Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion...Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.展开更多
In response to the challenges faced by unmanned swarms in mountain obstacle-breaching missions within complex terrains,such as poor task-resource coupling,lengthy solution generation times,and poor inter-platform coll...In response to the challenges faced by unmanned swarms in mountain obstacle-breaching missions within complex terrains,such as poor task-resource coupling,lengthy solution generation times,and poor inter-platform collaboration,an unmanned swarm scheduling strategy tailored is proposed for mountain obstacle-breaching missions.Initially,by formalizing the descriptions of obstacle breaching operations,the swarm,and obstacle targets,an optimization model is constructed with the objectives of expected global benefit,timeliness,and task completion degree.A meta-task decomposition and reassembly strategy is then introduced to more precisely match the capabilities of unmanned platforms with task requirements.Additionally,a meta-task decomposition optimization model and a meta-task allocation operator are incorporated to achieve efficient allocation of swarm resources and collaborative scheduling.Simulation results demonstrate that the model can accurately generate reasonable and feasible obstacle breaching execution plans for unmanned swarms based on specific task requirements and environmental conditions.Moreover,compared to conventional strategies,the proposed strategy enhances task completion degree and expected returns while reducing the execution time of the plans.展开更多
Cascading failures pose a serious threat to the survivability of underwater unmanned swarm networks(UUSNs),significantly limiting their service ability in collaborative missions such as military reconnaissance and env...Cascading failures pose a serious threat to the survivability of underwater unmanned swarm networks(UUSNs),significantly limiting their service ability in collaborative missions such as military reconnaissance and environmental monitoring.Existing failure models primarily focus on power grids and traffic systems,and don't address the unique challenges of weak-communication UUSNs.In UUSNs,cascading failure present a complex and dynamic process driven by the coupling of unstable acoustic channels,passive node drift,adversarial attacks,and network heterogeneity.To address these challenges,a directed weighted graph model of UUSNs is first developed,in which node positions are updated according to ocean-current-driven drift and link weights reflect the probability of successful acoustic transmission.Building on this UUSNs graph model,a cascading failure model is proposed that integrates a normal-failure-recovery state-cycle mechanism,multiple attack strategies,and routingbased load redistribution.Finally,under a five-level connectivity UUSNs scheme,simulations are conducted to analyze how dynamic topology,network load,node recovery delay,and attack modes jointly affect network survivability.The main findings are:(1)moderate node drift can improve survivability by activating weak links;(2)based-energy routing(BER)outperform based-depth routing(BDR)in harsh conditions;(3)node self-recovery time is critical to network survivability;(4)traditional degree-based critical node metrics are inadequate for weak-communication UUSNs.These results provide a theoretical foundation for designing robust survivability mechanisms in weak-communication UUSNs.展开更多
For mission-oriented unmanned aerial vehicle(UAV)swarms,mission capability assessment provides an important reference in the design and development process,and is a precondition for mission success.For this multi-crit...For mission-oriented unmanned aerial vehicle(UAV)swarms,mission capability assessment provides an important reference in the design and development process,and is a precondition for mission success.For this multi-criteria decisionmaking(MCDM)problem,the current literature lacks a way to unambiguously present criteria and the popular fuzzy analytic network process(ANP)approaches neglect the hesitancy of subjective judgments.To fill these research gaps,an MCDM method based on unified architecture framework(UAF)and interval-valued spherical fuzzy ANP(IVSF-ANP)is proposed in this paper.Firstly,selected viewpoints in UAF are extended to construct criteria models with standardized representation.Secondly,interval-valued spherical fuzzy sets are introduced to ANP to weight interdependent criteria,handling fuzziness and hesitancy in pairwise comparisons.A method of adjusting weights of experts based on their decision similarities is also included in this process to reduce ambiguity brought by multiple experts.Next,performance characteristics are non-linearly transformed regarding to expectations to get final results.This proposition is applied to assess the mission capability of UAV swarms to search and strike surface vessels.Comparative analysis shows that the proposed method is valid and reasonable.展开更多
The exploration of unmanned aerial vehicle(UAV)swarm systems represents a focal point in the research of multiagent systems,with the investigation of their fission-fusion behavior holding significant theoretical and p...The exploration of unmanned aerial vehicle(UAV)swarm systems represents a focal point in the research of multiagent systems,with the investigation of their fission-fusion behavior holding significant theoretical and practical value.This review systematically examines the methods for fission-fusion of UAV swarms from the perspective of multi-agent systems,encompassing the composition of UAV swarm systems and fission-fusion conditions,information interaction mechanisms,and existing fission-fusion approaches.Firstly,considering the constituent units of UAV swarms and the conditions influencing fission-fusion,this paper categorizes and introduces the UAV swarm systems.It further examines the effects and limitations of fission-fusion methods across various categories and conditions.Secondly,a comprehensive analysis of the prevalent information interaction mechanisms within UAV swarms is conducted from the perspective of information interaction structures.The advantages and limitations of various mechanisms in the context of fission-fusion behaviors are summarized and synthesized.Thirdly,this paper consolidates the existing implementation research findings related to the fission-fusion behavior of UAV swarms,identifies unresolved issues in fission-fusion research,and discusses potential solutions.Finally,the paper concludes with a comprehensive summary and systematically outlines future research opportunities.展开更多
