Owing to the fast growth of global market,textile and apparel industries which are a typical seasonal business are facing crucial challenges from both competitors and consumers.In order to get survived,forming supply ...Owing to the fast growth of global market,textile and apparel industries which are a typical seasonal business are facing crucial challenges from both competitors and consumers.In order to get survived,forming supply chain and utilizing the emerged technology to establish a quick response system become an important common practice for enterprises in terms of cost reduction and efficiency improvement.This paper presents a multi-agent-based integrated framework for quick response in textile and apparel supply chain.By sharing information and collaborating among chain partner,the multi-agent system provides a promising computing paradigm for quick response business processes.A prototype based on the proposed framework is implemented using ZEUS toolkit.It presents how the proposed architecture is being designed to establish collaborative business environment by providing dynamic quick response processes.展开更多
The acceleration of urbanization has led to an increase in the number of urban floating population, which leads to more demands for the housing rental market. With the support of policies, long-term lease apartments h...The acceleration of urbanization has led to an increase in the number of urban floating population, which leads to more demands for the housing rental market. With the support of policies, long-term lease apartments have begun to emerge. However, under the multi-subject supply, longterm lease apartments have encountered problems such as small profits in their development. Starting from the background of the development of long-term lease apartments, this study classified the main types of long-term lease apartments, analyzed the four profit models of comprehensive profit, expansion of rent difference, REITs and value-added services based on their business models, and proposed corresponding suggestions on the profitability of long-term lease apartments according to the current situation of profit difficulty of long-term lease apartments and the lack of profit models.展开更多
With the advent of sixth-generation mobile communications(6G),space-air-ground integrated networks have become mainstream.This paper focuses on collaborative scheduling for mobile edge computing(MEC)under a three-tier...With the advent of sixth-generation mobile communications(6G),space-air-ground integrated networks have become mainstream.This paper focuses on collaborative scheduling for mobile edge computing(MEC)under a three-tier heterogeneous architecture composed of mobile devices,unmanned aerial vehicles(UAVs),and macro base stations(BSs).This scenario typically faces fast channel fading,dynamic computational loads,and energy constraints,whereas classical queuing-theoretic or convex-optimization approaches struggle to yield robust solutions in highly dynamic settings.To address this issue,we formulate a multi-agent Markov decision process(MDP)for an air-ground-fused MEC system,unify link selection,bandwidth/power allocation,and task offloading into a continuous action space and propose a joint scheduling strategy that is based on an improved MATD3 algorithm.The improvements include Alternating Layer Normalization(ALN)in the actor to suppress gradient variance,Residual Orthogonalization(RO)in the critic to reduce the correlation between the twin Q-value estimates,and a dynamic-temperature reward to enable adaptive trade-offs during training.On a multi-user,dual-link simulation platform,we conduct ablation and baseline comparisons.The results reveal that the proposed method has better convergence and stability.Compared with MADDPG,TD3,and DSAC,our algorithm achieves more robust performance across key metrics.展开更多
With the new characteristics of global cooperation in supply chains being synthetically considered,a hybrid model to the cooperative negotiation process for the order distribution in supply chain is mainly studied.Aft...With the new characteristics of global cooperation in supply chains being synthetically considered,a hybrid model to the cooperative negotiation process for the order distribution in supply chain is mainly studied.After reviewing and analyzing some main domestic and overseas processes in cooperative negotiation modeling in supply chain,some problems are subsequently pointed out.For example,the traditional simple multi-agent system(MAS)frameworks which have some limitations,are not suitable for solving modeling complex systems.To solve these problems,thinking with the aid of the multi-agent structure and complex system modeling,the manufacturing supply chain is taken as an example,and a time Petri net production model is adopted to decompose the materials.And then a cooperative negotiation model for the order distribution in supply chain is constructed based on combining multi-agent techniques with time Petri net modeling.The simulation results reveal that the above model helps solve the problems of cooperative negotiation in supply chains.展开更多
