| Citation: | WU Ao, YANG Rennong, LIANG Xiaolong, et al. Cooperative search algorithm based on pheromone decision for UAV swarm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(4): 814-827. doi: 10.13700/j.bh.1001-5965.2020.0026(in Chinese) |
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.
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