A task allocation problem for the heterogeneous unmanned aerial vehicle (UAV) swarm in unknown environments is studied in this paper.Considering that the actual mission environment information may be unknown,the UAV s...A task allocation problem for the heterogeneous unmanned aerial vehicle (UAV) swarm in unknown environments is studied in this paper.Considering that the actual mission environment information may be unknown,the UAV swarm needs to detect the environment first and then attack the detected targets.The heterogeneity of UAVs,multiple types of tasks,and the dynamic nature of task environment lead to uneven load and time sequence problems.This paper proposes an improved contract net protocol (CNP) based task allocation scheme,which effectively balances the load of UAVs and improves the task efficiency.Firstly,two types of task models are established,including regional reconnaissance tasks and target attack tasks.Secondly,for regional reconnaissance tasks,an improved CNP algorithm using the uncertain contract is developed.Through uncertain contracts,the area size of the regional reconnaissance task is determined adaptively after this task assignment,which can improve reconnaissance efficiency and resource utilization.Thirdly,for target attack tasks,an improved CNP algorithm using the fuzzy integrated evaluation and the double-layer negotiation is presented to enhance collaborative attack efficiency through adjusting the assignment sequence adaptively and multi-layer allocation.Finally,the effectiveness and advantages of the improved method are verified through comparison simulations.展开更多
Microseismic (MS) source location plays an important role in MS monitoring. This paper proposes a MS source location method based on particle swarm optimization (PSO) and multi-sensor arrays, where a free weight joint...Microseismic (MS) source location plays an important role in MS monitoring. This paper proposes a MS source location method based on particle swarm optimization (PSO) and multi-sensor arrays, where a free weight joints the P-wave first arrival data. This method adaptively adjusts the preference for “superior” arrays and leverages “inferior” arrays to escape local optima, thereby improving the location accuracy. The effectiveness and stability of this method were validated through synthetic tests, pencil-lead break (PLB) experiments, and mining engineering applications. Specifically, for synthetic tests with 1 μs Gaussian noise and 100 μs large noise in rock samples, the location error of the multi-sensor arrays jointed location method is only 0.30 cm, which improves location accuracy by 97.51% compared to that using a single sensor array. The average location error of PLB events on three surfaces of a rock sample is reduced by 48.95%, 26.40%, and 55.84%, respectively. For mine blast event tests, the average location error of the dual sensor arrays jointed method is 62.74 m, 54.32% and 14.29% lower than that using only sensor arrays 1 and 2, respectively. In summary, the proposed multi-sensor arrays jointed location method demonstrates good noise resistance, stability, and accuracy, providing a compelling new solution for MS location in relevant mining scenarios.展开更多
Task allocation for munition swarms is constrained by reachable region limitations and real-time requirements.This paper proposes a reachable region guided distributed coalition formation game(RRGDCF)method to address...Task allocation for munition swarms is constrained by reachable region limitations and real-time requirements.This paper proposes a reachable region guided distributed coalition formation game(RRGDCF)method to address these issues.To enable efficient online task allocation,a reachable region prediction strategy based on fully connected neural networks(FCNNs)is developed.This strategy integrates high-fidelity data generated from the golden section method and low-fidelity data from geometric approximation in an optimal mixing ratio to form multi-fidelity samples,significantly enhancing prediction accuracy and efficiency under limited high-fidelity samples.These predictions are then incorporated into the coalition formation game framework.A tabu search mechanism guided by the reachable region center directs munitions to execute tasks within their respective reachable regions,mitigating redundant operations on ineffective coalition structures.Furthermore,an adaptive guidance coalition formation strategy optimizes allocation plans by leveraging the hit probabilities of munitions,replacing traditional random coalition formation methods.Simulation results demonstrate that RRGDCF surpasses the contract network protocol and traditional coalition formation game algorithms in optimality and computational efficiency.Hardware experiments further validate the method's practicality in dynamic scenarios.展开更多
This paper proposes a distributed event-triggered control(ETC)framework to address cooperative target fencing challenges in UAV swarm.The proposed architecture eliminates the reliance on preset formation parameters wh...This paper proposes a distributed event-triggered control(ETC)framework to address cooperative target fencing challenges in UAV swarm.The proposed architecture eliminates the reliance on preset formation parameters while achieving multi-objective cooperative control for target fencing,network connectivity preservation,collision avoidance,and communication efficiency optimization.Firstly,a differential state observer is constructed to obtain the target's unmeasurable states.Secondly,leveraging swarm selforganization principles,a geometric-constraint-free distributed fencing controller is designed by integrating potential field methods with consensus theory.The controller dynamically adjusts inter-UAV distances via single potential function,enabling coordinated optimization of persistent network connectivity and collision-free motion during target fencing.Thirdly,a dual-threshold ETC mechanism based on velocity consensus deviation and fencing error is proposed,which can be triggered based on task features to dynamically adjust the communication frequency,significantly reduce the communication burden and exclude Zeno behavior.Theoretical analysis demonstrates the stability of closed-loop systems.Multi-scenario simulations show that the proposed method can achieve robust fencing under target maneuverability,partial UAV failures,and communication disturbances.展开更多
