关键词: 超低空威胁, 无人机群协同, 路径规划, 精英学习, 局部搜索, 改进鸽群优化算法

Abstract: An improved pigeon inspired optimization algorithm (PIO) is proposed to solve the path planning problem for unmanned aerial vehicles (UAVs) collaboration in minimum altitude electromagnetic threat area whose characteristic is dense distribution of obstacles, diverse types and strong electromagnetic interference. Furthermore, the safety of UAVs and the combat effectiveness of UAVs are enhanced. The characteristics of the minimum altitude electromagnetic threat area are analyzed and multiple types of obstacles in minimum altitude electromagnetic threat area are modeled. The elite learning factor and local search strategy are introduced in different stages of PIO to improve the convergence speed and global search ability of the improved algorithm. Simulation comparison experiments and virtual scene experiments are conducted to verify the presented method. The results indicate that the proposed algorithm has better global search capability and faster convergence speed. It can provide support for path planning of UAVs safely and effectively in minimum altitude electromagnetic threat area.

Key words: minimum altitude threat, UAVs collaboration, path planning, elite learning, local search, improved pigeon inspired optimization