A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source di...A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.展开更多
In response to the problems existing in the teaching of unmanned systems courses,such as being confined to traditional teaching models and insufficient focus on practical application,this paper proposes to guide the t...In response to the problems existing in the teaching of unmanned systems courses,such as being confined to traditional teaching models and insufficient focus on practical application,this paper proposes to guide the teaching with the OBE concept,carry out the teaching goal planning of unmanned systems application based on the OBE concept,innovate teaching methods,reconstruct course content,revitalize the teaching process,improve the evaluation model,and stimulate learning motivation to enhance the quality of course teaching and achieve the teaching goal of“knowledge+ability.”This has a certain reference value for the reform practice of unmanned systems courses.展开更多
Dear Editor,This letter investigates the problem of multi-dimension formation tracking(MDFT)for the cross-domain unmanned systems,including several interconnected agents,namely,unmanned aerial vehicles(UAVs)and unmann...Dear Editor,This letter investigates the problem of multi-dimension formation tracking(MDFT)for the cross-domain unmanned systems,including several interconnected agents,namely,unmanned aerial vehicles(UAVs)and unmanned surface vehicles(USVs).We assume that each agent suffers from by the mixed constraints on its velocity,control input and Euler angle.Solving the MDFT problem implies that 1)The virtual state of each USV is determined in the earth coordinate by expanding its 2D work space to the 3D space.展开更多
As advancements in the Internet of Things(IoT)and unmanned technologies continues to progress,the development of unmanned system of systems(USS)has reached unprecedented levels.While prior research has predominantly e...As advancements in the Internet of Things(IoT)and unmanned technologies continues to progress,the development of unmanned system of systems(USS)has reached unprecedented levels.While prior research has predominantly examined temporal variations in USS resilience,spatial changes remain underexplored.However,USS may involve kinetic engagements and frequent spatial changes during mission execution,affecting signal interference in data layer communications.Although time-dependent factors primarily govern mission effectiveness of the USS,spatial factors influence the transmission stability of the data layer.Consequently,assessing spatiotemporal variations in USS performance is critical.To address these challenges,this study introduces a spatiotemporal resilience assessment framework,which evaluates USS resilience across both temporal and spatial dimensions.Furthermore,we propose a spatiotemporal resilience optimization scheme that enhances system adaptability throughout the mission lifecycle,with a particular emphasis on prevention and recovery strategies.Finally,we validate the validity of the proposed concepts and methods with a case study featuring a regular hexagonal deployment of USS.The results show that the spatiotemporal resilience can better reflect the spatial change characteristics of USS,and the proposed optimization strategy improves the prevention spatiotemporal resilience,recovery spatiotemporal resilience,and entire-process spatiotemporal resilience of USS by 0.22%,8.39%,and 11.29%,respectively.展开更多
In the process of performing a task,autonomous unmanned systems face the problem of scene changing,which requires the ability of real-time decision-making under dynamically changing scenes.Therefore,taking the unmanne...In the process of performing a task,autonomous unmanned systems face the problem of scene changing,which requires the ability of real-time decision-making under dynamically changing scenes.Therefore,taking the unmanned system coordinative region control operation as an example,this paper combines knowledge representation with probabilistic decisionmaking and proposes a role-based Bayesian decision model for autonomous unmanned systems that integrates scene cognition and individual preferences.Firstly,according to utility value decision theory,the role-based utility value decision model is proposed to realize task coordination according to the preference of the role that individual is assigned.Then,multi-entity Bayesian network is introduced for situation assessment,by which scenes and their uncertainty related to the operation are semantically described,so that the unmanned systems can conduct situation awareness in a set of scenes with uncertainty.Finally,the effectiveness of the proposed method is verified in a virtual task scenario.This research has important reference value for realizing scene cognition,improving cooperative decision-making ability under dynamic scenes,and achieving swarm level autonomy of unmanned systems.展开更多