This article started with an overview of the current technological status and engineering developments in the field of swarm munitions.It first introduced swarm behaviors and related swarm algorithms,and then provided...This article started with an overview of the current technological status and engineering developments in the field of swarm munitions.It first introduced swarm behaviors and related swarm algorithms,and then provided a comprehensive summary of the research progress in the field of swarm munitions from four aspects:Collaborative perception and detection,collaborative positioning and navigation,task allocation for swarms,and path planning for swarms.In summary,future developments in collaborative perception,planning,positioning,navigation,and decision-making for swarm munitions will trend towards intelligence,adaptability,and collaboration.It can enable swarm munitions to be better adapted to complex and dynamic battlefields,improving operational effectiveness and mission capabilities.展开更多
Low collateral damage weapons achieve controlled personnel injury through the coupling of shock waves and particle swarms,where the particle swarms arise from the high-explosive dispersion of compacted metal particle ...Low collateral damage weapons achieve controlled personnel injury through the coupling of shock waves and particle swarms,where the particle swarms arise from the high-explosive dispersion of compacted metal particle ring.To investigate the dynamic response of the human target under combined shock waves and particle swarms loading,a physical human surrogate torso model(HSTM)was developed,and the dynamic response test experiment was conducted under the combined loading.The effects of particle size on the loading parameters,the damage patterns of the ballistic plate and HSTM,and the dynamic response parameters of the HSTM with and without protection are mainly analyzed.Our findings revealed that particle swarms can effectively delay the shock wave attenuation,especially the best effect when the particle size was 0.28–0.45 mm.The ballistic plate mainly exhibited dense perforation of the outer fabric and impacted crater damage of ceramic plates,whereas the unprotected HSTM was mainly dominated by high-density and small-size ballistic cavity group damage.The peak values of the dynamic response parameters for the HSTM under combined loading were significantly larger than those under bare charge loading,with multiple peaks observed.Under unprotected conditions,the peak acceleration of skeletons and peak pressure of organs increased with the particle size.Under protected conditions,the particle size,the number of particles hit,and the fit of the ballistic plate to the HSTM together affected the dynamic response parameters of the HSTM.展开更多
Developing intelligent unmanned swarm systems(IUSSs)is a highly intricate process.Although current simulators and toolchains have made a notable contribution to the develop-ment of algorithms for IUSSs,they tend to co...Developing intelligent unmanned swarm systems(IUSSs)is a highly intricate process.Although current simulators and toolchains have made a notable contribution to the develop-ment of algorithms for IUSSs,they tend to concentrate on iso-lated technical elements and are deficient in addressing the full spectrum of critical technologies and development needs in a systematic and integrative manner.Furthermore,the current suite of tools has not adequately addressed the challenge of bridging the gap between simulation and real-world deployment of algorithms.Therefore,a comprehensive solution must be developed that encompasses the entire IUSS development life-cycle.In this study,we present the RflySim ToolChain,which has been developed with the specific aim of facilitating the rapid development and validation of IUSSs.The RflySim ToolChain employs a model-based design(MBD)approach,integrating a modeling and simulation module,a lower reliable control mo-dule,and an upper swarm decision-making module.This compre-hensive integration encompasses the entire process,from mo-deling and simulation to testing and deployment,thereby enabling users to rapidly construct and validate IUSSs.The prin-cipal advantages of the RflySim ToolChain are as follows:it pro-vides a comprehensive solution that meets the full-stack devel-opment needs of IUSSs;the highly modular architecture and comprehensive software development kit(SDK)facilitate the automation of the entire IUSS development process.Further-more,the high-fidelity model design and reliable architecture solution ensure a seamless transition from simulation to real-world deployment,which is known as the simulation to reality(Sim2Real)process.This paper presents a series of case stu-dies that illustrate the effectiveness of the RflySim ToolChain in supporting the research and application of IUSSs.展开更多
