Unmanned aerial vehicles(UAVs)have become one of the key technologies to achieve future data collection due to their high mobility,rapid deployment,low cost,and the ability to establish line-of-sight communication lin...Unmanned aerial vehicles(UAVs)have become one of the key technologies to achieve future data collection due to their high mobility,rapid deployment,low cost,and the ability to establish line-of-sight communication links.However,when UAV swarm perform tasks in narrow spaces,they often encounter various spatial obstacles,building shielding materials,and high-speed node movements,which result in intermittent network communication links and cannot support the smooth comple-tion of tasks.In this paper,a high mobility and dynamic topol-ogy of the UAV swarm is particularly considered and the high dynamic mobile topology-based clustering(HDMTC)algorithm is proposed.Simulation and real flight verification results verify that the proposed HDMTC algorithm achieves higher stability of net-work,longer link expiration time(LET),and longer node lifetime,all of which improve the communication performance for UAV swarm networks.展开更多
To fulfill the training requirements for the daily operations of multirotor unmanned aerial vehicles(UAVs)clusters,a UAV cluster collaborative task integrated simulation platform(UAV-TISP)was developed.The platform in...To fulfill the training requirements for the daily operations of multirotor unmanned aerial vehicles(UAVs)clusters,a UAV cluster collaborative task integrated simulation platform(UAV-TISP)was developed.The platform integrates a suite of hardware and software to simulate a range of collaborative UAV cluster operation scenarios.It features modules for collaborative task planning,UAV cluster simulations,and tactical monitoring.The platform significantly reduces training costs by eliminating physical drone dependencies while offering a flexible environment for testing swarm algorithms.UAV-TISP supports both individual UAV and swarm operations,incorporating high-fidelity flight dynamics,real-time communication via user datagram protocol(UDP),and collision avoidance strategies.Utilizing the OSGEarth engine,it enables dynamic 3D environment visualization and scenario customization.Three key task scenarios-route flight,formation reconstruction,and formation transformation-were tested to validate the platform’s efficacy.Results demonstrated robust formation maintenance,adaptive collision avoidance,and seamless task execution.Comparative analysis with Gazebo Sim revealed lower trajectory deviations in UAV-TISP,highlighting its superior accuracy in simulating real-world flight dynamics.Future work will focus on enhancing scalability for diverse UAV models,optimizing swarm networking under communication constraints,and expanding mission scenarios.UAV-TISP serves as a versatile tool for both operational training and advanced algorithm development in UAV cluster applications.展开更多
Cooperative unmanned aerial vehicles(UAVs)cluster technology is considered a prospective solution for area coverage problems,enabling network access and emergency communications in remote areas.In this paper,we invest...Cooperative unmanned aerial vehicles(UAVs)cluster technology is considered a prospective solution for area coverage problems,enabling network access and emergency communications in remote areas.In this paper,we investigate how to control UAV cluster to achieve long-term and stable regional coverage while maintaining link connectivity and minimizing energy consumption,given the limited communication range and energy consumption of the UAVs themselves.To this end,we propose a cooperative UAV cluster strategy based on multi-agent deep reinforcement learning(MADRL)to achieve fair coverage of communication regions,which we call MADRL-based cooperative UAV cluster strategy(MADRL-CUCS).Our solution is a centralized training distributed execution architecture and defines a cluster structure for leader UAVs and follower UAVs.Under the premise of comprehensively considering the maximum coverage,we use a new energy efficiency function to minimize energy consumption,so as to extend the network lifetime of the UAVs cluster networks.The new fairness index and collision avoidance factor are used to ensure that the UAV cluster achieve effective and secure regional coverage.We adopt depth first search algorithm to check the link connectivity of the UAVs during the coverage process.Experiments show that the MADRL-CUCS algorithm outperforms the benchmark algorithm.展开更多
Purpose-With the development of wireless networks and artificial intelligence technology,unmanned aerial vehicle(UAV)clusters are widely used in various fields and cluster intelligence attacks are more harmful.However...Purpose-With the development of wireless networks and artificial intelligence technology,unmanned aerial vehicle(UAV)clusters are widely used in various fields and cluster intelligence attacks are more harmful.However,most methods defending against UAV clusters produce consumption of non-reusable resources.To address this problem,a tethered UAV is adopted to perform active defense against adversary UAV clusters in this article,which can reduce the consumption of nonreusable resources.Design/methodology/approach-Using tethered UAV to enter the opponent’s UAV cluster and analyze the flow of packets in adversary UAV cluster to find and occupy the central node.The tethered UAV can acquire and analyze key packets by deploying a grayhole attack at the location of the central node,after which the packets are selectively tampered with and discarded to cripple the opposing UAV cluster.Findings-Comparing packet loss rate and delay with a normal network and the network that suffered from grayhole attack,it can be seen that the proposed scheme makes the tethered UAV close to the normal nodes in the UAV cluster and difficult to be detected.In addition,the tethered UAV is able to capture more packets compared to the other two networks,and the average deviation of the tethered UAV in capturing packets is around 5%in repeated experiments.Originality/value-This article proposes an active defense method assisted by tethered UAV,which can minimize the consumption of nonreusable resources.The tethered UAV is converged to ordinary nodes of the opponent’s cluster,in which it is not easily detected.It provides a new direction for point-air defense technology.展开更多
