Qinyue He

In mountainous wildfire response, unmanned aerial vehicle (UAV) emergency services depend on rapidly deployable and spatially reliable vertiports. However, existing site-selection methods insufficiently support continuous UAV operations in topographically complex terrain. This study develops a geographic information system (GIS)-based framework integrating Multi-Criteria Decision-Making (MCDM) and k-medoids clustering to shift UAV vertiport planning from individual site suitability ranking to networked deployment. The framework organizes feasible vertiport candidates into sector-based support staging nodes for coordinated multi-node wildfire response. Applied to the Xichang wildfire, the selected vertiport candidates reduced the mean first-arrival time by up to 71.4% compared with existing fire stations. Under differentiated reload-time assumptions, the staging node support mode further reduced the mean sortie cycle time by up to 56.85% and increased the mean delivery capacity from 17 to 42 fire-extinguishing bombs per UAV per hour. These results indicate that combining vertiport suitability evaluation with staging node-based logistical coordination improves the spatial organization and operational efficiency of UAV-enabled wildfire suppression. The framework offers decision support for networked UAV vertiport and staging node deployment in mountainous wildfire response.

The large-scale operation of multiple fixed-wing unmanned aerial vehicles (UAVs) in shared airspace requires efficient flight conflict detection and resolution to ensure aviation safety. However, existing research predominantly lacks collaborative optimization of multi-dimensional maneuver recommendations and struggles with dynamic priority allocation in complex multi-UAV scenarios, leaving a critical gap in the field. To bridge this gap, this paper proposes a Complex Network-Based Multi-UAV Conflict Resolution (NCR) method, which first constructs a three-dimensional (3D) flight conflict detection and resolution model for fixed-wing UAVs. The core innovation lies in mapping dynamic multi-UAV conflict scenarios into a flight conflict network, where UAVs serve as nodes and conflict urgencies act as edge weights. By calculating network and node robustness, the method accurately identifies key UAVs requiring immediate maneuver. Subsequently, taking the minimum variation in the velocity vector as the core objective, NCR iteratively searches for optimal resolution recommendations for these key UAVs using an improved fitness function until the conflict network collapses. Simulation and comparative experiments in 3D airspace, including evaluations against serial-based resolution, random-recommendation resolution, and a classical reactive baseline, demonstrate that NCR efficiently resolves multi-UAV conflicts with minimal trajectory deviations and fewer maneuvering UAVs. Furthermore, a macro-micro bi-level validation architecture based on a six-degree-of-freedom (6-DOF) aerodynamic platform is introduced to verify the physical executability of the proposed strategies. Results demonstrate that by incorporating a dynamic aerodynamic compensation margin, the inevitable trajectory tracking deviations caused by system inertia are enveloped within the safety threshold, ensuring absolute flight safety in engineering practice. Notably, as conflict complexity increases, NCR exhibits prominent advantages in reducing velocity variation costs, minimizing the number of maneuvering UAVs, and avoiding unnecessary trajectory deviations.