Abstract:

For overcoming the disadvantages of traditional ant colony algorithm in UAV 3D path planning, such as slow convergence speed and high space complexity, this paper proposes a 3D path planning method for UAV based on improved ant colony algorithm. In this method, the local search strategy and the initial pheromone adjustment factor are improved, and the path offset factor is added to the heuristic function, which reduces the complexity of the path search space and improves the search efficiency and convergence speed of the algorithm. In the search space established using DEM digital elevation data, compared with existing algorithms, the algorithm in this paper shortens the planning path by 24.08%, and the running time of the algorithm is reduced by about 11.56%, which show the feasibility and effectiveness of the improved ant colony algorithm in the 3D path planning of UAVs.

Key words: 3D path planning, pheromone adjusting factor, path offset factor, ant colony algorithm

摘要：

针对蚁群算法在无人机（UAV）三维航迹规划中存在的收敛速度慢、空间复杂度高的缺点，提出了一种基于改进蚁群算法的无人机（UAV）三维航迹规划方法。该方法改进了局部搜索策略、初始信息素调整因子并在启发函数中加入了路径偏移因子，从而降低了航迹搜索空间的复杂度，提高了算法的搜索效率和收敛速度。在利用DEM数字高程数据建立的搜索空间中，该算法与现有算法相比，规划航迹缩短约24.08%，运行时间减少约11.56%，表明改进蚁群算法在无人机（UAV）三维航迹规划中的可行性和有效性。

关键词: 三维航迹规划, 信息素调整因子, 路径偏移因子, 蚁群算法