Human-Robot Collaboration (HRC) is increasingly integrated into industrial settings, combining the efficiency of automation with the flexibility of human workers. To ensure safety, the ISO/TS 15066:2016 standard outlines four types of collaborative operation. Among these, Speed and Separation Monitoring (SSM) emerges as the most promising for enhancing accessibility in shared workspaces while maintaining high throughput. However, current implementations of SSM face significant challenges due to hardware, software, and regulatory limitations. Realizing the full potential of dynamically changing safety zones requires precise, real-time data on speed, trajectory, and intent of both human and robot. Unfortunately, existing monitoring sensors and algorithms are unable to reliably acquire these measurements. Moreover, even if such data were obtainable, it is not yet safety-rated for industrial applications. Ambiguities within ISO/TS 15066 and the lack of standardized terminology for different SSM methods further complicate integration. This paper introduces a refined classification of SSM based on separation distance calculation (Fixed Sized, Variable Sized, Variable Shaped) and monitoring approach (Static, Mobile), providing a structured framework for evaluating SSM implementations. While Fixed Sized SSM is widely used due to its simplicity, it lacks the real-time adaptability required for optimal collaboration. In contrast, Variable Sized and Variable Shaped SSM dynamically optimize safety zones but remain underutilized due to technological and regulatory barriers. The second categorization distinguishes between Static Monitoring, where the zones have a fixed position, and Dynamic Monitoring, where they adapt to the movement of the robotic system. By providing a structured terminology and exploring these categories with examples and research, this paper aims to advance the understanding and implementation of SSM. Addressing current challenges and ambiguities in standards is critical for the broader adoption of SSM, paving the way for safer, more efficient, and accessible collaborative robotic systems.
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