In environments with unpredictable agent traversal (e.g. Industry 5.0 (I5) workfloors, hospitals, malls), a robot benefits from a harm risk-based criterion that judges whether evasive behaviors are actually necessary. Such an approach ensures safety when plan adjustment is needed, while preserving efficiency and agility when not, rendering the I5 paradigm more practicable and reducing congestion. In this paper, we propose a risk estimator based on mean free path that maps robot actions to probabilities of a harmful outcome. The mapping has a simple underlying construction, and the resulting risk space is continuous and differentiable across actions (if the underlying hazard map is such), making our model easily compatible with both optimization algorithms and Reinforcement Learning (RL) policies. It is expandable beyond accounting merely for visible agents, allowing for the easy inclusion of contributions due to latent hazards, such as blind spots, or even hazard map adjustments due to agent Theory of Mind (ToM) (e.g. awareness of robot). We show via Monte Carlo simulation that, for harm probabilities above 0.1, the accuracy of our method does not fall below a 5% error relative to the Ground Truth (GT) value.