基于信息素决策的无人机集群协同搜索算法

针对无人机(UAV)集群在未知环境中无先验信息条件下的搜索问题，提出了一种以信息素为决策机制的无人机集群协同搜索算法。首先，考虑无人机通信约束，建立了有外部节点的星型网络通信和无外部节点的自组织网络通信2种形式的搜索模型。其次，通过环境地图向信息素地图映射的方法建立任务环境模型。将任务过程分为3个阶段，在搜索阶段，无人机通过不断地移动实现本机信息素地图的更新；在通信阶段，通过通信网络实现多机信息素地图的融合；在决策阶段，根据局部信息和全局信息做出决策，并将栅格信息素浓度作为决策函数来引导无人机的位置更新。基于信息素地图覆盖率来定量描述搜索效果。最后，仿真结果表明，所提算法能够对区域进行覆盖搜索，表现为搜索效率高、抗毁性强、受集群的初始位置影响小。 

Cooperative search algorithm based on pheromone decision for UAV swarm

To solve the problem of Unmanned Aerial Vehicle (UAV) swarm search in unknown environment without prior information, this paper proposes a UAV swarm cooperative search algorithm with pheromone as decision mechanism. Firstly, considering the communication constraints of UAV, two search models which are star communication network with external nodes and self-organizing communication network without external nodes are established. Secondly, the task environment model is established by mapping environment map to pheromone map. In this paper, the task process is divided into three stages. In the search stage, the UAV can update the local pheromone map by moving constantly. In the communication stage, the fusion of UAV swarm pheromone maps is realized through the communication network. In the decision-making stage, the decision is made based on the local information and the global information, and the grid pheromone concentration is taken as the decision function to guide the position update of the UAV. Based on pheromone map coverage rate, the search results are quantitatively described. Finally, the simulation results show that the method proposed in this paper can search and cover the region, which is characterized by high search efficiency, strong destruction resistance and little influence by the initial location of the swarm.