Previously, the military establishment has been the primary developer and user of micro technologies associated with unmanned systems. As these technologies become available commercially, a need exists to integrate th...Previously, the military establishment has been the primary developer and user of micro technologies associated with unmanned systems. As these technologies become available commercially, a need exists to integrate the use of the technology into local or regional public safety and homeland security incidents. The purpose of this presentation is to explain several key factors to consider when using micro technologies and unmanned systems in support of public safety and homeland security officials. Real time information is critical to the decision making process for public safety and homeland security officials to make assessments and quickly resolve crisis situations. Unmanned micro-vehicles and micro technologies are well suited to remotely observe, gather essential information, and immediately relay it to incident responders. These technologies can provide extremely important support during responses to hostage situations, hazardous environments, search and rescue, natural disasters, border patrol and many others. The true benefit is having remote resources providing real time support to incident responders. This paper discusses the use of several different types of micro-vehicle platforms in public safety scenarios and their use of associated technologies such as GPS （Global Positioning System） autopilot, communication, and sensor devices.展开更多
Welcome to this special issue of The International Journal of Intelligent Control and Systems(IJICS),which is dedicated to Autonomous Intelligence for Unmanned Systems.In recent years,we have witnessed a rapid increas...Welcome to this special issue of The International Journal of Intelligent Control and Systems(IJICS),which is dedicated to Autonomous Intelligence for Unmanned Systems.In recent years,we have witnessed a rapid increase in the deployment of unmanned systems across a wide range of civilian and military applications,including unmanned aerial vehicles(UAVs),autonomous ground vehicles(AGVs),and unmanned surface vessels(USVs).It is critically important that effective analysis and control be carried out for these systems,especially when operating in complex and dynamic environments.The autonomous control capability has emerged as one of the key factors determining their success in task execution.Consequently,significant research efforts are now focused on enhancing the autonomy,robustness,and safety of unmanned systems,as well as exploring novel control strategies and advanced technical approaches to address these challenges.展开更多
With the rapid expansion of unmanned system capabilities,integrating and sharing computing resources has become essential.In addition to enhancing resource utilization efficiency,this architecture may also introduce c...With the rapid expansion of unmanned system capabilities,integrating and sharing computing resources has become essential.In addition to enhancing resource utilization efficiency,this architecture may also introduce conflicts related to resource competition.Therefore,effective resource-sharing configurations are crucial to ensure the Safety of the Intended Functionality(SOTIF).This paper proposes a computing resource configuration analysis and optimization methods for SOTIF.First,four SOTIF requirements are explored using the computing resource-sharing architecture for unmanned systems,encompassing computing time,computing power,energy consumption restrictions,and mutual exclusion and correlation.Secondly,the computing resource configuration model and its SOTIF constraints are formalized based on the graph and set theories.Subsequently,this study divides the design process of computing resource configuration schemes into resource selection and allocation.It introduces a resource selection optimization method based on Forward Checking and a resource allocation optimization method based on NSGA-II.Finally,a typical unmanned driving scenario is considered as an example,and the optimal resource selection and allocation schemes are sequentially determined using the proposed method on the computing platform.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
The use of unmanned aerial system(UAS)in congested airspace and/or in the proximity of critical infrastructure poses several challenges as far as safe and secure operations are concerned.The paper provides a detailed ...The use of unmanned aerial system(UAS)in congested airspace and/or in the proximity of critical infrastructure poses several challenges as far as safe and secure operations are concerned.The paper provides a detailed description of the architecture and workflow of a platform for UAS traffic management(UTM),designed to pave the way for increased,improved and safer UAS operations in the civil airspace.In particular,access to low-altitude airspace for UAS operations is managed,while facilitating the implementation of beyond visual line-of-sight(BVLOS)operations,and ensuring a safe and efficient integration of UAS into both controlled and uncontrolled airspace.Detection and management of unidentified or uncooperative UAS’s is also taken care of.To this end,an architecture based on three interacting layers is proposed,with the air traffic control at the highest level,the UAS operator(s)at the bottom,and a UAS service supplier acting as an interface.The platform,with its physical and digital elements,guarantees the effective and efficient interaction among these three layers,including management of contingency scenarios,which require a variation of admissible flight volumes for UAS operations and/or fast trajectory re-planning.The platform,developed within a research project which involved several partners,was tested in a relevant operational scenario at the Grottaglie-Taranto airport in Italy.The operators involved in the tests provided positive feedback on the services provided by the platform and the usability of the interfaces,while also making suggestions for adding new features in future developments.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