In the realm of missile defense systems,the self-sufficient maneuver capacity of missile swarms is pivotal for their survival.Through the analysis of the missile dynamics model,a time-efficient cooperative attack stra...In the realm of missile defense systems,the self-sufficient maneuver capacity of missile swarms is pivotal for their survival.Through the analysis of the missile dynamics model,a time-efficient cooperative attack strategy for missile swarm is proposed.Based on the distribution of the attackers and defenders,the collision avoidance against the defenders is considered during the attack process.By analyzing the geometric relationship between the relative velocity vector and relative position vector of the attackers and defenders,the collision avoidance constrains of attacking swarm are redefined.The key point is on adjusting the relative velocity vectors to fall outside the collision cone.This work facilitates high-precision attack toward the target while keeping safe missing distance between other attackers during collision avoidance process.By leveraging an innovative repulsion artificial function,a time-efficient cooperative attack strategy for missile swarm is obtained.Through rigorous simulation,the effectiveness of this cooperative attack strategy is substantiated.Furthermore,by employing Monte Carlo simulation,the success rate of the cooperative attack strategy is assessesed and the optimal configuration for the missile swarm is deduced.展开更多
Drone swarm systems,equipped with photoelectric imaging and intelligent target perception,are essential for reconnaissance and strike missions in complex and high-risk environments.They excel in information sharing,an...Drone swarm systems,equipped with photoelectric imaging and intelligent target perception,are essential for reconnaissance and strike missions in complex and high-risk environments.They excel in information sharing,anti-jamming capabilities,and combat performance,making them critical for future warfare.However,varied perspectives in collaborative combat scenarios pose challenges to object detection,hindering traditional detection algorithms and reducing accuracy.Limited angle-prior data and sparse samples further complicate detection.This paper presents the Multi-View Collaborative Detection System,which tackles the challenges of multi-view object detection in collaborative combat scenarios.The system is designed to enhance multi-view image generation and detection algorithms,thereby improving the accuracy and efficiency of object detection across varying perspectives.First,an observation model for three-dimensional targets through line-of-sight angle transformation is constructed,and a multi-view image generation algorithm based on the Pix2Pix network is designed.For object detection,YOLOX is utilized,and a deep feature extraction network,BA-RepCSPDarknet,is developed to address challenges related to small target scale and feature extraction challenges.Additionally,a feature fusion network NS-PAFPN is developed to mitigate the issue of deep feature map information loss in UAV images.A visual attention module(BAM)is employed to manage appearance differences under varying angles,while a feature mapping module(DFM)prevents fine-grained feature loss.These advancements lead to the development of BA-YOLOX,a multi-view object detection network model suitable for drone platforms,enhancing accuracy and effectively targeting small objects.展开更多
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
Aiming at the characteristics of autonomy,confrontation,and uncertainty in unmanned aerial vehicle(UAV)swarm operations,case-based reasoning(CBR)technology with advantages such as weak dependence on domain knowledge a...Aiming at the characteristics of autonomy,confrontation,and uncertainty in unmanned aerial vehicle(UAV)swarm operations,case-based reasoning(CBR)technology with advantages such as weak dependence on domain knowledge and efficient problem-solving is introduced,and a recommendation method for UAV swarm operation strategies based on CBR is proposed.Firstly,we design a universal framework for UAV swarm operation strategies from three dimensions:operation effectiveness,time,and cost.Secondly,based on the representation of operation cases,certain,fuzzy,interval,and classification attribute similarity calculation methods,as well as entropybased attribute weight allocation methods,are suggested to support the calculation of global similarity of cases.This method is utilized to match the source case with the most similarity from the historical case library,to obtain the optimal recommendation strategy for the target case.Finally,in the form of red blue confrontation,a UAV swarm operation strategy recommendation case is constructed based on actual battle cases,and a system simulation analysis is conducted.The results show that the strategy given in the example performs the best in three evaluation indicators,including cost-effectiveness,and overall outperforms other operation strategies.Therefore,the proposed method has advantages such as high real-time performance and interpretability,and can address the issue of recommending UAV swarm operation strategies in complex battlefield environments across both online and offline modes.At the same time,this study could also provide new ideas for the selection of UAV swarm operation strategies.展开更多
Compared with single-domain unmanned swarms,cross-domain unmanned swarms continue to face new challenges in terms of platform performance and constraints.In this paper,a joint unmanned swarm target assignment and miss...Compared with single-domain unmanned swarms,cross-domain unmanned swarms continue to face new challenges in terms of platform performance and constraints.In this paper,a joint unmanned swarm target assignment and mission trajectory planning method is proposed to meet the requirements of cross-domain unmanned swarm mission planning.Firstly,the different performances of cross-domain heterogeneous platforms and mission requirements of targets are characterised by using a collection of operational resources.Secondly,an algorithmic framework for joint target assignment and mission trajectory planning is proposed,in which the initial planning of the trajectory is performed in the target assignment phase,while the trajectory is further optimised afterwards.Next,the estimation of the distribution algorithms is combined with the genetic algorithm to solve the objective function.Finally,the algorithm is numerically simulated by specific cases.Simulation results indicate that the proposed algorithm can perform effective task assignment and trajectory planning for cross-domain unmanned swarms.Furthermore,the solution performance of the hybrid estimation of distribution algorithm(EDA)-genetic algorithm(GA)algorithm is better than that of GA and EDA.展开更多
文摘Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.
