基于序列生成对抗网络的无人机集群任务分配方法

Task Allocation Method of UAV Clusters Based on Sequence Generative Adversarial Network

针对现有无人机集群任务分配算法在进行较大规模的任务分配时求解效率降低、求解时间大幅增加的问题,提出一种基于序列生成对抗网络的任务分配方法。通过构建包含战场信息特征提取网络和序列生成网络的序列生成模型,解决战场信息到任务分配序列的生成问题;构建基于多核多层卷积网络的判别模型,提出收益-评价双指导式策略梯度更新对模型进行训练,解决任务分配序列离散的问题,保证任务分配序列的质量。仿真结果表明,新方法在保证分配序列质量的情况下,能够高效地生成与战场信息对应的任务分配序列。

In response to the problem that the existing UAV cluster task allocation algorithm decreases the solution efficiency and increases the solution time significantly when performing larger scale task allocations,a task allocation method based on sequence generative adversarial network is proposed.A sequence generation model containing a battlefield information feature extraction network and a sequence generation network are constructed to solve the problem of generating a sequence from battlefield information to task allocation.A discriminative model based on a multicore-multilayer convolutional network is constructed,and a gain-evaluation dual-guided policy gradient update is proposed for model training,which solves the problem of discrete task allocation sequences and ensures the quality of task allocation sequences.Simulation results show that the proposed method can efficiently generate task allocation sequences corresponding to battlefield information while guaranteeing the quality.