In order to overcome defects in existing ASCTS(Agricultural Supply Chain Traceability System,a new traceability system based on Multi-Agent System(MAS) is put forward.By qualitative method,I analyze problems of applic...In order to overcome defects in existing ASCTS(Agricultural Supply Chain Traceability System,a new traceability system based on Multi-Agent System(MAS) is put forward.By qualitative method,I analyze problems of application of Agent technology in tracing quality of agricultural products.Physical model is built for this system and structure of traceability system is determined.Finally,algorithm is presented for major entities.From analysis of algorithm,it is proved that this system has some reference value in improving breadth and depth of product traceability.展开更多
To acquire a competitive advantage in the expanding market,manufacturing enterprises should be able to manage their supply chains as effectively as possible.It is now becoming popular to model supply chains as multi-a...To acquire a competitive advantage in the expanding market,manufacturing enterprises should be able to manage their supply chains as effectively as possible.It is now becoming popular to model supply chains as multi-agent systems and use discrete event simulation to learn more about their behaviors or investigate the implications of alternative configurations.In order to enhance the computational efficiency and keep the simulation credibility,this paper proposes a message-driving formalism for the simulation of multi-agent supply chain systems.Through the message-driving formalism,the problem of shared variables is addressed and the parallel operation of agents is implemented.Simulation experiments with a prototype implementation show that the message-driving formalism is able to provide credible results in significantly less simulation time.展开更多
Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-...Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-robot control.Empowering cooperative MARL with multi-task decision-making capabilities is expected to further broaden its application scope.In multi-task scenarios,cooperative MARL algorithms need to address 3 types of multi-task problems:reward-related multi-task,arising from different reward functions;multi-domain multi-task,caused by differences in state and action spaces,state transition functions;and scalability-related multi-task,resulting from the dynamic variation in the number of agents.Most existing studies focus on scalability-related multitask problems.However,with the increasing integration between large language models(LLMs)and multi-agent systems,a growing number of LLM-based multi-agent systems have emerged,enabling more complex multi-task cooperation.This paper provides a comprehensive review of the latest advances in this field.By combining multi-task reinforcement learning with cooperative MARL,we categorize and analyze the 3 major types of multi-task problems under multi-agent settings,offering more fine-grained classifications and summarizing key insights for each.In addition,we summarize commonly used benchmarks and discuss future directions of research in this area,which hold promise for further enhancing the multi-task cooperation capabilities of multi-agent systems and expanding their practical applications in the real world.展开更多
This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method...This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.展开更多
This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary obj...This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.展开更多
Due to the characteristics of line-of-sight(LoS)communication in unmanned aerial vehicle(UAV)networks,these systems are highly susceptible to eavesdropping and surveillance.To effectively address the security concerns...Due to the characteristics of line-of-sight(LoS)communication in unmanned aerial vehicle(UAV)networks,these systems are highly susceptible to eavesdropping and surveillance.To effectively address the security concerns in UAV communication,covert communication methods have been adopted.This paper explores the joint optimization problem of trajectory and transmission power in a multi-hop UAV relay covert communication system.Considering the communication covertness,power constraints,and trajectory limitations,an algorithm based on multi-agent proximal policy optimization(MAPPO),named covert-MAPPO(C-MAPPO),is proposed.The proposed method leverages the strengths of both optimization algorithms and reinforcement learning to analyze and make joint decisions on the transmission power and flight trajectory strategies for UAVs to achieve cooperation.Simulation results demonstrate that the proposed method can maximize the system throughput while satisfying covertness constraints,and it outperforms benchmark algorithms in terms of system throughput and reward convergence speed.展开更多
This paper proposes a prototype system for modeling and simulation of supply chains using a widely accepted agent platform Java agent development platform (JADE). A simple but practical coordination mechanism agent-ba...This paper proposes a prototype system for modeling and simulation of supply chains using a widely accepted agent platform Java agent development platform (JADE). A simple but practical coordination mechanism agent-based dynamic information network for supply chains (ADINS) is employed for the illustration of the suggested system and a simulation experiment is performed using a supply chain model of a Korean LCD manufacturing company. The result shows that the suggested mechanism is successful in reducing bullwhip effects and increasing service rates.展开更多