基金supported by the National Natural Science Foundation of China(61374186)。
文摘In response to the challenges faced by unmanned swarms in mountain obstacle-breaching missions within complex terrains,such as poor task-resource coupling,lengthy solution generation times,and poor inter-platform collaboration,an unmanned swarm scheduling strategy tailored is proposed for mountain obstacle-breaching missions.Initially,by formalizing the descriptions of obstacle breaching operations,the swarm,and obstacle targets,an optimization model is constructed with the objectives of expected global benefit,timeliness,and task completion degree.A meta-task decomposition and reassembly strategy is then introduced to more precisely match the capabilities of unmanned platforms with task requirements.Additionally,a meta-task decomposition optimization model and a meta-task allocation operator are incorporated to achieve efficient allocation of swarm resources and collaborative scheduling.Simulation results demonstrate that the model can accurately generate reasonable and feasible obstacle breaching execution plans for unmanned swarms based on specific task requirements and environmental conditions.Moreover,compared to conventional strategies,the proposed strategy enhances task completion degree and expected returns while reducing the execution time of the plans.
基金supported in part by the National Natural Science Foundation of China(Key Program)under Grant No.62031021。
文摘Cascading failures pose a serious threat to the survivability of underwater unmanned swarm networks(UUSNs),significantly limiting their service ability in collaborative missions such as military reconnaissance and environmental monitoring.Existing failure models primarily focus on power grids and traffic systems,and don't address the unique challenges of weak-communication UUSNs.In UUSNs,cascading failure present a complex and dynamic process driven by the coupling of unstable acoustic channels,passive node drift,adversarial attacks,and network heterogeneity.To address these challenges,a directed weighted graph model of UUSNs is first developed,in which node positions are updated according to ocean-current-driven drift and link weights reflect the probability of successful acoustic transmission.Building on this UUSNs graph model,a cascading failure model is proposed that integrates a normal-failure-recovery state-cycle mechanism,multiple attack strategies,and routingbased load redistribution.Finally,under a five-level connectivity UUSNs scheme,simulations are conducted to analyze how dynamic topology,network load,node recovery delay,and attack modes jointly affect network survivability.The main findings are:(1)moderate node drift can improve survivability by activating weak links;(2)based-energy routing(BER)outperform based-depth routing(BDR)in harsh conditions;(3)node self-recovery time is critical to network survivability;(4)traditional degree-based critical node metrics are inadequate for weak-communication UUSNs.These results provide a theoretical foundation for designing robust survivability mechanisms in weak-communication UUSNs.
基金supported by the National Natural Science Foundation of China(62073267,61903305)the Fundamental Research Funds for the Central Universities(HXGJXM202214)。
文摘For mission-oriented unmanned aerial vehicle(UAV)swarms,mission capability assessment provides an important reference in the design and development process,and is a precondition for mission success.For this multi-criteria decisionmaking(MCDM)problem,the current literature lacks a way to unambiguously present criteria and the popular fuzzy analytic network process(ANP)approaches neglect the hesitancy of subjective judgments.To fill these research gaps,an MCDM method based on unified architecture framework(UAF)and interval-valued spherical fuzzy ANP(IVSF-ANP)is proposed in this paper.Firstly,selected viewpoints in UAF are extended to construct criteria models with standardized representation.Secondly,interval-valued spherical fuzzy sets are introduced to ANP to weight interdependent criteria,handling fuzziness and hesitancy in pairwise comparisons.A method of adjusting weights of experts based on their decision similarities is also included in this process to reduce ambiguity brought by multiple experts.Next,performance characteristics are non-linearly transformed regarding to expectations to get final results.This proposition is applied to assess the mission capability of UAV swarms to search and strike surface vessels.Comparative analysis shows that the proposed method is valid and reasonable.
基金supported by the National Natural Science Foundation of China(U20B2042).