Aimed at the doubly near-far problems in a large range suffered by the remote user group and in a small range existing in both nearby and remote user groups during energy harvesting and computation offloading,a resour...Aimed at the doubly near-far problems in a large range suffered by the remote user group and in a small range existing in both nearby and remote user groups during energy harvesting and computation offloading,a resource allocation method for unmanned aerial vehicle(UAV)-assisted and user cooperation non-linear energy harvesting mobile edge computing(MEC)system is proposed.The UAV equipped with an MEC server is introduced to provide energy and computing services for the remote user group to alleviate the doubly near-far problem in a large range suffered by the remote user group.The doubly near-far problem in a small range existing in both nearby and remote user groups is mitigated by user cooperation.The specific user cooperation strategy is that the user near the base station or the UAV is used as a relay to transfer the computing task of the user far from the base station or the UAV to the MEC server for computing.By jointly optimizing users’offloading time,users’transmitting power,and the hovering position of the UAV,the resource allocation problem is modeled as a nonlinear programming problem with the objective of maximizing computation efficiency.The suboptimal solution is obtained by adopting the differential evolution algorithm.Simulation results show that,compared with the resource allocation method based on genetic algorithm and the without user cooperation method,the proposed method has higher computation efficiency.展开更多
A security issue with multi-sensor unmanned aerial vehicle(UAV)cyber physical systems(CPS)from the viewpoint of a false data injection(FDI)attacker is investigated in this paper.The FDI attacker can employ attacks on ...A security issue with multi-sensor unmanned aerial vehicle(UAV)cyber physical systems(CPS)from the viewpoint of a false data injection(FDI)attacker is investigated in this paper.The FDI attacker can employ attacks on feedback and feed-forward channels simultaneously with limited resource.The attacker aims at degrading the UAV CPS's estimation performance to the max while keeping stealthiness characterized by the Kullback-Leibler(K-L)divergence.The attacker is resource limited which can only attack part of sensors,and the attacked sensor as well as specific forms of attack signals at each instant should be considered by the attacker.Also,the sensor selection principle is investigated with respect to time invariant attack covariances.Additionally,the optimal switching attack strategies in regard to time variant attack covariances are modeled as a multi-agent Markov decision process(MDP)with hybrid discrete-continuous action space.Then,the multi-agent MDP is solved by utilizing the deep Multi-agent parameterized Q-networks(MAPQN)method.Ultimately,a quadrotor near hover system is used to validate the effectiveness of the results in the simulation section.展开更多
Evaluating Unmanned Aerial Vehicle(UAV)systems within a System-of-Systems(SoS)environment helps clarify their contribution to the overall combat capability and supports effectiveness-oriented system optimization.When ...Evaluating Unmanned Aerial Vehicle(UAV)systems within a System-of-Systems(SoS)environment helps clarify their contribution to the overall combat capability and supports effectiveness-oriented system optimization.When assessing decision systems in such an environment,cross-level modeling and simulation are required,which often face a trade-off between low modeling cost and high simulation accuracy,while the credibility of results remains challenging to ensure.To address these issues,this study proposes a hybrid-granularity Hardware-In-the-Loop(HIL)SoS environment construction method based on Graphical Evaluation and Review Technique(GERT).The method employs GERT to analyze the relationships between simulation systems,the System Under Test(SUT),and mission outcomes,thereby determining the required model precision for different systems.A dynamic resource allocation algorithm is applied to adjust model granularity on demand,ensuring high-fidelity simulation under constrained total cost.Additionally,GERT estimates the computational frequency and communication bandwidth requirements of the SUT,guiding hardware selection to enhance simulation credibility.A UAV maritime combat case study was conducted for validation.The results demonstrate that,compared to the flat modeling approach,the hybrid-granularity scenario based on GERT analysis achieves higher simulation accuracy with lower overall model complexity.The coefficient of variation in evaluation results significantly decreases in HIL simulations compared to virtual simulations,confirming improved credibility.Under the hybrid-granularity HIL scenario,the decision system was evaluated from an effectiveness perspective,identifying the most sensitive performance parameter.Subsequent targeted optimization led to an 11.90%improvement in effectiveness,validating the method's practical utility.展开更多
