrating Human Head and Hands Tracking with a RGB-D Camera

In the evolving Industry 5.0 landscape, human-robot collaboration (HRC) is crucial to improve safety and productivity in shared industrial workspaces. This paper presents a cost-effective solution to track the human skeleton and identify the body parts that are most exposed to collision risk during a HRC task, i.e. the head and the hands, using a RGB-D camera. The depth images are processed through artificial intelligence (AI) algorithms, ensuring an accurate estimation of human motion and proximity to the robot. Furthermore, a robot control module has been developed to modulate the robot's speed based on the risk assessment calculated in real time, implementing a collaborative Speed and Separation Monitoring (SSM) scenario. To improve the efficiency of the workcell, the research proposes a multiplication coefficient to compute the minimum protective separation distance suggested by the ISO 10218-2, which weights the actual risk based on the biomechanical limits of the human part involved, according to the maximum permissible forces provided by the standard. Experimental results in a real robotic work cell demonstrate that the proposed system can achieve safety performance comparable to traditional motion capture systems, such as the OptiTrack; meanwhile it does not require operators to wear additional equipment and offers the advantages of reduced costs and a simpler setup, which is more affordable and adaptable to a wider range of industrial environments.