文摘The exploration of unmanned aerial vehicle(UAV)swarm systems represents a focal point in the research of multiagent systems,with the investigation of their fission-fusion behavior holding significant theoretical and practical value.This review systematically examines the methods for fission-fusion of UAV swarms from the perspective of multi-agent systems,encompassing the composition of UAV swarm systems and fission-fusion conditions,information interaction mechanisms,and existing fission-fusion approaches.Firstly,considering the constituent units of UAV swarms and the conditions influencing fission-fusion,this paper categorizes and introduces the UAV swarm systems.It further examines the effects and limitations of fission-fusion methods across various categories and conditions.Secondly,a comprehensive analysis of the prevalent information interaction mechanisms within UAV swarms is conducted from the perspective of information interaction structures.The advantages and limitations of various mechanisms in the context of fission-fusion behaviors are summarized and synthesized.Thirdly,this paper consolidates the existing implementation research findings related to the fission-fusion behavior of UAV swarms,identifies unresolved issues in fission-fusion research,and discusses potential solutions.Finally,the paper concludes with a comprehensive summary and systematically outlines future research opportunities.
基金National Natural Science Foundation of China (12202293)Sichuan Science and Technology Program (2023NSFSC0393,2022NSFSC1952)。
文摘A task allocation problem for the heterogeneous unmanned aerial vehicle (UAV) swarm in unknown environments is studied in this paper.Considering that the actual mission environment information may be unknown,the UAV swarm needs to detect the environment first and then attack the detected targets.The heterogeneity of UAVs,multiple types of tasks,and the dynamic nature of task environment lead to uneven load and time sequence problems.This paper proposes an improved contract net protocol (CNP) based task allocation scheme,which effectively balances the load of UAVs and improves the task efficiency.Firstly,two types of task models are established,including regional reconnaissance tasks and target attack tasks.Secondly,for regional reconnaissance tasks,an improved CNP algorithm using the uncertain contract is developed.Through uncertain contracts,the area size of the regional reconnaissance task is determined adaptively after this task assignment,which can improve reconnaissance efficiency and resource utilization.Thirdly,for target attack tasks,an improved CNP algorithm using the fuzzy integrated evaluation and the double-layer negotiation is presented to enhance collaborative attack efficiency through adjusting the assignment sequence adaptively and multi-layer allocation.Finally,the effectiveness and advantages of the improved method are verified through comparison simulations.
基金Project(SICGM2023301) supported by the State Key Laboratory of Strata Intelligent Control and Green Mining Co-founded by Shandong Province and the Ministry of Science and Technology,ChinaProject(SMDPC202202) supported by the Key Laboratory of Mining Disaster Prevention and Control,ChinaProject(U21A2030) supported by the National Natural Science Foundation of China。
文摘Microseismic (MS) source location plays an important role in MS monitoring. This paper proposes a MS source location method based on particle swarm optimization (PSO) and multi-sensor arrays, where a free weight joints the P-wave first arrival data. This method adaptively adjusts the preference for “superior” arrays and leverages “inferior” arrays to escape local optima, thereby improving the location accuracy. The effectiveness and stability of this method were validated through synthetic tests, pencil-lead break (PLB) experiments, and mining engineering applications. Specifically, for synthetic tests with 1 μs Gaussian noise and 100 μs large noise in rock samples, the location error of the multi-sensor arrays jointed location method is only 0.30 cm, which improves location accuracy by 97.51% compared to that using a single sensor array. The average location error of PLB events on three surfaces of a rock sample is reduced by 48.95%, 26.40%, and 55.84%, respectively. For mine blast event tests, the average location error of the dual sensor arrays jointed method is 62.74 m, 54.32% and 14.29% lower than that using only sensor arrays 1 and 2, respectively. In summary, the proposed multi-sensor arrays jointed location method demonstrates good noise resistance, stability, and accuracy, providing a compelling new solution for MS location in relevant mining scenarios.
基金supported by the National Natural Science Foundation of China(Grant 52372347,52425211,52272360)。
文摘Task allocation for munition swarms is constrained by reachable region limitations and real-time requirements.This paper proposes a reachable region guided distributed coalition formation game(RRGDCF)method to address these issues.To enable efficient online task allocation,a reachable region prediction strategy based on fully connected neural networks(FCNNs)is developed.This strategy integrates high-fidelity data generated from the golden section method and low-fidelity data from geometric approximation in an optimal mixing ratio to form multi-fidelity samples,significantly enhancing prediction accuracy and efficiency under limited high-fidelity samples.These predictions are then incorporated into the coalition formation game framework.A tabu search mechanism guided by the reachable region center directs munitions to execute tasks within their respective reachable regions,mitigating redundant operations on ineffective coalition structures.Furthermore,an adaptive guidance coalition formation strategy optimizes allocation plans by leveraging the hit probabilities of munitions,replacing traditional random coalition formation methods.Simulation results demonstrate that RRGDCF surpasses the contract network protocol and traditional coalition formation game algorithms in optimality and computational efficiency.Hardware experiments further validate the method's practicality in dynamic scenarios.
