无人异构集群相较于单一类型、单一个体的无人平台,能够完成更为复杂的任务,同时对严苛战场环境有着更高的适应度.在无人异构集群协同执行任务时,任务分配是至关重要的环节,需要考虑异构无人平台和任务的多种约束和目标.传统的任务分配...无人异构集群相较于单一类型、单一个体的无人平台,能够完成更为复杂的任务,同时对严苛战场环境有着更高的适应度.在无人异构集群协同执行任务时,任务分配是至关重要的环节,需要考虑异构无人平台和任务的多种约束和目标.传统的任务分配方法分配效率低且难以处理大规模复杂任务.联盟博弈通过形成由若干参与者组成的联盟,根据个体的属性、偏好对群体进行划分,从而实现个体以及群体利益的最大化.本文以无人异构集群任务分配为背景,研究了基于改进联盟博弈算法的最优分配策略,基于可能的战场环境设计了模拟任务场景并完成实验验证.首先,考虑异构平台在任务中的初始位置、速度、携带资源以及个体声誉等因素,建立了基于空间自适应博弈(Spatial adaptive play algorithm, SAP)的联盟博弈的任务分配算法模型.其次,基于任务场景,搭建了任务所需的软件与硬件平台.最后,针对模拟的战场环境,对所提算法及搭建的异构无人集群平台进行了实验验证.验证结果表明,在异构无人集群平台重分配的任务背景下,本平台能综合考虑战场态势,寻找最优的任务分配方式,协调各作战单位完成任务目标.展开更多
This paper considers social welfare maximization for spatial resource sharing networks(SRSNs),in which multiple autonomous users are spatially located and mutual influence only occurs between nearby users.To cope with...This paper considers social welfare maximization for spatial resource sharing networks(SRSNs),in which multiple autonomous users are spatially located and mutual influence only occurs between nearby users.To cope with a lack of central control and the restriction that only local information is available,a spatial resource sharing game is proposed.However,individual selfishness in traditional game models generally leads to inefficiency and dilemmas.Inspired by local cooperative behavior in biological sys- tems,a community cooperation mechanism(CCM)is proposed to improve the efficiency of the game.Specifically,when a user makes a decision,it maximizes the aggregate payoffs for its local community rather than selfishly consider itself.It is analytically shown that with the bio-inspired CCM,the social optimum of SRSNs is achieved with an exchange of local information.The proposed bio-inspired CCM is very general and can be applied to various communication networks.展开更多
文摘无人异构集群相较于单一类型、单一个体的无人平台,能够完成更为复杂的任务,同时对严苛战场环境有着更高的适应度.在无人异构集群协同执行任务时,任务分配是至关重要的环节,需要考虑异构无人平台和任务的多种约束和目标.传统的任务分配方法分配效率低且难以处理大规模复杂任务.联盟博弈通过形成由若干参与者组成的联盟,根据个体的属性、偏好对群体进行划分,从而实现个体以及群体利益的最大化.本文以无人异构集群任务分配为背景,研究了基于改进联盟博弈算法的最优分配策略,基于可能的战场环境设计了模拟任务场景并完成实验验证.首先,考虑异构平台在任务中的初始位置、速度、携带资源以及个体声誉等因素,建立了基于空间自适应博弈(Spatial adaptive play algorithm, SAP)的联盟博弈的任务分配算法模型.其次,基于任务场景,搭建了任务所需的软件与硬件平台.最后,针对模拟的战场环境,对所提算法及搭建的异构无人集群平台进行了实验验证.验证结果表明,在异构无人集群平台重分配的任务背景下,本平台能综合考虑战场态势,寻找最优的任务分配方式,协调各作战单位完成任务目标.
基金supported by the National Basic Research Program of China(2009CB320400)the National Natural Science Foundation of China(60932002,61172062)
文摘This paper considers social welfare maximization for spatial resource sharing networks(SRSNs),in which multiple autonomous users are spatially located and mutual influence only occurs between nearby users.To cope with a lack of central control and the restriction that only local information is available,a spatial resource sharing game is proposed.However,individual selfishness in traditional game models generally leads to inefficiency and dilemmas.Inspired by local cooperative behavior in biological sys- tems,a community cooperation mechanism(CCM)is proposed to improve the efficiency of the game.Specifically,when a user makes a decision,it maximizes the aggregate payoffs for its local community rather than selfishly consider itself.It is analytically shown that with the bio-inspired CCM,the social optimum of SRSNs is achieved with an exchange of local information.The proposed bio-inspired CCM is very general and can be applied to various communication networks.
