This paper presents a methodology by which risk to ground based elements from nearby UAS operations can be estimated and used to plan UAS paths to achieve acceptable risk levels. Risk estimates are generated using a set of reasonable assumptions that produce a worst-case estimate of risk when applied to open data sets. These readily-available data sources are used to generate estimates of human population below the airspace in which UAS operations will take place. From this, properties of the UAS are used to infer likely collision zones in worst-case failure scenarios of the UAS and estimates of risk to the overflown human population are generated. In this paper we derive a risk-assessment methodology, which we call Risk-Aware Navigation and Guidance for Resilience (RANGR), identify readily-available sources of population data, and define the assumptions employed to generate worst-case estimates. In addition, we also demonstrate path planning capabilities of RANGR in simulation.
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