The combat tasks faced by UAVs are becoming more and more complex.Traditional single UAVs are limited by the constraints of platform load capacity,lightweight load,and insufficient lowpower equipment,so it is difficul...The combat tasks faced by UAVs are becoming more and more complex.Traditional single UAVs are limited by the constraints of platform load capacity,lightweight load,and insufficient lowpower equipment,so it is difficult to complete complex tasks independently.Aiming at typical UAV collaborative confrontation application scenarios,this paper constructs a multi-agentoriented cluster collaborative intelligent system architecture and establishes a swarm-oriented intelligent UAV cluster collaborative control algorithm.Moreover,this paper forms a simulation environment for UAV cluster collaborative confrontation and completes the design and implementation of the UAV cluster collaborative confrontation system based on swarm intelligence.In addition,this paper analyses the key technologies of the UAV cluster collaborative system with the support of the Internet of Things technology and verifies the performance of the system after constructing the corresponding system.The experimental results show that the system constructed in this paper is effective.展开更多
This paper proposes a distributed task allocation algorithm based on game theory to solve the complex task allocation optimization problem when UAV clusters carry heterogeneous resources and tasks have heterogeneous d...This paper proposes a distributed task allocation algorithm based on game theory to solve the complex task allocation optimization problem when UAV clusters carry heterogeneous resources and tasks have heterogeneous demands. Considering the heterogeneity of resources,two pre-processing methods are proposed: one is the grouping algorithm that combines greedy algorithm with simulated annealing algorithm, and the other is the improved K-medoids clustering algorithm based on heterogeneous resources. These pre-process methods, through grouping and clustering, can reduce the complexity of task allocation. The entropy weight method is utilized to prioritize tasks based on multiple metrics. Considering task demands,airborne resources and path cost, a coalition formation game model is established, which is proved to be a potential game. Then a distributed task allocation algorithm based on coalition formation game is designed to address the task allocation problem. Finally, the simulation involving 30 tasks with heterogeneous requirements assigned to 100 UAVs validates the effectiveness of the proposed algorithm, showing that it can achieve good task allocation results with great real-time performance.展开更多
基金supported by the National Key Research and Development Program of China(2024YFB4504500)Shanghai Collaborative Innovation Project(24xtcx00500).
文摘Unmanned aerial vehicles(UAVs)have become one of the key technologies to achieve future data collection due to their high mobility,rapid deployment,low cost,and the ability to establish line-of-sight communication links.However,when UAV swarm perform tasks in narrow spaces,they often encounter various spatial obstacles,building shielding materials,and high-speed node movements,which result in intermittent network communication links and cannot support the smooth comple-tion of tasks.In this paper,a high mobility and dynamic topol-ogy of the UAV swarm is particularly considered and the high dynamic mobile topology-based clustering(HDMTC)algorithm is proposed.Simulation and real flight verification results verify that the proposed HDMTC algorithm achieves higher stability of net-work,longer link expiration time(LET),and longer node lifetime,all of which improve the communication performance for UAV swarm networks.
文摘To fulfill the training requirements for the daily operations of multirotor unmanned aerial vehicles(UAVs)clusters,a UAV cluster collaborative task integrated simulation platform(UAV-TISP)was developed.The platform integrates a suite of hardware and software to simulate a range of collaborative UAV cluster operation scenarios.It features modules for collaborative task planning,UAV cluster simulations,and tactical monitoring.The platform significantly reduces training costs by eliminating physical drone dependencies while offering a flexible environment for testing swarm algorithms.UAV-TISP supports both individual UAV and swarm operations,incorporating high-fidelity flight dynamics,real-time communication via user datagram protocol(UDP),and collision avoidance strategies.Utilizing the OSGEarth engine,it enables dynamic 3D environment visualization and scenario customization.Three key task scenarios-route flight,formation reconstruction,and formation transformation-were tested to validate the platform’s efficacy.Results demonstrated robust formation maintenance,adaptive collision avoidance,and seamless task execution.Comparative analysis with Gazebo Sim revealed lower trajectory deviations in UAV-TISP,highlighting its superior accuracy in simulating real-world flight dynamics.Future work will focus on enhancing scalability for diverse UAV models,optimizing swarm networking under communication constraints,and expanding mission scenarios.UAV-TISP serves as a versatile tool for both operational training and advanced algorithm development in UAV cluster applications.
基金supported by the National Natural Science Foundation of China(No.62376165).