Multi-agent systems(MASs)have demonstrated significant achievements in a wide range of tasks,leveraging their capacity for coordination and adaptation within complex environments.Moreover,the enhancement of their inte...Multi-agent systems(MASs)have demonstrated significant achievements in a wide range of tasks,leveraging their capacity for coordination and adaptation within complex environments.Moreover,the enhancement of their intelligent functionalities is crucial for tackling increasingly challenging tasks.This goal resonates with a paradigm shift within the artificial intelligence(AI)community,from“internet AI”to“embodied AI”,and the MASs with embodied AI are referred to as embodied multi-agent systems(EMASs).An EMAS has the potential to acquire generalized competencies through interactions with environments,enabling it to effectively address a variety of tasks and thereby make a substantial contribution to the quest for artificial general intelligence.Despite the burgeoning interest in this domain,a comprehensive review of EMAS has been lacking.This paper offers analysis and synthesis for EMASs from a control perspective,conceptualizing each embodied agent as an entity equipped with a“brain”for decision and a“body”for environmental interaction.System designs are classified into open-loop,closed-loop,and double-loop categories,and EMAS implementations are discussed.Additionally,the current applications and challenges faced by EMASs are summarized and potential avenues for future research in this field are provided.展开更多
This paper mainly focuses on the velocity-constrained consensus problem of discrete-time heterogeneous multi-agent systems with nonconvex constraints and arbitrarily switching topologies,where each agent has first-ord...This paper mainly focuses on the velocity-constrained consensus problem of discrete-time heterogeneous multi-agent systems with nonconvex constraints and arbitrarily switching topologies,where each agent has first-order or second-order dynamics.To solve this problem,a distributed algorithm is proposed based on a contraction operator.By employing the properties of the stochastic matrix,it is shown that all agents’position states could converge to a common point and second-order agents’velocity states could remain in corresponding nonconvex constraint sets and converge to zero as long as the joint communication topology has one directed spanning tree.Finally,the numerical simulation results are provided to verify the effectiveness of the proposed algorithms.展开更多
The development of chassis active safety control technology has improved vehicle stability under extreme conditions.However,its cross-system and multi-functional characteristics make the controller difficult to achiev...The development of chassis active safety control technology has improved vehicle stability under extreme conditions.However,its cross-system and multi-functional characteristics make the controller difficult to achieve cooperative goals.In addition,the chassis system,which has high complexity,numerous subsystems,and strong coupling,will also lead to low computing efficiency and poor control effect of the controller.Therefore,this paper proposes a scenario-driven hybrid distributed model predictive control algorithm with variable control topology.This algorithm divides multiple stability regions based on the vehicle’s β−γ phase plane,forming a mapping relationship between the control structure and the vehicle’s state.A control input fusion mechanism within the transition domain is designed to mitigate the problems of system state oscillation and control input jitter caused by switching control structures.Then,a distributed state-space equation with state coupling and input coupling characteristics is constructed,and a weighted local agent cost function in quadratic programming is derived.Through cost coupling,local agents can coordinate global performance goals.Finally,through Simulink/CarSim joint simulation and hardware-in-the-loop(HIL)test,the proposed algorithm is validated to improve vehicle stability while ensuring trajectory tracking accuracy and has good applicability for multi-objective coordinated control.This paper combines the advantages of distributed MPC and decentralized MPC,achieving a balance between approximating the global optimal results and the solution’s efficiency.展开更多
This paper focuses on the problem of leaderfollowing consensus for nonlinear cascaded multi-agent systems.The control strategies for these systems are transformed into successive control problem schemes for lower-orde...This paper focuses on the problem of leaderfollowing consensus for nonlinear cascaded multi-agent systems.The control strategies for these systems are transformed into successive control problem schemes for lower-order error subsystems.A distributed consensus analysis for the corresponding error systems is conducted by employing recursive methods and virtual controllers,accompanied by a series of Lyapunov functions devised throughout the iterative process,which solves the leaderfollowing consensus problem of a class of nonlinear cascaded multi-agent systems.Specific simulation examples illustrate the effectiveness of the proposed control algorithm.展开更多
