Collaborative flight plan scheduling for urban low-altitude UAV

This study develops a public route network for low-altitude logistics UAV delivery path management, which integrates the Digital Elevation Model (DEM), OpenStreetMap (OSM) data, and ArcGIS geographic information within a 3D urban airspace environment. By using the Analytic Hierarchy Process (AHP), a dynamic task priority evaluation mechanism is developed. A mixed-integer programming (MIP) model is formulated to minimize comprehensive operational costs covering transportation cost, delay cost, and conflict cost. The model is solved by the a two-layered genetic algorithm within the Python environment, and a case study with 70 delivery tasks in the area of Beijing Jiaotong University is conducted. Results demonstrate that the proposed method effectively reduces both the flight conflict rate and overall operational cost in low-altitude UAV flight plan scheduling.