多无人机三维群集算法的适用性研究与改进

针对多无人机群三维空间运动的复杂群集控制问题，提出了基于生物群集行为、依据Reynolds规则描述的三维群集控制算法。已有的研究大多将无人机群集运动简化为二维平面运动，但这不符合实际控制需求。为此，将群集控制算法和人工势场算法推广到三维无人机群集控制中，建立了三维无人机群空间运动模型，通过多种不同条件下的仿真，研究了两种算法在三维群集控制中的有效性。结果显示两种算法用于三维群集控制均具有一定效果，但相对二维所需要的条件更为苛刻。同时，注意到智能算法具有更好的群体聚集效果，而人工势场算法则避碰效果更迅速明显。据此，对人工势场算法和智能算法进行了改进，通过在距离大于平衡点时采用智能算法聚集，在距离小于平衡点时采用人工势场算法避碰，得到能同时获得更好的聚集、避碰效果的新的群集控制算法。

The Research and Improvement of Flocking Control for Three-dimensional UAV Swarms

Since the three-dimensional motion control of UAV swarms is quite complex, a three-dimensional flocking control algorithm based on biological collective behaviors and Reynolds rules is proposed. Most of previous research simplified UAV swarms as two-dimensional motion, but which is not in accordance with practical control demand. Therefore, flocking algorithm and artificial potential field algorithm are used in three-dimensional UAV swarms. Three-dimensional UAV swarms simulation model is built. Besides, whether the two algorithms are effective in controlling the three-dimensional UAV swarms through simulation in different initial conditions. The results show that the two algorithms have some effect on flocking motion control in three-dimensional space, but they need harsher terms than in two-dimensional plane. At the same time, it has been found that the intelligent algorithm is better in cohesion control but the artificial potential field algorithm is better in separation control. As a result, a new flocking control algorithms combined the two algorithms is proposed that the algorithm uses flocking algorithm in long distance cohesion control and artificial potential field algorithm in short distance separation control, which performs better in three-dimensional UAV swarms control.