In this paper,the distributed optimal formation control problem of heterogeneous Euler–Lagrange multi-agent systems with generic formation constraints and inequality constraints is investigated.Based on the primal–d...In this paper,the distributed optimal formation control problem of heterogeneous Euler–Lagrange multi-agent systems with generic formation constraints and inequality constraints is investigated.Based on the primal–dual dynamics and the adaptive control technique,a distributed optimal formation controller consists of a velocity reference signal generator and a velocity tracking controller is proposed.By using the optimality condition,the relationship between the equilibrium point of the closed-loop system and the optimal solution of the optimization problem is established.Then,by utilizing Lyapunov stability analysis,it is rigorously proved that the optimal formation is reached with the proposed controller.Lastly,simulation examples are provided to substantiate the theoretical results.展开更多
This article investigates the time-varying output group formation tracking control(GFTC)problem for heterogeneous multi-agent systems(HMASs)under switching topologies.The objective is to design a distributed control s...This article investigates the time-varying output group formation tracking control(GFTC)problem for heterogeneous multi-agent systems(HMASs)under switching topologies.The objective is to design a distributed control strategy that enables the outputs of the followers to form the desired sub-formations and track the outputs of the leader in each subgroup.Firstly,novel distributed observers are developed to estimate the states of the leaders under switching topologies.Then,GFTC protocols are designed based on the proposed observers.It is shown that with the distributed protocol,the GFTC problem for HMASs under switching topologies is solved if the average dwell time associated with the switching topologies is larger than a fixed threshold.Finally,an example is provided to illustrate the effectiveness of the proposed control strategy.展开更多
When a fire breaks out in a high-rise building,the occlusion of smoke and obstacles results in dearth of crucial information concerning people in distress,thereby creating a challenge in their detection.Given the rest...When a fire breaks out in a high-rise building,the occlusion of smoke and obstacles results in dearth of crucial information concerning people in distress,thereby creating a challenge in their detection.Given the restricted sensing range of a single unmanned aerial vehicle(UAV)cam-era,enhancing the target recognition rate becomes challenging without target information.To tackle this issue,this paper proposes a multi-agent autonomous collaborative detection method for multi-targets in complex fire environments.The objective is to achieve the fusion of multi-angle visual information,effectively increasing the target’s information dimension,and ultimately address-ing the problem of low target recognition rate caused by the lack of target information.The method steps are as follows:first,the you only look once version5(YOLOv5)is used to detect the target in the image;second,the detected targets are tracked to monitor their movements and trajectories;third,the person re-identification(ReID)model is employed to extract the appearance features of targets;finally,by fusing the visual information from multi-angle cameras,the method achieves multi-agent autonomous collaborative detection.The experimental results show that the method effectively combines the visual information from multi-angle cameras,resulting in improved detec-tion efficiency for people in distress.展开更多
基金Supported by National Natural Science Foundation of China(No.70772073)Shanghai Social Science Foundation(No.2007BZH001)Shanghai Natural Science Foundation(No.07ZR14003)
文摘Owing to the fast growth of global market,textile and apparel industries which are a typical seasonal business are facing crucial challenges from both competitors and consumers.In order to get survived,forming supply chain and utilizing the emerged technology to establish a quick response system become an important common practice for enterprises in terms of cost reduction and efficiency improvement.This paper presents a multi-agent-based integrated framework for quick response in textile and apparel supply chain.By sharing information and collaborating among chain partner,the multi-agent system provides a promising computing paradigm for quick response business processes.A prototype based on the proposed framework is implemented using ZEUS toolkit.It presents how the proposed architecture is being designed to establish collaborative business environment by providing dynamic quick response processes.
文摘The acceleration of urbanization has led to an increase in the number of urban floating population, which leads to more demands for the housing rental market. With the support of policies, long-term lease apartments have begun to emerge. However, under the multi-subject supply, longterm lease apartments have encountered problems such as small profits in their development. Starting from the background of the development of long-term lease apartments, this study classified the main types of long-term lease apartments, analyzed the four profit models of comprehensive profit, expansion of rent difference, REITs and value-added services based on their business models, and proposed corresponding suggestions on the profitability of long-term lease apartments according to the current situation of profit difficulty of long-term lease apartments and the lack of profit models.
