Optimized Airspace Structures and Sequencing Method for Urban Logistics Droneport

As an emerging strategic industry, drone delivery operation has demonstrated significant potential in urban environments due to its efficiency and adaptability to complex scenarios. However, critical bottlenecks persist during the take-off and landing phases, where accident rates account for over 52% of total flight risks, severely limiting operational safety and throughput. While existing droneport designs and sequencing strategies draw inspiration from traditional aviation methods, they inadequately address the separation of take-off/landing flows and lack tailored solutions for logistics drones’ unique characteristics. To overcome these limitations, this paper presents an integrated framework combining innovative airspace design with dynamic sequencing optimization. First, a novel terminal airspace structure is proposed to enable simultaneous multi-drone operations through spatially segregated routes and dedicated zones, fundamentally resolving collision risks between ascending and descending drones. Second, a real-time sequencing model based on the Hungarian algorithm is developed, incorporating drone-specific factors such as battery levels and task priorities to formulate a cost matrix for optimal scheduling. Experimental results demonstrate that the proposed airspace design reduces take-off/landing time by 34.8% compared to conventional funnel-shaped configurations. The sequencing algorithm prioritizes high-value missions while reducing the average waiting time for low-battery drones by 47.3%, effectively alleviating endurance pressures. Notably, the sequencing algorithm prevents low-battery drones from crashing in the experiments. In comparison, under the sequencing of the comparison method, numerous drones crash due to low battery levels.