基于群体意志统一的无人机协同围捕策略

针对无人机(UAV)协同围捕问题, 提出一种基于群体意志统一的围捕策略。受人类在协作任务中的认知机理启发, 引入“群体意志”定义无人机的协作认知, 并构建双回路认知模型, 借助图卷积网络对围捕无人机获取的局部态势进行融合认知, 有效减轻无人机系统的计算负载。依靠变分推断原理和生成式自动编码器对围捕无人机进行群体意志趋同学习, 依据Apollonius圆实现协同围捕, 使无人机集群涌现出更加智能化的围捕效果。通过对比仿真验证了所提策略的有效性和智能性。 

Multi-UAV round up strategy based on unity of group will

To solve the problem of unmanned aerial vehicle (UAV) coordinated rounding up, a strategy was proposed based on the unity of group will. Inspired by the cognitive mechanism of human beings in collaborative tasks, this paper introduces "group will" to define the collaborative cognition of UAVs, builds a double-loop cognitive model, and integrates the cognition of the local situation acquired by the rounded up UAVs with the help of the graph convolutional network, so as to effectively reduce the computing load of UAVs. On the basis of the variational inference principle and generative autoencoder, the group will convergence learning is carried out on the UAV, and the coordinated rounding up is realized on the basis of the Apollonius circle so that the UAV cluster emerges a more intelligent rounding up effect. The simulation results show the effectiveness and intelligence of the designed rounding up strategy.