基于优化的元胞蚁群算法的无人飞行器动态路径规划方法

针对无人飞行器路径规划蚁群算法收敛速度慢，提出了一种新的蚁群算法。首先，以栅格为环境地图，在蚁群算法搜索过程中加入了圈形轨迹识别算法，避免了无人飞行器出现折返跑的现象。其次，采用最优路径进行信息素更新，减少了蚁群算法在搜索过程中产生的盲目交叉和“蚂蚁遗失”现象。最后，引入了无人飞行器轨迹的尖角优化策略，更好地模拟了无人飞行器的飞行特征。仿真实验表明，新算法具有更好的全局搜索能力。

Dynamic Path Planning for UAV Based on Optimized Cellular Ant Colony Algorithm

In view of the slow convergence rate of path planning ant colony algorithm for UAV, a new ant colony method was proposed. Firstly, using the grid Instead of the environmental map, the loop trajectory identification algorithm was added in the search process of ant colony algorithm, which avoids the phenomenon of reentry run of unmanned aerial vehicle. Secondly, the optimal path was used to update pheromone which reduces the blind crossover and "ant loss" in the search process. Finally, the sharp angle optimization strategy of UAV was introduced, and the flight characteristics of UAV were simulated better. Simulation experiments show that the optimal path obtained by the new algorithm has better global search ability.