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During the last decades, collaborative robots capable of operating out of their cages are widely used in industry to assist humans in mundane and harsh manufacturing tasks. Although such robots are inherently safe by design, they are commonly accompanied by external sensors and other cyber-physical systems, to facilitate close cooperation with humans, which frequently render the collaborative ecosystem unsafe and prone to hazards. We introduce a method that capitalizes on partially observable Markov decision processes (POMDP) to amalgamate nominal actions of the system along with unsafe control actions posed by the System Theoretic Process Analysis (STPA). A decision-making mechanism that constantly prompts the system into a safer state is realized by providing situation awareness about the safety levels of the collaborative ecosystem by associating the system safety awareness with specific groups of selected actions. POMDP compensates the partial observability and uncertainty of the current state of the collaborative environment and creates safety screening policies that tend to make decisions that balance the system from unsafe to safe states in real time during the operational phase. The theoretical framework is assessed on a simulated human–robot collaborative scenario and proved capable of identifying loss and success scenarios.

Decision Making with STPA through Markov Decision Process, a Theoretic Framework for Safe Human-Robot Collaboration