文摘With the advent of sixth-generation mobile communications(6G),space-air-ground integrated networks have become mainstream.This paper focuses on collaborative scheduling for mobile edge computing(MEC)under a three-tier heterogeneous architecture composed of mobile devices,unmanned aerial vehicles(UAVs),and macro base stations(BSs).This scenario typically faces fast channel fading,dynamic computational loads,and energy constraints,whereas classical queuing-theoretic or convex-optimization approaches struggle to yield robust solutions in highly dynamic settings.To address this issue,we formulate a multi-agent Markov decision process(MDP)for an air-ground-fused MEC system,unify link selection,bandwidth/power allocation,and task offloading into a continuous action space and propose a joint scheduling strategy that is based on an improved MATD3 algorithm.The improvements include Alternating Layer Normalization(ALN)in the actor to suppress gradient variance,Residual Orthogonalization(RO)in the critic to reduce the correlation between the twin Q-value estimates,and a dynamic-temperature reward to enable adaptive trade-offs during training.On a multi-user,dual-link simulation platform,we conduct ablation and baseline comparisons.The results reveal that the proposed method has better convergence and stability.Compared with MADDPG,TD3,and DSAC,our algorithm achieves more robust performance across key metrics.
基金The National Natural Science Foundation of China(No.70401013)the National Key Technology R&D Program of China during the 11th Five-Year Plan Period(No.2006BAH02A06)
文摘With the new characteristics of global cooperation in supply chains being synthetically considered,a hybrid model to the cooperative negotiation process for the order distribution in supply chain is mainly studied.After reviewing and analyzing some main domestic and overseas processes in cooperative negotiation modeling in supply chain,some problems are subsequently pointed out.For example,the traditional simple multi-agent system(MAS)frameworks which have some limitations,are not suitable for solving modeling complex systems.To solve these problems,thinking with the aid of the multi-agent structure and complex system modeling,the manufacturing supply chain is taken as an example,and a time Petri net production model is adopted to decompose the materials.And then a cooperative negotiation model for the order distribution in supply chain is constructed based on combining multi-agent techniques with time Petri net modeling.The simulation results reveal that the above model helps solve the problems of cooperative negotiation in supply chains.
基金Supported by National Natural Science Foundation of China(71071001)
文摘In order to overcome defects in existing ASCTS(Agricultural Supply Chain Traceability System,a new traceability system based on Multi-Agent System(MAS) is put forward.By qualitative method,I analyze problems of application of Agent technology in tracing quality of agricultural products.Physical model is built for this system and structure of traceability system is determined.Finally,algorithm is presented for major entities.From analysis of algorithm,it is proved that this system has some reference value in improving breadth and depth of product traceability.
基金supported by National Key Technology R&D Program of China(Project No.2009BAH48B03)
文摘To acquire a competitive advantage in the expanding market,manufacturing enterprises should be able to manage their supply chains as effectively as possible.It is now becoming popular to model supply chains as multi-agent systems and use discrete event simulation to learn more about their behaviors or investigate the implications of alternative configurations.In order to enhance the computational efficiency and keep the simulation credibility,this paper proposes a message-driving formalism for the simulation of multi-agent supply chain systems.Through the message-driving formalism,the problem of shared variables is addressed and the parallel operation of agents is implemented.Simulation experiments with a prototype implementation show that the message-driving formalism is able to provide credible results in significantly less simulation time.
基金The National Natural Science Foundation of China(62136008,62293541)The Beijing Natural Science Foundation(4232056)The Beijing Nova Program(20240484514).
文摘Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-robot control.Empowering cooperative MARL with multi-task decision-making capabilities is expected to further broaden its application scope.In multi-task scenarios,cooperative MARL algorithms need to address 3 types of multi-task problems:reward-related multi-task,arising from different reward functions;multi-domain multi-task,caused by differences in state and action spaces,state transition functions;and scalability-related multi-task,resulting from the dynamic variation in the number of agents.Most existing studies focus on scalability-related multitask problems.However,with the increasing integration between large language models(LLMs)and multi-agent systems,a growing number of LLM-based multi-agent systems have emerged,enabling more complex multi-task cooperation.This paper provides a comprehensive review of the latest advances in this field.By combining multi-task reinforcement learning with cooperative MARL,we categorize and analyze the 3 major types of multi-task problems under multi-agent settings,offering more fine-grained classifications and summarizing key insights for each.In addition,we summarize commonly used benchmarks and discuss future directions of research in this area,which hold promise for further enhancing the multi-task cooperation capabilities of multi-agent systems and expanding their practical applications in the real world.
基金The National Natural Science Foundation of China(W2431048)The Science and Technology Research Program of Chongqing Municipal Education Commission,China(KJZDK202300807)The Chongqing Natural Science Foundation,China(CSTB2024NSCQQCXMX0052).
