基于搜索意图交互的无人机集群协同搜索算法

针对无先验信息条件下无人机集群的协同搜索问题，提出一种以覆盖率为引导，以机间安全距离、通信距离、偏航角调整及搜索边界等为约束的无人机集群协同搜索算法。通过建立环境地图矩阵对任务区域进行描述，进一步定义环境地图更新算子实现搜索过程中环境地图的快速更新。设计了集群协同搜索任务的回报函数，采用粒子群算法进行求解，得到每架无人机在已知环境地图下的最优决策，即决策意图。每架无人机在获取其他成员决策意图的基础上重新进行决策，实现协同决策。针对不同规模集群提出了集中式和分布式2种协同决策方案。仿真结果表明，所提算法能够对存在未知威胁的不规则任务区域进行有效覆盖搜索，覆盖率远高于不进行协同决策的个体决策方法。 

Cooperative search algorithm for UAV swarm based on search intention interaction

Aimed at the cooperative search problem of UAV swarm without prior information, a cooperative search algorithm of UAV swarm is proposed, which is guided by coverage rate and constrained by safe distance, communication distance, yaw angle adjustment and search boundary. The task area is described by establishing the environmental map matrix, and the environmental map update operator is further defined to realize the rapid update of the environmental map in the search process. The return function of swarm cooperative search task is designed, and particle swarm optimization algorithm is used to solve the problem in order to obtain the optimal decision of each UAV under the known environment map, namely the decision intention. Each UAV makes decisions again based on acquiring the decision-making intentions of other members to achieve cooperative decision-making. Two cooperative decision-making schemes, centralized and distributed, are proposed for swarms with different scales. The simulation results show that the proposed algorithm can effectively search the irregular task area with unknown threat, and the coverage is much higher than that of the individual decision method without cooperative decision.