面向多点侦察和通信服务的多无人机协同任务分配

Multi-UAV cooperative task allocation for multi-point reconnaissance and communication service

针对多无人机对多个异构任务目标进行侦察和通信服务的协同优化问题,通过考虑不同目标的任务要求和价值,以及多机协同增益与任务行为制约关系,构建斯坦伯格博弈模型,将上层无人机建立为博弈领导者,下层无人机建立为博弈的跟随者,并提出一种分布式策略更新迭代算法,实现了多无人机任务分配方案的稳定收敛以及系统任务收益优化.仿真结果显示,所提方法能有效提升多无人机系统同时完成多个任务的效益,并能在不同环境下实现面向异构任务价值的高效协同.

Aiming at the collaborative optimization of multi-UAV reconnaissance and communication service for multiple heterogeneous targets,the Stackelberg game model is constructed by considering the mission requirements and target values,as well as the restriction between multi-UAV coordination gain and task behavior.The upper-level drone is established as the leader of the game,while the lower-level drones are established as the followers of the game,and then a distributed strategy update iterative algorithm is proposed,which realizes the stable convergence of the multi-UAV task allocation scheme and the optimization of the task revenue.Simulation results show that the proposed approach can effectively improve the efficiency of multi-UAV systems to complete multiple tasks at the same time,and can achieve efficient collaboration for the values of heterogeneous tasks in different environments.