Recently,with the increasing complexity of multiplex Unmanned Aerial Vehicles(multi-UAVs)collaboration in dynamic task environments,multi-UAVs systems have shown new characteristics of inter-coupling among multiplex g...Recently,with the increasing complexity of multiplex Unmanned Aerial Vehicles(multi-UAVs)collaboration in dynamic task environments,multi-UAVs systems have shown new characteristics of inter-coupling among multiplex groups and intra-correlation within groups.However,previous studies often overlooked the structural impact of dynamic risks on agents among multiplex UAV groups,which is a critical issue for modern multi-UAVs communication to address.To address this problem,we integrate the influence of dynamic risks on agents among multiplex UAV group structures into a multi-UAVs task migration problem and formulate it as a partially observable Markov game.We then propose a Hybrid Attention Multi-agent Reinforcement Learning(HAMRL)algorithm,which uses attention structures to learn the dynamic characteristics of the task environment,and it integrates hybrid attention mechanisms to establish efficient intra-and inter-group communication aggregation for information extraction and group collaboration.Experimental results show that in this comprehensive and challenging model,our algorithm significantly outperforms state-of-the-art algorithms in terms of convergence speed and algorithm performance due to the rational design of communication mechanisms.展开更多
It is comment that unmanned aerial vehicles （UAVs） have limitation on information cap- turing in reality applications. Therefore, online method of motion planning is necessary for such UA- Vs. Gyroscopic force （GF...It is comment that unmanned aerial vehicles （UAVs） have limitation on information cap- turing in reality applications. Therefore, online method of motion planning is necessary for such UA- Vs. Gyroscopic force （GF） is used for obstacle avoidance as an online method. However, classical GF has shortcoming in generating orbit for UAV with high velocity because the GF results in a time- varying turning radius. Modified gyroscopic force （MGF） given by function of velocity can overcome this shortcoming and help get a more practical control law for avoidance. MGF can also be used to implement the guidance of UAV by designing particular active conditions. Interactions in forms of stress function and damping force are introduced so that an UAV group can have coordinated motion. By combining controls of MGF and interactions, motion planning of UAV group in obstacle environ- ment can be implemented.展开更多
针对故障条件下的多无人机分组编队控制问题,提出一种基于同步分布式模型预测控制(distributed model predictive control,DMPC)的分组编队控制算法。首先,建立考虑虚拟领导者的分组分层控制框架。接着,对编队中的不可恢复故障进行建模...针对故障条件下的多无人机分组编队控制问题,提出一种基于同步分布式模型预测控制(distributed model predictive control,DMPC)的分组编队控制算法。首先,建立考虑虚拟领导者的分组分层控制框架。接着,对编队中的不可恢复故障进行建模,并提出交互健康判别机制及“故障隔离”策略,结合分组分层控制框架提出故障条件下分组编队控制方案。然后,将故障模型与同步DMPC理论相结合,根据“故障隔离”策略,设计故障条件下的单组编队控制算法,并进一步根据控制方案给出分组编队控制算法,且基于Lyapunov理论证明控制算法下编队系统的稳定性。最后,通过仿真验证所设计算法在故障条件下的有效性和优越性。展开更多
针对无人机群组在军事对抗复杂环境中,网络拓扑结构动态变化,提出了一种面向无人机群组的轻量动态密钥管理方案,旨在解决无人机加入及退出、批量加入及退出、群组合并及分裂等网络拓扑动态变化导致密钥更新问题,同时在网络拓扑没有变化...针对无人机群组在军事对抗复杂环境中,网络拓扑结构动态变化,提出了一种面向无人机群组的轻量动态密钥管理方案,旨在解决无人机加入及退出、批量加入及退出、群组合并及分裂等网络拓扑动态变化导致密钥更新问题,同时在网络拓扑没有变化的情况下进行本地周期性更新,提高无人机群组密钥更新效率。将参与构造的秘密信息分为用户空间、剩余空间和撤销空间,用户空间可以在接收密钥组管理器(key group manager, KGM)的广播消息后计算恢复出会话组密钥,而剩余空间和撤销空间无法计算恢复出组密钥。密钥更新过程中,KGM利用空闲时间提前在密钥池中选取密钥进行预计算处理,降低KGM因构造广播消息进行复杂计算导致的时延问题。分析和实验仿真表明,该方案具有前向和后向安全性、抗共谋攻击和节点撤销能力,与现有无人机密钥管理方案相比,该方案优化了计算和通信开销,且节点存储开销较小,适用于动态拓扑变化的无人机群组网络。展开更多
研究了异构多无人机系统的任务分配问题,将任务根据类型分组,以总收益最大为目标函数,考虑无人机能力、任务分组等约束条件建立了MUAP-GT(multiUAV task assignment problem for grouped tasks)模型.在对MUAP-GT进行对偶分解的基础上,...研究了异构多无人机系统的任务分配问题,将任务根据类型分组,以总收益最大为目标函数,考虑无人机能力、任务分组等约束条件建立了MUAP-GT(multiUAV task assignment problem for grouped tasks)模型.在对MUAP-GT进行对偶分解的基础上,首先设计了带共享存储中心的分布式拍卖算法,通过无人机个体目标的最大化实现了整体目标最大化.进一步地结合最大一致性算法将共享存储中心移除,使算法变为完全分布式的算法.最后通过仿真实验表明了算法的有效性与收敛性.展开更多
基金supported by the Key Research and Development Program of Jiangsu Province of China(No.BE2022157)the National Natural Science Foundation of China(Nos.62303111,62076060,and 61932007)+1 种基金the Defense Industrial Technology Development Program(No.JCKY2021214B002)the Fellowship of China Postdoctoral Science Foundation(No.2022M720715).