文摘This paper proposes a distributed event-triggered control(ETC)framework to address cooperative target fencing challenges in UAV swarm.The proposed architecture eliminates the reliance on preset formation parameters while achieving multi-objective cooperative control for target fencing,network connectivity preservation,collision avoidance,and communication efficiency optimization.Firstly,a differential state observer is constructed to obtain the target's unmeasurable states.Secondly,leveraging swarm selforganization principles,a geometric-constraint-free distributed fencing controller is designed by integrating potential field methods with consensus theory.The controller dynamically adjusts inter-UAV distances via single potential function,enabling coordinated optimization of persistent network connectivity and collision-free motion during target fencing.Thirdly,a dual-threshold ETC mechanism based on velocity consensus deviation and fencing error is proposed,which can be triggered based on task features to dynamically adjust the communication frequency,significantly reduce the communication burden and exclude Zeno behavior.Theoretical analysis demonstrates the stability of closed-loop systems.Multi-scenario simulations show that the proposed method can achieve robust fencing under target maneuverability,partial UAV failures,and communication disturbances.
文摘This article started with an overview of the current technological status and engineering developments in the field of swarm munitions.It first introduced swarm behaviors and related swarm algorithms,and then provided a comprehensive summary of the research progress in the field of swarm munitions from four aspects:Collaborative perception and detection,collaborative positioning and navigation,task allocation for swarms,and path planning for swarms.In summary,future developments in collaborative perception,planning,positioning,navigation,and decision-making for swarm munitions will trend towards intelligence,adaptability,and collaboration.It can enable swarm munitions to be better adapted to complex and dynamic battlefields,improving operational effectiveness and mission capabilities.
文摘Low collateral damage weapons achieve controlled personnel injury through the coupling of shock waves and particle swarms,where the particle swarms arise from the high-explosive dispersion of compacted metal particle ring.To investigate the dynamic response of the human target under combined shock waves and particle swarms loading,a physical human surrogate torso model(HSTM)was developed,and the dynamic response test experiment was conducted under the combined loading.The effects of particle size on the loading parameters,the damage patterns of the ballistic plate and HSTM,and the dynamic response parameters of the HSTM with and without protection are mainly analyzed.Our findings revealed that particle swarms can effectively delay the shock wave attenuation,especially the best effect when the particle size was 0.28–0.45 mm.The ballistic plate mainly exhibited dense perforation of the outer fabric and impacted crater damage of ceramic plates,whereas the unprotected HSTM was mainly dominated by high-density and small-size ballistic cavity group damage.The peak values of the dynamic response parameters for the HSTM under combined loading were significantly larger than those under bare charge loading,with multiple peaks observed.Under unprotected conditions,the peak acceleration of skeletons and peak pressure of organs increased with the particle size.Under protected conditions,the particle size,the number of particles hit,and the fit of the ballistic plate to the HSTM together affected the dynamic response parameters of the HSTM.
基金supported by the National Natural Science Foundation of China(62406345).
文摘Developing intelligent unmanned swarm systems(IUSSs)is a highly intricate process.Although current simulators and toolchains have made a notable contribution to the develop-ment of algorithms for IUSSs,they tend to concentrate on iso-lated technical elements and are deficient in addressing the full spectrum of critical technologies and development needs in a systematic and integrative manner.Furthermore,the current suite of tools has not adequately addressed the challenge of bridging the gap between simulation and real-world deployment of algorithms.Therefore,a comprehensive solution must be developed that encompasses the entire IUSS development life-cycle.In this study,we present the RflySim ToolChain,which has been developed with the specific aim of facilitating the rapid development and validation of IUSSs.The RflySim ToolChain employs a model-based design(MBD)approach,integrating a modeling and simulation module,a lower reliable control mo-dule,and an upper swarm decision-making module.This compre-hensive integration encompasses the entire process,from mo-deling and simulation to testing and deployment,thereby enabling users to rapidly construct and validate IUSSs.The prin-cipal advantages of the RflySim ToolChain are as follows:it pro-vides a comprehensive solution that meets the full-stack devel-opment needs of IUSSs;the highly modular architecture and comprehensive software development kit(SDK)facilitate the automation of the entire IUSS development process.Further-more,the high-fidelity model design and reliable architecture solution ensure a seamless transition from simulation to real-world deployment,which is known as the simulation to reality(Sim2Real)process.This paper presents a series of case stu-dies that illustrate the effectiveness of the RflySim ToolChain in supporting the research and application of IUSSs.
基金supported by the Intelligent Aerospace System Leading Innovation Team Program of Zhejiang(2022R01003).
