The scheduling of rescue aircraft needs to be studied in depth because of its criticality for the general aviation rescue of forest fires. This paper constructs a collaborative schedule optimization model for general aviation rescue under the condition of multiple aircraft, from multiple rally points to multiple fire points, targeting the shortest rescue time and the lowest rescue cost in the context of forest fires based on the simulation verification of a forest fire that broke out simultaneously in multiple locations in Liangshan Prefecture, Sichuan Province, China. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm was used to find the optimal set of solutions satisfying the objective function: four feasible solutions. Then, the optimal solution was solved based on the weighted TOPSIS method, which was the optimal solution for this rescue task. The simulation results show that unnecessary flight times can be reduced by optimizing the schedule plan. Under the premise of ensuring rescue timeliness, the utilization rate of rescue aircraft was improved, and rescue costs were further reduced. The presented work provides a theoretical reference for the efficient development of general aviation rescue.

In this paper, we propose a trajectory planning method for high-density flights in a complex environment with multiple buildings in the city. Moreover, a simulation study of four-dimensional trajectory planning for aircraft is conducted with different flight densities in an urban environment. In this paper, the Rapidly Exploring Random Tree Star (RRT*) algorithm is improved to adapt to urban air traffic, including node expansion angle constraint, dynamic collision detection, adapting to stratified airspace, and adding virtual obstacles. The urban environment model with multiple obstacles is established, the stratified airspace and flight parameters of the vehicle are set, and simulation experiments are conducted. The experimental results demonstrate the effectiveness of the proposed algorithms and the necessity of real-time and unified management of high-density flights in urban air traffic. The proposal of maximum aircraft density and airspace layer construction in the stratified airspace of the simulation environment is obtained. Lastly, a four-dimensional trajectory planning method for high-density flights in an urban multi-building environment is provided.

This paper presents a conflict detection and resolution method based on a velocity obstacle method for flight conflicts in a three-dimensional space. With the location and speed information of the two aircraft, the optimal relief strategy is obtained using particle swarm optimization. Aiming at the problem of excessive computational complexity in solving flight conflicts in a three-dimensional space with a cylindrical flight protection zone, an improved method for narrowing the search range is proposed to achieve a rapid solution by simplifying the complicated three-dimensional problem into a two-dimensional problem. The generality and flexibility of the method is effectively verified through simulations in flight conflict scenarios which almost cover all common situations. The experimental results show that the method can accurately determine the conflict time and generate the optimal relief strategy for different scenarios. The improved method of optimizing-search-range can significantly improve the computational efficiency, taking about 0.4 s to find the optimal solution, which can be used in real-time conflict resolution. The study provides a new solution for the flight conflict resolution problem.

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.