文摘Recently,with the increasing complexity of multiplex Unmanned Aerial Vehicles(multi-UAVs)collaboration in dynamic task environments,multi-UAVs systems have shown new characteristics of inter-coupling among multiplex groups and intra-correlation within groups.However,previous studies often overlooked the structural impact of dynamic risks on agents among multiplex UAV groups,which is a critical issue for modern multi-UAVs communication to address.To address this problem,we integrate the influence of dynamic risks on agents among multiplex UAV group structures into a multi-UAVs task migration problem and formulate it as a partially observable Markov game.We then propose a Hybrid Attention Multi-agent Reinforcement Learning(HAMRL)algorithm,which uses attention structures to learn the dynamic characteristics of the task environment,and it integrates hybrid attention mechanisms to establish efficient intra-and inter-group communication aggregation for information extraction and group collaboration.Experimental results show that in this comprehensive and challenging model,our algorithm significantly outperforms state-of-the-art algorithms in terms of convergence speed and algorithm performance due to the rational design of communication mechanisms.
基金Supported by the National Natural Science Foundation of China(61350010)
文摘It is comment that unmanned aerial vehicles （UAVs） have limitation on information cap- turing in reality applications. Therefore, online method of motion planning is necessary for such UA- Vs. Gyroscopic force （GF） is used for obstacle avoidance as an online method. However, classical GF has shortcoming in generating orbit for UAV with high velocity because the GF results in a time- varying turning radius. Modified gyroscopic force （MGF） given by function of velocity can overcome this shortcoming and help get a more practical control law for avoidance. MGF can also be used to implement the guidance of UAV by designing particular active conditions. Interactions in forms of stress function and damping force are introduced so that an UAV group can have coordinated motion. By combining controls of MGF and interactions, motion planning of UAV group in obstacle environ- ment can be implemented.
文摘针对故障条件下的多无人机分组编队控制问题,提出一种基于同步分布式模型预测控制(distributed model predictive control,DMPC)的分组编队控制算法。首先,建立考虑虚拟领导者的分组分层控制框架。接着,对编队中的不可恢复故障进行建模,并提出交互健康判别机制及“故障隔离”策略,结合分组分层控制框架提出故障条件下分组编队控制方案。然后,将故障模型与同步DMPC理论相结合,根据“故障隔离”策略,设计故障条件下的单组编队控制算法,并进一步根据控制方案给出分组编队控制算法,且基于Lyapunov理论证明控制算法下编队系统的稳定性。最后,通过仿真验证所设计算法在故障条件下的有效性和优越性。
文摘针对无人机群组在军事对抗复杂环境中,网络拓扑结构动态变化,提出了一种面向无人机群组的轻量动态密钥管理方案,旨在解决无人机加入及退出、批量加入及退出、群组合并及分裂等网络拓扑动态变化导致密钥更新问题,同时在网络拓扑没有变化的情况下进行本地周期性更新,提高无人机群组密钥更新效率。将参与构造的秘密信息分为用户空间、剩余空间和撤销空间,用户空间可以在接收密钥组管理器(key group manager, KGM)的广播消息后计算恢复出会话组密钥,而剩余空间和撤销空间无法计算恢复出组密钥。密钥更新过程中,KGM利用空闲时间提前在密钥池中选取密钥进行预计算处理,降低KGM因构造广播消息进行复杂计算导致的时延问题。分析和实验仿真表明,该方案具有前向和后向安全性、抗共谋攻击和节点撤销能力,与现有无人机密钥管理方案相比,该方案优化了计算和通信开销,且节点存储开销较小,适用于动态拓扑变化的无人机群组网络。
文摘研究了异构多无人机系统的任务分配问题,将任务根据类型分组,以总收益最大为目标函数,考虑无人机能力、任务分组等约束条件建立了MUAP-GT(multiUAV task assignment problem for grouped tasks)模型.在对MUAP-GT进行对偶分解的基础上,首先设计了带共享存储中心的分布式拍卖算法,通过无人机个体目标的最大化实现了整体目标最大化.进一步地结合最大一致性算法将共享存储中心移除,使算法变为完全分布式的算法.最后通过仿真实验表明了算法的有效性与收敛性.
