科技创新2030“新一代人工智能”重大项目(2018AAA0102405); 国家自然科学基金(U20B2071, T2121003, U1913602, U19B2033)
多无人机集群协同规划是无人机自主控制领域的前沿热点之一。 提出了一种基于无监督学习离散鸽群优化的多机侦察任务分配方法。 通过Dubins路径建立无人机模型，给出了简化的传感器模型和侦察目标的模型，并建立了无人机集群任务分配的模型和性能指标函数。采用无监督学习方法对侦察目标进行柔性分组，利用改进离散鸽群优化策略对该任务分配模型进行了求解，以有效解决无人机机间任务负载不平衡问题，可提高无人机集群侦察的效率。通过仿真对比实验和三维态势视景仿真平台综合实验，验证了所提出方法的可行性和有效性。
Cooperative planning of multiple unmanned aerial vehicles (UAVs) swarm is a hotspot research issue in the field of UAVs autonomous control. A novel task allocation method is proposed for multi-UAV reconnaissance based on unsupervised learning discrete pigeon-inspired optimization. Firstly, a model of UAV is established by Dubins path. Secondly, a simplified sensor model and a reconnaissance target model are given, and a task allocation model of multi-UAV and index function of performance are established. The reconnaissance targets are grouped flexibly by the unsupervised learning method, and then the task allocation model is solved by using the improved discrete pigeon-inspired optimization strategy, effectively solving the unbalanced task load between UAV and improving the efficiency of UAV swarm reconnaissance. Finally, the method proposed in this paper is valid through the simulation comparison experiments and the three dimensional situational scene simulation platform experiments.
龙泓, 魏晨, 段海滨.基于无监督学习离散鸽群 优化的多无人机侦察任务分配[J].空军工程大学学报,2023,24(5):16-22复制