数学建模及其应用

针对不同能力的无人机集群协同区域搜索中区域划分和航路规划，本文提出一种不同权重的区域划分以及划分后的航路规划算法，即不同能力无人机的加权Voronoi图划分和以能力分配的区域内航路规划.首先，利用栅格法将搜索区域离散化，从栅格的中心点集合中经过多次迭代选出较为分散且数量与无人机数量相同的点作为加权Voronoi图的生成元，然后设计无人机搜索能力的计算函数，根据无人机搜索能力计算其加权Voronoi算法中的生成元权重并用扫描边界法生成加权Voronoi图；然后，将各分区根据各自无人机搜索宽度栅格化，以各栅格的中心点为航路点，设计遗传算法航路规划并与弓形算法航路作比较.仿真结果表明，本文提出的不同能力无人机协同搜索方案设计合理，对比了遗传算法和弓形算法所得搜索路线，发现大多数情况下弓形算法表现更好.

In order to solve the problem of regional division and route planning in the collaborative area search of UAV swarms with different capabilities, this paper proposes a regional division with different weights and a route planning algorithm after division, that is, the weighted Voronoi diagram division of UAVs with different capabilities and the regional route planning based on capability allocation. Firstly, the grid method is used to discretize the search area, and the more scattered points with the same number as the number of UAVs are selected from the center point set of the grid as the generators of the weighted Voronoi graph after many iterations, and then the calculation function of the UAV search capability is designed, and the weighted Voronoi diagram in the weighted Voronoi algorithm is calculated according to the UAV search ability, and the weighted Voronoi graph is generated by the scanning boundary method. Then, each partition was rasterized according to its respective UAV search width, and the center point of each grid was used as the waypoint, and the genetic algorithm route planning was designed and compared with the bow algorithm route. The simulation results show that the cooperative search scheme of UAVs with different abilities proposed in this paper is reasonable, and the search routes obtained by the genetic algorithm and the bow algorithm are compared, and it is found that the bow algorithm performs better in most cases.