  Low-altitude urban flight planning for small Unmanned Aircraft Systems (UAS)
requires accurate vehicle, environment maps, and risk models to assure flight
plans consider the urban landscape as well as airspace constraints. This paper
presents a suite of motion planning metrics designed for small UAS urban
flight. We define map-based and path-based metrics to holistically characterize
motion plan quality. Proposed metrics are examined in the context of
representative geometric, graph-based, and sampling-based motion planners
applied to a multicopter small UAS. A novel multi-objective heuristic is
proposed and applied for graph-based and sampling motion planners at four urban
UAS flight altitude layers. Monte Carlo case studies in a New York City urban
environment illustrate metric map properties and planner performance. Motion
plans are evaluated as a function of planning algorithm, location, range, and
flight altitude.
