基于飞行角度优化的蚁群改进算法

Improved Ant Colony Algorithm Based on Flight Angle Optimization

针对无人飞行器路径规划问题,实现了排序蚁群算法。在此基础上,引入了针对无人飞行器飞行特征的飞行角度优化策略,并建立了转移概率的更新原则。通过模拟飞行环境建立栅格化地图,进行仿真验证,输出无人飞行器的最优路径,以验证最优解的质量和算法的收敛速度。结果表明,该方法能有效消除飞行过程中的尖角和折返现象,更加符合无人飞行器的飞行特征。与传统的方法相比,算法的收敛速度和最优解的质量均得到了提升。

For the path planning of unmanned aerial vehicle(UAV), the ranked ant system(RAS) algorithm was realized. On this basis, the flight angle optimization strategy for UAV flight characteristics was introduced, and the updating principle of transfer probability was established. The raster map was built to simulate the flight environment, and the simulation verification was carried out to output the optimal path of the UAV, verify the quality of the optimal solution and the convergence speed of the algorithm. The results show that the method can effectively eliminate the sharp angle and reversion phenomenon in the flight process, which is more in line with the flight characteristics of the UAV. Compared with the traditional methods, the convergence speed and the quality of the optimal solution are improved.