文摘This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.
基金supported by the National Natural Science Foundation of China(Nos.12272104,U22B2013).
文摘This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.
基金supported by the Natural Science Foundation of Jiangsu Province,China(No.BK20240200)in part by the National Natural Science Foundation of China(Nos.62271501,62071488,62471489 and U22B2002)+1 种基金in part by the Key Technologies R&D Program of Jiangsu,China(Prospective and Key Technologies for Industry)(Nos.BE2023022 and BE2023022-4)in part by the Post-doctoral Fellowship Program of CPSF,China(No.GZB20240996).
文摘Due to the characteristics of line-of-sight(LoS)communication in unmanned aerial vehicle(UAV)networks,these systems are highly susceptible to eavesdropping and surveillance.To effectively address the security concerns in UAV communication,covert communication methods have been adopted.This paper explores the joint optimization problem of trajectory and transmission power in a multi-hop UAV relay covert communication system.Considering the communication covertness,power constraints,and trajectory limitations,an algorithm based on multi-agent proximal policy optimization(MAPPO),named covert-MAPPO(C-MAPPO),is proposed.The proposed method leverages the strengths of both optimization algorithms and reinforcement learning to analyze and make joint decisions on the transmission power and flight trajectory strategies for UAVs to achieve cooperation.Simulation results demonstrate that the proposed method can maximize the system throughput while satisfying covertness constraints,and it outperforms benchmark algorithms in terms of system throughput and reward convergence speed.
文摘This paper proposes a prototype system for modeling and simulation of supply chains using a widely accepted agent platform Java agent development platform (JADE). A simple but practical coordination mechanism agent-based dynamic information network for supply chains (ADINS) is employed for the illustration of the suggested system and a simulation experiment is performed using a supply chain model of a Korean LCD manufacturing company. The result shows that the suggested mechanism is successful in reducing bullwhip effects and increasing service rates.
基金supported in part by National Natural Science Foundation of China(62495095,62088101).
文摘Multi-agent systems(MASs)have demonstrated significant achievements in a wide range of tasks,leveraging their capacity for coordination and adaptation within complex environments.Moreover,the enhancement of their intelligent functionalities is crucial for tackling increasingly challenging tasks.This goal resonates with a paradigm shift within the artificial intelligence(AI)community,from“internet AI”to“embodied AI”,and the MASs with embodied AI are referred to as embodied multi-agent systems(EMASs).An EMAS has the potential to acquire generalized competencies through interactions with environments,enabling it to effectively address a variety of tasks and thereby make a substantial contribution to the quest for artificial general intelligence.Despite the burgeoning interest in this domain,a comprehensive review of EMAS has been lacking.This paper offers analysis and synthesis for EMASs from a control perspective,conceptualizing each embodied agent as an entity equipped with a“brain”for decision and a“body”for environmental interaction.System designs are classified into open-loop,closed-loop,and double-loop categories,and EMAS implementations are discussed.Additionally,the current applications and challenges faced by EMASs are summarized and potential avenues for future research in this field are provided.
基金2024 Jiangsu Province Youth Science and Technology Talent Support Project2024 Yancheng Key Research and Development Plan(Social Development)projects,“Research and Application of Multi Agent Offline Distributed Trust Perception Virtual Wireless Sensor Network Algorithm”and“Research and Application of a New Type of Fishery Ship Safety Production Monitoring Equipment”。
文摘This paper mainly focuses on the velocity-constrained consensus problem of discrete-time heterogeneous multi-agent systems with nonconvex constraints and arbitrarily switching topologies,where each agent has first-order or second-order dynamics.To solve this problem,a distributed algorithm is proposed based on a contraction operator.By employing the properties of the stochastic matrix,it is shown that all agents’position states could converge to a common point and second-order agents’velocity states could remain in corresponding nonconvex constraint sets and converge to zero as long as the joint communication topology has one directed spanning tree.Finally,the numerical simulation results are provided to verify the effectiveness of the proposed algorithms.
基金Supported by National Natural Science Foundation of China(Grant Nos.52225212,52272418,U22A20100)National Key Research and Development Program of China(Grant No.2022YFB2503302).