Rapidly-exploring Random Tree(RRT)and its variants have become foundational in path-planning research,yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees l...Rapidly-exploring Random Tree(RRT)and its variants have become foundational in path-planning research,yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees lead to slow convergence and force an unfavorable trade-off between path quality and traversal safety.To address these challenges,we introduce HS-APF-RRT*,a novel algorithm that fuses layered sampling,an enhanced Artificial Potential Field(APF),and a dynamic neighborhood-expansion mechanism.First,the workspace is hierarchically partitioned into macro,meso,and micro sampling layers,progressively biasing random samples toward safer,lower-energy regions.Second,we augment the traditional APF by incorporating a slope-dependent repulsive term,enabling stronger avoidance of steep obstacles.Third,a dynamic expansion strategy adaptively switches between 8 and 16 connected neighborhoods based on local obstacle density,striking an effective balance between search efficiency and collision-avoidance precision.In simulated off-road scenarios,HS-APF-RRT*is benchmarked against RRT*,GoalBiased RRT*,and APF-RRT*,and demonstrates significantly faster convergence,lower path-energy consumption,and enhanced safety margins.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
As unmanned underwater vehicles (UUVs) are increasingly designed to perform long-duration missions in highly complex and often extreme environments, traditional design methods face significant and growing challenges^(...As unmanned underwater vehicles (UUVs) are increasingly designed to perform long-duration missions in highly complex and often extreme environments, traditional design methods face significant and growing challenges^([1,2]).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
With the continuous advancement of unmanned technology in various application domains,the development and deployment of blind-spot-free panoramic video systems have gained increasing importance.Such systems are partic...With the continuous advancement of unmanned technology in various application domains,the development and deployment of blind-spot-free panoramic video systems have gained increasing importance.Such systems are particularly critical in battlefield environments,where advanced panoramic video processing and wireless communication technologies are essential to enable remote control and autonomous operation of unmanned ground vehicles(UGVs).However,conventional video surveillance systems suffer from several limitations,including limited field of view,high processing latency,low reliability,excessive resource consumption,and significant transmission delays.These shortcomings impede the widespread adoption of UGVs in battlefield settings.To overcome these challenges,this paper proposes a novel multi-channel video capture and stitching system designed for real-time video processing.The system integrates the Speeded-Up Robust Features(SURF)algorithm and the Fast Library for Approximate Nearest Neighbors(FLANN)algorithm to execute essential operations such as feature detection,descriptor computation,image matching,homography estimation,and seamless image fusion.The fused panoramic video is then encoded and assembled to produce a seamless output devoid of stitching artifacts and shadows.Furthermore,H.264 video compression is employed to reduce the data size of the video stream without sacrificing visual quality.Using the Real-Time Streaming Protocol(RTSP),the compressed stream is transmitted efficiently,supporting real-time remote monitoring and control of UGVs in dynamic battlefield environments.Experimental results indicate that the proposed system achieves high stability,flexibility,and low latency.With a wireless link latency of 30 ms,the end-to-end video transmission latency remains around 140 ms,enabling smooth video communication.The system can tolerate packet loss rates(PLR)of up to 20%while maintaining usable video quality(with latency around 200 ms).These properties make it well-suited for mobile communication scenarios demanding high real-time video performance.展开更多
To conduct marine surveys,multiple unmanned surface vessels(Multi-USV)with different capabilities perform collaborative mapping in multiple designated areas.This paper proposes a task allocation algorithm based on int...To conduct marine surveys,multiple unmanned surface vessels(Multi-USV)with different capabilities perform collaborative mapping in multiple designated areas.This paper proposes a task allocation algorithm based on integer linear programming(ILP)with flow balance constraints,ensuring the fair and efficient distribution of sub-areas among USVs and maintaining strong connectivity of assigned regions.In the established gridmap,a search-based path planning algorithm is performed on the sub-areas according to the allocation scheme.It uses the greedy algorithm and the A*algorithm to achieve complete coverage of the barrier-free area and obtain an efficient trajectory of each USV.The greedy algorithm enables fast local traversal of unvisited grids,while the A*algorithm ensures navigation to escape from deadlock areas and maintains global path continuity.The comparison of task allocation results proves that the task allocation algorithm based on ILP improves the mapping efficiency and task distribution fairness.The proposed allocation method and result analysis provide a certain reference for the practical application ofMulti-USV to perform survey tasks collaboratively.展开更多
基金co-supported by the National Key R&D Program of China(No.2023YFB4704400)the Zhejiang Provincial Natural Science Foundation of China(No.LQ24F030012)the National Natural Science Foundation of China General Project(No.62373033)。
文摘A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.
