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针对多UAV协同搜索问题,建立了基于搜索概率图的UAV环境信息描述模型,提出了一种基于多蚁群算法的协同目标搜索算法。该算法由多个蚂蚁种群构成,每个蚂蚁种群负责搜索一架无人机的路径。蚂蚁个体在搜索路径时通过其所在群体的信息素的引导以趋向最优路径,同时,受到来自其它种群的信息素的排斥作用进而避免无效搜索。实验结果表明,该方法能有效地实现多UAV之间的协同,实现路径搜索,减少路径交叠,提高了搜索效能。
For multi-UAV collaborative search problem, a description model of UAV environment information based on search probability map is established, and a collaborative target search algorithm based on multi-ant colony algorithm is proposed. The algorithm consists of multiple ant populations, each of which is responsible for searching a drone’s path. Ant individuals are guided by the pheromones of their group to search for optimal paths while searching for paths, and at the same time they are rejected by pheromones from other populations to avoid invalid searches. Experimental results show that this method can effectively achieve the coordination among multiple UAVs, realize path search, reduce path overlap and improve search efficiency.