文摘The development of chassis active safety control technology has improved vehicle stability under extreme conditions.However,its cross-system and multi-functional characteristics make the controller difficult to achieve cooperative goals.In addition,the chassis system,which has high complexity,numerous subsystems,and strong coupling,will also lead to low computing efficiency and poor control effect of the controller.Therefore,this paper proposes a scenario-driven hybrid distributed model predictive control algorithm with variable control topology.This algorithm divides multiple stability regions based on the vehicle’s β−γ phase plane,forming a mapping relationship between the control structure and the vehicle’s state.A control input fusion mechanism within the transition domain is designed to mitigate the problems of system state oscillation and control input jitter caused by switching control structures.Then,a distributed state-space equation with state coupling and input coupling characteristics is constructed,and a weighted local agent cost function in quadratic programming is derived.Through cost coupling,local agents can coordinate global performance goals.Finally,through Simulink/CarSim joint simulation and hardware-in-the-loop(HIL)test,the proposed algorithm is validated to improve vehicle stability while ensuring trajectory tracking accuracy and has good applicability for multi-objective coordinated control.This paper combines the advantages of distributed MPC and decentralized MPC,achieving a balance between approximating the global optimal results and the solution’s efficiency.
基金National Natural Science Foundation of China(No.12071370)。
文摘This paper focuses on the problem of leaderfollowing consensus for nonlinear cascaded multi-agent systems.The control strategies for these systems are transformed into successive control problem schemes for lower-order error subsystems.A distributed consensus analysis for the corresponding error systems is conducted by employing recursive methods and virtual controllers,accompanied by a series of Lyapunov functions devised throughout the iterative process,which solves the leaderfollowing consensus problem of a class of nonlinear cascaded multi-agent systems.Specific simulation examples illustrate the effectiveness of the proposed control algorithm.
基金supported in part by the National Key Research and Development Program of China under Grant 2022YFB3303900in part by the National Natural Science Foundation of China under Grants 62103277 and 62025305。
文摘In this paper,the distributed optimal formation control problem of heterogeneous Euler–Lagrange multi-agent systems with generic formation constraints and inequality constraints is investigated.Based on the primal–dual dynamics and the adaptive control technique,a distributed optimal formation controller consists of a velocity reference signal generator and a velocity tracking controller is proposed.By using the optimality condition,the relationship between the equilibrium point of the closed-loop system and the optimal solution of the optimization problem is established.Then,by utilizing Lyapunov stability analysis,it is rigorously proved that the optimal formation is reached with the proposed controller.Lastly,simulation examples are provided to substantiate the theoretical results.
文摘This article investigates the time-varying output group formation tracking control(GFTC)problem for heterogeneous multi-agent systems(HMASs)under switching topologies.The objective is to design a distributed control strategy that enables the outputs of the followers to form the desired sub-formations and track the outputs of the leader in each subgroup.Firstly,novel distributed observers are developed to estimate the states of the leaders under switching topologies.Then,GFTC protocols are designed based on the proposed observers.It is shown that with the distributed protocol,the GFTC problem for HMASs under switching topologies is solved if the average dwell time associated with the switching topologies is larger than a fixed threshold.Finally,an example is provided to illustrate the effectiveness of the proposed control strategy.
文摘When a fire breaks out in a high-rise building,the occlusion of smoke and obstacles results in dearth of crucial information concerning people in distress,thereby creating a challenge in their detection.Given the restricted sensing range of a single unmanned aerial vehicle(UAV)cam-era,enhancing the target recognition rate becomes challenging without target information.To tackle this issue,this paper proposes a multi-agent autonomous collaborative detection method for multi-targets in complex fire environments.The objective is to achieve the fusion of multi-angle visual information,effectively increasing the target’s information dimension,and ultimately address-ing the problem of low target recognition rate caused by the lack of target information.The method steps are as follows:first,the you only look once version5(YOLOv5)is used to detect the target in the image;second,the detected targets are tracked to monitor their movements and trajectories;third,the person re-identification(ReID)model is employed to extract the appearance features of targets;finally,by fusing the visual information from multi-angle cameras,the method achieves multi-agent autonomous collaborative detection.The experimental results show that the method effectively combines the visual information from multi-angle cameras,resulting in improved detec-tion efficiency for people in distress.