文摘In response to the problems existing in the teaching of unmanned systems courses,such as being confined to traditional teaching models and insufficient focus on practical application,this paper proposes to guide the teaching with the OBE concept,carry out the teaching goal planning of unmanned systems application based on the OBE concept,innovate teaching methods,reconstruct course content,revitalize the teaching process,improve the evaluation model,and stimulate learning motivation to enhance the quality of course teaching and achieve the teaching goal of“knowledge+ability.”This has a certain reference value for the reform practice of unmanned systems courses.
基金supported in part by the National Natural Science Foundation of China(62073301,62373162,62473349,U24A20268,62233007)the Shenzhen Science and Technology Program(JCYJ20240813114007010).
文摘Dear Editor,This letter investigates the problem of multi-dimension formation tracking(MDFT)for the cross-domain unmanned systems,including several interconnected agents,namely,unmanned aerial vehicles(UAVs)and unmanned surface vehicles(USVs).We assume that each agent suffers from by the mixed constraints on its velocity,control input and Euler angle.Solving the MDFT problem implies that 1)The virtual state of each USV is determined in the earth coordinate by expanding its 2D work space to the 3D space.
基金support for this research from the Natural Science Foundation of Henan Province(252300421005).
文摘As advancements in the Internet of Things(IoT)and unmanned technologies continues to progress,the development of unmanned system of systems(USS)has reached unprecedented levels.While prior research has predominantly examined temporal variations in USS resilience,spatial changes remain underexplored.However,USS may involve kinetic engagements and frequent spatial changes during mission execution,affecting signal interference in data layer communications.Although time-dependent factors primarily govern mission effectiveness of the USS,spatial factors influence the transmission stability of the data layer.Consequently,assessing spatiotemporal variations in USS performance is critical.To address these challenges,this study introduces a spatiotemporal resilience assessment framework,which evaluates USS resilience across both temporal and spatial dimensions.Furthermore,we propose a spatiotemporal resilience optimization scheme that enhances system adaptability throughout the mission lifecycle,with a particular emphasis on prevention and recovery strategies.Finally,we validate the validity of the proposed concepts and methods with a case study featuring a regular hexagonal deployment of USS.The results show that the spatiotemporal resilience can better reflect the spatial change characteristics of USS,and the proposed optimization strategy improves the prevention spatiotemporal resilience,recovery spatiotemporal resilience,and entire-process spatiotemporal resilience of USS by 0.22%,8.39%,and 11.29%,respectively.
基金the Military Science Postgraduate Project of PLA(JY2020B006).
文摘In the process of performing a task,autonomous unmanned systems face the problem of scene changing,which requires the ability of real-time decision-making under dynamically changing scenes.Therefore,taking the unmanned system coordinative region control operation as an example,this paper combines knowledge representation with probabilistic decisionmaking and proposes a role-based Bayesian decision model for autonomous unmanned systems that integrates scene cognition and individual preferences.Firstly,according to utility value decision theory,the role-based utility value decision model is proposed to realize task coordination according to the preference of the role that individual is assigned.Then,multi-entity Bayesian network is introduced for situation assessment,by which scenes and their uncertainty related to the operation are semantically described,so that the unmanned systems can conduct situation awareness in a set of scenes with uncertainty.Finally,the effectiveness of the proposed method is verified in a virtual task scenario.This research has important reference value for realizing scene cognition,improving cooperative decision-making ability under dynamic scenes,and achieving swarm level autonomy of unmanned systems.
