## 2 Related Work  
The systematic safety analysis and the systemic modeling of the hazardous situations in collaborative robots with humans has not yet been widely researched [19]. More focus has been applied on the identification of hazards on existing robotic systems to measure their dependability by employing system theory methods [11]. Yet, it has been proven that classic hazard identification approaches such as FTA and FMECA [13] are not well-suited for human-robot collaboration applications, since they cannot deal with unpredictable human-robot interactions (HRI). In another approach presented in [15], the authors systematized the pairing of HAZOP and Unified Modeling Language (UML), and demonstrated results also for collaborative scenarios, excluding the formal point of view and focusing on an informal solution, by gathering as much information as possible from brainstorming and meetings. Authors in [18; 20] studied the possibility of utilization of multi-robots with human workers in a laboratory environment by exploiting STPA and fault tree analysis to identify the potential safety hazard scenarios, their causal factors, and concluded with a set of recommendations. The approach proposed in [21] examined a case related to multi-robot systems considering the coordination, cooperation and collaboration aspects in accordance to which a risk identification study is performed by employing STPA to extract a set of risk scenarios related to different types of hierarchical coordination architectures in addition to their factors, and afterwards, the Bowtie method is utilized for the evaluation of the obtained scenarios. The method applied on a collaborative environment with multiple mobile robots and a great number of hazardous scenarios has been identified. From a different perspective, the authors in [22] introduced the SAFER-HRC methodology, which concerns an exhaustive exploration of all possible execution workflows of the application, the automatic identification of hazards within the workflows and the determination of their associated risk with the respective ISO standards, while the system also introduces provisions for risky hazards and verifies their effectiveness by computing residual risks. The method has been tested on an actual collaborative scenario and proved efficient in identifying hazards, yet the performance of it significantly depends on the accuracy of the model and the modeling of the probabilistic distribution of human's unintended behavior or errors. As we study deeper, the research endeavors conducted for the systematic identification of hazards and risks, it becomes apparent that the identification of risks in real time, i.e., during the operation phase of the system, especially for HRC applications, is an imperative yet challenging task. Of such applications can be considered the current trend of automation and data exchange in manufacturing technologies of Industry 4.0 [23], as well as applications related to personal assistive robots [24], where also true and even physical interaction is required. In such applications, there is the need to predict the next accident more efficiently, yet the challenge of measuring what the actual safety level of a system is at a certain moment in time at a given context [25] is still under investigation, regardless of the introduction of new accident models and the view of safety as an emergent system property. The authors in [26] proposed a method to measure the gap between system design and operation as a metric for safety and introduced the Risk SituatiOn Awareness  
Figure 1: The flowchart of the proposed methodology. Provision (RiskSOAP) indicator based on which one can compare the safety constraints of existing systems compared to their ideal set of safety constraints that derive from their STPA analysis, and calculate a situational awareness indicator for the system under study.