文摘Cooperative unmanned aerial vehicles(UAVs)cluster technology is considered a prospective solution for area coverage problems,enabling network access and emergency communications in remote areas.In this paper,we investigate how to control UAV cluster to achieve long-term and stable regional coverage while maintaining link connectivity and minimizing energy consumption,given the limited communication range and energy consumption of the UAVs themselves.To this end,we propose a cooperative UAV cluster strategy based on multi-agent deep reinforcement learning(MADRL)to achieve fair coverage of communication regions,which we call MADRL-based cooperative UAV cluster strategy(MADRL-CUCS).Our solution is a centralized training distributed execution architecture and defines a cluster structure for leader UAVs and follower UAVs.Under the premise of comprehensively considering the maximum coverage,we use a new energy efficiency function to minimize energy consumption,so as to extend the network lifetime of the UAVs cluster networks.The new fairness index and collision avoidance factor are used to ensure that the UAV cluster achieve effective and secure regional coverage.We adopt depth first search algorithm to check the link connectivity of the UAVs during the coverage process.Experiments show that the MADRL-CUCS algorithm outperforms the benchmark algorithm.
基金funded by Basic Research Project of the National Defence Science and Industry Bureau(Project No.JCKY2022405C010)the Translational Application Project of the“Wise Eyes Action”(Project No.F2B6A194)Beijing Information Science and Technology University Education Reform(Project No.2024JGYB35).
文摘Purpose-With the development of wireless networks and artificial intelligence technology,unmanned aerial vehicle(UAV)clusters are widely used in various fields and cluster intelligence attacks are more harmful.However,most methods defending against UAV clusters produce consumption of non-reusable resources.To address this problem,a tethered UAV is adopted to perform active defense against adversary UAV clusters in this article,which can reduce the consumption of nonreusable resources.Design/methodology/approach-Using tethered UAV to enter the opponent’s UAV cluster and analyze the flow of packets in adversary UAV cluster to find and occupy the central node.The tethered UAV can acquire and analyze key packets by deploying a grayhole attack at the location of the central node,after which the packets are selectively tampered with and discarded to cripple the opposing UAV cluster.Findings-Comparing packet loss rate and delay with a normal network and the network that suffered from grayhole attack,it can be seen that the proposed scheme makes the tethered UAV close to the normal nodes in the UAV cluster and difficult to be detected.In addition,the tethered UAV is able to capture more packets compared to the other two networks,and the average deviation of the tethered UAV in capturing packets is around 5%in repeated experiments.Originality/value-This article proposes an active defense method assisted by tethered UAV,which can minimize the consumption of nonreusable resources.The tethered UAV is converged to ordinary nodes of the opponent’s cluster,in which it is not easily detected.It provides a new direction for point-air defense technology.
基金the project of“13th five year plan”of educational science of Shaanxi Province in 2020.Project Name:Practical research on the cultivation of innovative talents of new engineering based on PBL mode.Project number:SGH20Y1420the project of Natural science research program of Shanxi Province.Project Name:Re-search on Key Technologies of UAV adaptive scene matching visual navigation system.Project Name:2020JM-637.
文摘The combat tasks faced by UAVs are becoming more and more complex.Traditional single UAVs are limited by the constraints of platform load capacity,lightweight load,and insufficient lowpower equipment,so it is difficult to complete complex tasks independently.Aiming at typical UAV collaborative confrontation application scenarios,this paper constructs a multi-agentoriented cluster collaborative intelligent system architecture and establishes a swarm-oriented intelligent UAV cluster collaborative control algorithm.Moreover,this paper forms a simulation environment for UAV cluster collaborative confrontation and completes the design and implementation of the UAV cluster collaborative confrontation system based on swarm intelligence.In addition,this paper analyses the key technologies of the UAV cluster collaborative system with the support of the Internet of Things technology and verifies the performance of the system after constructing the corresponding system.The experimental results show that the system constructed in this paper is effective.
基金supported by the National Natural Science Foundation of China (Grant Nos. 62273177, 62020106003 and 62233009)Natural Science Foundation of Jiangsu Province of China (Grant Nos. BK20211566 and 20222012)+2 种基金Programme of Introducing Talents of Discipline to Universities of China (Grant No. B20007)National Key Laboratory of Space Intelligent Control Technology Open Fund (Grant No. HTKJ2023KL502006)Fundamental Research Funds for the Central Universities (Grant No. NI2024001)
文摘This paper proposes a distributed task allocation algorithm based on game theory to solve the complex task allocation optimization problem when UAV clusters carry heterogeneous resources and tasks have heterogeneous demands. Considering the heterogeneity of resources,two pre-processing methods are proposed: one is the grouping algorithm that combines greedy algorithm with simulated annealing algorithm, and the other is the improved K-medoids clustering algorithm based on heterogeneous resources. These pre-process methods, through grouping and clustering, can reduce the complexity of task allocation. The entropy weight method is utilized to prioritize tasks based on multiple metrics. Considering task demands,airborne resources and path cost, a coalition formation game model is established, which is proved to be a potential game. Then a distributed task allocation algorithm based on coalition formation game is designed to address the task allocation problem. Finally, the simulation involving 30 tasks with heterogeneous requirements assigned to 100 UAVs validates the effectiveness of the proposed algorithm, showing that it can achieve good task allocation results with great real-time performance.
