Urban Metric Maps for Small Unmanned Aircraft Systems Motion Planning
This addresses the need for accurate motion planning in urban settings for small UAS, but it is incremental as it focuses on metric development and evaluation rather than a breakthrough.
The paper tackles the problem of low-altitude urban flight planning for small Unmanned Aircraft Systems by defining map-based and path-based metrics to evaluate motion plan quality, and demonstrates their application through Monte Carlo case studies in a New York City environment, showing performance as a function of planning algorithm, location, range, and flight altitude.
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.