As urban populations continue to grow and road traffic congestion worsens, traditional ground logistics has become less efficient. This has led to longer logistics times and increased costs. Therefore, unmanned aerial vehicle (UAV) logistics has become increasingly popular. However, free-planned routes cannot meet the safety and efficiency requirements of urban airspace mobility. To address this issue, a public air route network for low-altitude logistics UAVs needs to be established in urban areas. This paper proposes a public route network planning method based on the obstacle-based Voronoi diagram and A* algorithm, as follows: Firstly, construct a city airspace grid model in which the characteristics of the airspace are mapped onto the grid map. Introduce an obstacle clustering algorithm based on DBSCAN to generate representative obstacle points as the Voronoi seed nodes. Utilize the Voronoi diagram to establish the initial route network. Then, conduct an improved path planning by employing the A* algorithm for obstacle avoidance in route edges that pass through obstacles. To ensure the safe operation of drones, set constraints on the route safety interval. This process will generate a low-altitude public air route network for urban areas. After considering the flight costs of logistics UAVs at different altitudes, the height for the route network layout is determined. Finally, the route network evaluation indicators are established. The simulation results demonstrate that compared with the city road network planning method and the central radial network planning method, the total route length is shortened by 7.1% and 9%, respectively, the airspace coverage is increased by 9.8% and 35%, respectively, the average network degree is reduced by 52.6% and 212%, respectively, and the average flight time is reduced by 19.4s and 3.7s, respectively. In addition, by solving the network model using the Dijkstra algorithm, when the energy cost and risk cost weights are 0.6 and 0.4, respectively, and the safety interval is taken as 15 m, the total path cost value of the planned trajectory is minimized.

This paper proposes a digital low-altitude airspace unmanned aerial vehicle (UAV) path planning method tailored for urban risk environments and conducts an operational capacity assessment of the airspace. The study employs a vertical–horizontal grid partitioning technique to achieve airspace grid-based modeling, classifying and configuring “management-operation” grids. By integrating multi-source heterogeneous data, including building structures, population density, and sheltering factor, a grid-based discrete risk quantification model is established to evaluate comprehensive mid-air collision risk, ground impact risk, third-party risk, and UAV turning risk. A path planning method considering the optimization of the turning points of parallelograms was proposed, and the Parallel-A* algorithm was adopted for its solution. Finally, an airspace operational capacity assessment model and a conflict simulation model for urban risk environments are developed to quantify the operational capacity of urban low-altitude airspace. Using Liuhe District in Nanjing as the experimental area, the study reveals that the environmental airspace risk decreases significantly with increasing flight altitude and eventually stabilizes. In the implementation of path planning, compared with the A* and Weight-A* algorithms, the Parallel-A* algorithm demonstrates clear advantages in terms of lower average comprehensive risk and fewer turning points. In the operational capacity assessment experiments, the airspace capacity across different altitude layers increases with flight altitude and stabilizes after comprehensive risk is reduced. This research provides a theoretical foundation for the scientific management and optimal resource allocation of urban low-altitude airspace, facilitating the safe application and sustainable development of UAVs in urban environments.

Aiming at the problem of the scheduling scheme of urban low-altitude logistics unmanned aerial vehicles (UAVs), this paper establishes a demand-based UAV scheduling scheme model using an improved simulated annealing algorithm, taking minimizing the cost of distribution as the objective function and considering restrictions such as UAV performance constraints, airspace constraints, and distribution constraints, among others. For verification, actual express data and airspace constraints in Shanghai are taken as examples. Two urban air traffic networks are constructed using road and building data. The analysis results show that the planning scheme of this model is superior to other forecasting models in terms of delivery cost and delivery time. In addition, this model can flexibly calculate the optimal scheduling scheme under the constraints of multiple parameters, according to the requirements of delivery volume, delivery distance, UAV performance, etc.

A reasonable definition of nodes load and capacity is essential for improving the robustness of scale-free networks against cascading failure, which has gained significant attention over recent years. This paper presents two methods for defining the load-capacity model: a degree-based method and a betweenness-based method. In these methods, the initial load and capacity of nodes were determined by considering the degrees and betweenness centrality of nodes and their neighbors. These values could be adjusted using both global and local parameters. This paper achieved load redistribution during cascading failures through targeted attacks on network nodes. In addition, this study applied load redistribution to cascading failure processes in networks by targeting network nodes. In order to evaluate the effectiveness of the proposed approach, this paper examines the impact of adjusting two parameters on the minimum critical tolerance coefficient and network robustness. Computer-generated scale-free networks and a real network were used for evaluation purposes. The findings indicated that higher global parameters resulted in a lower average robustness index. Moreover, our degree-based method demonstrated a smaller minimum critical tolerance coefficient and average robustness index compared to existing load definition methods. Therefore, the proposed methods enhanced the robustness and integrity of scale-free networks against attacks.

With the rapid development of urban air mobility, achieving safe and segregated flight for unmanned aerial vehicles amid the surging demand for low-altitude logistics has become a critical issue. This paper proposes a method for planning the public route network of urban low-altitude terminal logistics while considering environmental risks in the digital airspace. First, based on parallel system theory, we develop a digital airspace environment model that supports public route network planning by mapping physical and social elements to an artificial system. Furthermore, we establish a digital airspace grid partitioning system, develop grid access rules, and create a quantification model for urban low-altitude airspace environmental risks. Utilizing a layered airspace approach, this paper configures approach–departure grids, develops methods for initial public route network planning, and facilitates orderly re-planning of routes, ultimately establishing a hub-and-spoke public route network with segregation. This study conducts detailed case simulation studies based on realistic constraints, focusing on environmental risk, accurate grid configuration, comprehensive cost, algorithm complexity, and network scale. Simulation results demonstrate that the proposed method effectively constructs conflict-free networks, while maintaining low risks and inflection points. The findings align with the current development stage of urban air mobility characterized by the principle of ‘isolation first, then integration.’ This approach enables a gradual transition from route isolation to future integrated flight, thereby providing technical support for advancing low-altitude logistics operations.

With the development of modern military ship equipment, the airspace operation environment of shipboard aircraft is becoming increasingly complex. The objective and accurate evaluation of ship-terminal capacity is the basis of air traffic flow management for shipboard aircraft, and it is also the premise of improving the efficiency of airspace resources. First of all, this paper divides the airspace and ship terminal areas and realizes the route network planning for the arrival and departure of shipboard aircraft. Following this, according to the airspace characteristics of the ship terminal area and the operation process of the shipboard aircraft, an arrival-and-departure network flow model for shipboard aircraft is established by using capacity-limitation and flow-conservation conditions. Finally, simulated annealing (SA) is used to solve the maximum flow in the arrival-and-departure network flow model, and the capacity evaluation results of the ship terminal area are obtained. The results show that when the number of gates is n_g ≥ 7, the bottleneck in the ship terminal area’s operation capacity is the deck runway. When 3 ≤ n_g < 7, imbalanced take-off and landing tasks lead to a waste of runway resources, and when n_g < 3, the number of gates becomes the bottleneck which limits the capacity. With the number of gates being reduced from seven to three, the capacity is reduced from twenty sorties per hour to six sorties per hour. The model and core idea proposed in this paper can not only quickly evaluate the capacity of the terminal area of ships but also provide a solid foundation for the development of future fleet groups and the full use of maritime airspace.