面向多模异构任务的无人机集群自主协同优化

Autonomous Collaborative Optimization of UAV Swarms for Multi-mode Heterogeneous Tasks

针对无人机集群同时遂行多个异构模式、异构价值、异构需求任务时的自主协同优化问题,构建了集群遂行多模异构任务协同优化模型,提出了一种基于重叠式联盟博弈的分布式协作算法。通过综合考虑任务模式、任务价值、任务需求,以及集群中不同无人机成员的资源情况,基于不同任务类型下联盟内任务成功率和效能计算,优化无人机任务选择和资源分配并实现算法收敛和系统稳定,以及优化的分布式多机协同。仿真结果表明,所提方法能有效提高系统效用和任务成功率,并能在不同环境下实现面向异构任务目标的高效协同。

For the autonomous collaborative optimization problem when unmanned aerial vehicle(UAV)swarms simultaneously perform multiple tasks of heterogeneous mode,heterogeneous value,and heterogeneous demand,a collaborative optimization model for cluster execution of multi-mode heterogeneous tasks is constructed,and a distributed collaborative algorithm based on overlapping coalition formation games is proposed.By comprehensively considering the mission mode,mission value,mission requirements,and the resources of different UAV members in the cluster,according to the calculation of the mission success rate and efficiency in the coalition under different mission types,the UAV mission selection and resource allocation are optimized and the algorithm convergence and system stability are implemented to achieve optimized distributed multi-machine collaboration.The simulation results show that the proposed method can effectively improve the system utility and task success rate,and can achieve efficient collaboration for heterogeneous task objectives in different environments.