为解决飞行中的航班如何安全高效的绕过空域中出现的限制区，提出一种基于A *改进蚁群算法的改航路径规划方法。首先，通过构建空域栅格化模型，将飞行空域划分为规则的网格单元，为路径规划提供了离散化的表示方式。针对传统蚁群算法在路径规划中的不足，如搜索盲目性、收敛速度慢以及转弯冗余等问题，引入基于A *算法的启发式信息和初始信息素分布策略，利用A *算法生成的次优路径分配初始信息素，加速算法收敛；同时，设计平滑因子量化路径转弯角度，显著减少冗余转弯次数，提升路径平滑度。此外，通过系统优化启发式因子、信息素因子、信息素蒸发因子和平滑因子等关键参数，进一步强化算法的鲁棒性。仿真结果表明：改进后的蚁群算法在空域栅格模型中的路径长度、转弯次数和平均最短路径等指标上均优于传统蚁群算法和A *算法。在实际空域环境验证中，改进蚁群算法规划的路径燃油消耗量相比减少了3%~4%，最小转弯半径严格符合国际民航组织安全标准与航空器性能约束。证明改进后的蚁群算法在提升改航效率、降低飞行复杂度的同时，兼顾了安全性与经济性。

In light of the issue of how to safely and efficiently navigate around restricted areas in the airspace during flight, a modified ant colony algorithm based on the A * algorithm was proposed for rerouting path planning. Firstly, through the construction of an airspace grid model, the flight airspace was divided into regular grid cells, and a discrete representation for path planning was provided. The shortcomings of the traditional ant colony algorithm in path planning, such as the blindness of search, slow convergence speed, and redundant turns, were focused on by the paper. Heuristic information in line with the A * algorithm and an initial pheromone distribution strategy were innovatively introduced. The suboptimal path generated by the A * algorithm was utilized to allocate initial pheromones, thereby accelerating the convergence of the algorithm. Additionally, a smoothing factor was designed to quantify the turning angle of the path, and the number of redundant turns was significantly reduced, enhancing the smoothness of the path. Furthermore, by systematically optimizing key parameters such as the heuristic factor, pheromone factor, pheromone evaporation factor, and smoothing factor, the robustness of the algorithm was further strengthened. The simulation results show that improved ant colony algorithm is found to outperform the traditional ant colony algorithm and the A * algorithm in terms of path length, number of turns, and average shortest path in the airspace grid model. In the verification of the actual airspace environment, the path planned by the improved ant colony algorithm is found to reduce fuel consumption by 3% ~ 4% compared to traditional methods, and the minimum turning radius is strictly compliant with the safety standards and performance constraints of the International Civil Aviation Organization. This proves that the improved ant colony algorithm enhances rerouting efficiency and reduces flight complexity while taking into account both safety and economy.