元胞蚁群算法的城市楼宇间无人机航路规划

近年来,城市环境中小型无人机越来越多,然而传统的航路规划算法通常将威胁简化,并不能很好地解决无人机在城市环境的航路规划问题。因此引入"元胞"定义飞行区间,对"数字元胞地图"以及"航路罚函数"进行定义,并利用蚁群算法进行航路规划,使其可以对任意不规则威胁进行规避。仿真对比实验表明,所提的基于元胞蚁群的算法可以在不对障碍模型进行简化的情况下进行合理规避,为无人机在城市环境中规划出一条安全可飞的航路。

A Path Planning for UAVs in Urban Building Areas Based on Cellular Ant Colony Algorithm

Small-sized UAVs are increasing more and more rapidly in urban circumstance in recent years. However, the traditional path planning methods always threaten simplification, and fail to solve the problem of path-planning in the circumstance efficiently Therefore, cell is introduced to define the flying space, digital cellular map and path cost function are defined. At the same time by using ant colony algorithm this paper plans the path for UAVs, thus avoiding any kinds of irregular obstacles. The simulation results show that the proposed method can avoid obstacles logically under condition of the obstacle model without simplification. By doing so, this plans a safe flyable path in urban building areas for UAVs.