文摘In the realm of missile defense systems,the self-sufficient maneuver capacity of missile swarms is pivotal for their survival.Through the analysis of the missile dynamics model,a time-efficient cooperative attack strategy for missile swarm is proposed.Based on the distribution of the attackers and defenders,the collision avoidance against the defenders is considered during the attack process.By analyzing the geometric relationship between the relative velocity vector and relative position vector of the attackers and defenders,the collision avoidance constrains of attacking swarm are redefined.The key point is on adjusting the relative velocity vectors to fall outside the collision cone.This work facilitates high-precision attack toward the target while keeping safe missing distance between other attackers during collision avoidance process.By leveraging an innovative repulsion artificial function,a time-efficient cooperative attack strategy for missile swarm is obtained.Through rigorous simulation,the effectiveness of this cooperative attack strategy is substantiated.Furthermore,by employing Monte Carlo simulation,the success rate of the cooperative attack strategy is assessesed and the optimal configuration for the missile swarm is deduced.
基金supported by the Natural Science Foundation of China,Grant No.62103052.
文摘Drone swarm systems,equipped with photoelectric imaging and intelligent target perception,are essential for reconnaissance and strike missions in complex and high-risk environments.They excel in information sharing,anti-jamming capabilities,and combat performance,making them critical for future warfare.However,varied perspectives in collaborative combat scenarios pose challenges to object detection,hindering traditional detection algorithms and reducing accuracy.Limited angle-prior data and sparse samples further complicate detection.This paper presents the Multi-View Collaborative Detection System,which tackles the challenges of multi-view object detection in collaborative combat scenarios.The system is designed to enhance multi-view image generation and detection algorithms,thereby improving the accuracy and efficiency of object detection across varying perspectives.First,an observation model for three-dimensional targets through line-of-sight angle transformation is constructed,and a multi-view image generation algorithm based on the Pix2Pix network is designed.For object detection,YOLOX is utilized,and a deep feature extraction network,BA-RepCSPDarknet,is developed to address challenges related to small target scale and feature extraction challenges.Additionally,a feature fusion network NS-PAFPN is developed to mitigate the issue of deep feature map information loss in UAV images.A visual attention module(BAM)is employed to manage appearance differences under varying angles,while a feature mapping module(DFM)prevents fine-grained feature loss.These advancements lead to the development of BA-YOLOX,a multi-view object detection network model suitable for drone platforms,enhancing accuracy and effectively targeting small objects.
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.
基金supported by the National Natural Science Foundation of China(72101263)the Natural Science Foundation of Hunan Province(2023JJ40677).
文摘Aiming at the characteristics of autonomy,confrontation,and uncertainty in unmanned aerial vehicle(UAV)swarm operations,case-based reasoning(CBR)technology with advantages such as weak dependence on domain knowledge and efficient problem-solving is introduced,and a recommendation method for UAV swarm operation strategies based on CBR is proposed.Firstly,we design a universal framework for UAV swarm operation strategies from three dimensions:operation effectiveness,time,and cost.Secondly,based on the representation of operation cases,certain,fuzzy,interval,and classification attribute similarity calculation methods,as well as entropybased attribute weight allocation methods,are suggested to support the calculation of global similarity of cases.This method is utilized to match the source case with the most similarity from the historical case library,to obtain the optimal recommendation strategy for the target case.Finally,in the form of red blue confrontation,a UAV swarm operation strategy recommendation case is constructed based on actual battle cases,and a system simulation analysis is conducted.The results show that the strategy given in the example performs the best in three evaluation indicators,including cost-effectiveness,and overall outperforms other operation strategies.Therefore,the proposed method has advantages such as high real-time performance and interpretability,and can address the issue of recommending UAV swarm operation strategies in complex battlefield environments across both online and offline modes.At the same time,this study could also provide new ideas for the selection of UAV swarm operation strategies.
文摘Compared with single-domain unmanned swarms,cross-domain unmanned swarms continue to face new challenges in terms of platform performance and constraints.In this paper,a joint unmanned swarm target assignment and mission trajectory planning method is proposed to meet the requirements of cross-domain unmanned swarm mission planning.Firstly,the different performances of cross-domain heterogeneous platforms and mission requirements of targets are characterised by using a collection of operational resources.Secondly,an algorithmic framework for joint target assignment and mission trajectory planning is proposed,in which the initial planning of the trajectory is performed in the target assignment phase,while the trajectory is further optimised afterwards.Next,the estimation of the distribution algorithms is combined with the genetic algorithm to solve the objective function.Finally,the algorithm is numerically simulated by specific cases.Simulation results indicate that the proposed algorithm can perform effective task assignment and trajectory planning for cross-domain unmanned swarms.Furthermore,the solution performance of the hybrid estimation of distribution algorithm(EDA)-genetic algorithm(GA)algorithm is better than that of GA and EDA.