文摘Previously, the military establishment has been the primary developer and user of micro technologies associated with unmanned systems. As these technologies become available commercially, a need exists to integrate the use of the technology into local or regional public safety and homeland security incidents. The purpose of this presentation is to explain several key factors to consider when using micro technologies and unmanned systems in support of public safety and homeland security officials. Real time information is critical to the decision making process for public safety and homeland security officials to make assessments and quickly resolve crisis situations. Unmanned micro-vehicles and micro technologies are well suited to remotely observe, gather essential information, and immediately relay it to incident responders. These technologies can provide extremely important support during responses to hostage situations, hazardous environments, search and rescue, natural disasters, border patrol and many others. The true benefit is having remote resources providing real time support to incident responders. This paper discusses the use of several different types of micro-vehicle platforms in public safety scenarios and their use of associated technologies such as GPS （Global Positioning System） autopilot, communication, and sensor devices.
文摘Welcome to this special issue of The International Journal of Intelligent Control and Systems(IJICS),which is dedicated to Autonomous Intelligence for Unmanned Systems.In recent years,we have witnessed a rapid increase in the deployment of unmanned systems across a wide range of civilian and military applications,including unmanned aerial vehicles(UAVs),autonomous ground vehicles(AGVs),and unmanned surface vessels(USVs).It is critically important that effective analysis and control be carried out for these systems,especially when operating in complex and dynamic environments.The autonomous control capability has emerged as one of the key factors determining their success in task execution.Consequently,significant research efforts are now focused on enhancing the autonomy,robustness,and safety of unmanned systems,as well as exploring novel control strategies and advanced technical approaches to address these challenges.
基金supported by the National Natural Science Foundation of China(Grant Nos.72301296,72471192,72101270 and U2341213).
文摘With the rapid expansion of unmanned system capabilities,integrating and sharing computing resources has become essential.In addition to enhancing resource utilization efficiency,this architecture may also introduce conflicts related to resource competition.Therefore,effective resource-sharing configurations are crucial to ensure the Safety of the Intended Functionality(SOTIF).This paper proposes a computing resource configuration analysis and optimization methods for SOTIF.First,four SOTIF requirements are explored using the computing resource-sharing architecture for unmanned systems,encompassing computing time,computing power,energy consumption restrictions,and mutual exclusion and correlation.Secondly,the computing resource configuration model and its SOTIF constraints are formalized based on the graph and set theories.Subsequently,this study divides the design process of computing resource configuration schemes into resource selection and allocation.It introduces a resource selection optimization method based on Forward Checking and a resource allocation optimization method based on NSGA-II.Finally,a typical unmanned driving scenario is considered as an example,and the optimal resource selection and allocation schemes are sequentially determined using the proposed method on the computing platform.
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 European Union and Italian Ministry of University and Research through the call PON Research and Innovation 2014-2020,Axis Ⅱ,Action 2,project AcrOSS(Environment for Safe Operations of Remotely Piloted Aircraft),project number ARS01_00702-CUP:F36C18000210005.
文摘The use of unmanned aerial system(UAS)in congested airspace and/or in the proximity of critical infrastructure poses several challenges as far as safe and secure operations are concerned.The paper provides a detailed description of the architecture and workflow of a platform for UAS traffic management(UTM),designed to pave the way for increased,improved and safer UAS operations in the civil airspace.In particular,access to low-altitude airspace for UAS operations is managed,while facilitating the implementation of beyond visual line-of-sight(BVLOS)operations,and ensuring a safe and efficient integration of UAS into both controlled and uncontrolled airspace.Detection and management of unidentified or uncooperative UAS’s is also taken care of.To this end,an architecture based on three interacting layers is proposed,with the air traffic control at the highest level,the UAS operator(s)at the bottom,and a UAS service supplier acting as an interface.The platform,with its physical and digital elements,guarantees the effective and efficient interaction among these three layers,including management of contingency scenarios,which require a variation of admissible flight volumes for UAS operations and/or fast trajectory re-planning.The platform,developed within a research project which involved several partners,was tested in a relevant operational scenario at the Grottaglie-Taranto airport in Italy.The operators involved in the tests provided positive feedback on the services provided by the platform and the usability of the interfaces,while also making suggestions for adding new features in future developments.
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the National Natural Science Foundation of China(No.61871133)the Natural Science Foundation of Fujian Province(No.2021J01587)。
文摘Aimed at the doubly near-far problems in a large range suffered by the remote user group and in a small range existing in both nearby and remote user groups during energy harvesting and computation offloading,a resource allocation method for unmanned aerial vehicle(UAV)-assisted and user cooperation non-linear energy harvesting mobile edge computing(MEC)system is proposed.The UAV equipped with an MEC server is introduced to provide energy and computing services for the remote user group to alleviate the doubly near-far problem in a large range suffered by the remote user group.The doubly near-far problem in a small range existing in both nearby and remote user groups is mitigated by user cooperation.The specific user cooperation strategy is that the user near the base station or the UAV is used as a relay to transfer the computing task of the user far from the base station or the UAV to the MEC server for computing.By jointly optimizing users’offloading time,users’transmitting power,and the hovering position of the UAV,the resource allocation problem is modeled as a nonlinear programming problem with the objective of maximizing computation efficiency.The suboptimal solution is obtained by adopting the differential evolution algorithm.Simulation results show that,compared with the resource allocation method based on genetic algorithm and the without user cooperation method,the proposed method has higher computation efficiency.
文摘A security issue with multi-sensor unmanned aerial vehicle(UAV)cyber physical systems(CPS)from the viewpoint of a false data injection(FDI)attacker is investigated in this paper.The FDI attacker can employ attacks on feedback and feed-forward channels simultaneously with limited resource.The attacker aims at degrading the UAV CPS's estimation performance to the max while keeping stealthiness characterized by the Kullback-Leibler(K-L)divergence.The attacker is resource limited which can only attack part of sensors,and the attacked sensor as well as specific forms of attack signals at each instant should be considered by the attacker.Also,the sensor selection principle is investigated with respect to time invariant attack covariances.Additionally,the optimal switching attack strategies in regard to time variant attack covariances are modeled as a multi-agent Markov decision process(MDP)with hybrid discrete-continuous action space.Then,the multi-agent MDP is solved by utilizing the deep Multi-agent parameterized Q-networks(MAPQN)method.Ultimately,a quadrotor near hover system is used to validate the effectiveness of the results in the simulation section.
基金funded by Henan Key Laboratory of General Aviation Technology,grant number ZHKF-240202。
文摘Evaluating Unmanned Aerial Vehicle(UAV)systems within a System-of-Systems(SoS)environment helps clarify their contribution to the overall combat capability and supports effectiveness-oriented system optimization.When assessing decision systems in such an environment,cross-level modeling and simulation are required,which often face a trade-off between low modeling cost and high simulation accuracy,while the credibility of results remains challenging to ensure.To address these issues,this study proposes a hybrid-granularity Hardware-In-the-Loop(HIL)SoS environment construction method based on Graphical Evaluation and Review Technique(GERT).The method employs GERT to analyze the relationships between simulation systems,the System Under Test(SUT),and mission outcomes,thereby determining the required model precision for different systems.A dynamic resource allocation algorithm is applied to adjust model granularity on demand,ensuring high-fidelity simulation under constrained total cost.Additionally,GERT estimates the computational frequency and communication bandwidth requirements of the SUT,guiding hardware selection to enhance simulation credibility.A UAV maritime combat case study was conducted for validation.The results demonstrate that,compared to the flat modeling approach,the hybrid-granularity scenario based on GERT analysis achieves higher simulation accuracy with lower overall model complexity.The coefficient of variation in evaluation results significantly decreases in HIL simulations compared to virtual simulations,confirming improved credibility.Under the hybrid-granularity HIL scenario,the decision system was evaluated from an effectiveness perspective,identifying the most sensitive performance parameter.Subsequent targeted optimization led to an 11.90%improvement in effectiveness,validating the method's practical utility.
基金supported in part by 14th Five Year National Key R&D Program Project(Project Number:2023YFB3211001)the National Natural Science Foundation of China(62273339,U24A201397).
文摘Rapidly-exploring Random Tree(RRT)and its variants have become foundational in path-planning research,yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees lead to slow convergence and force an unfavorable trade-off between path quality and traversal safety.To address these challenges,we introduce HS-APF-RRT*,a novel algorithm that fuses layered sampling,an enhanced Artificial Potential Field(APF),and a dynamic neighborhood-expansion mechanism.First,the workspace is hierarchically partitioned into macro,meso,and micro sampling layers,progressively biasing random samples toward safer,lower-energy regions.Second,we augment the traditional APF by incorporating a slope-dependent repulsive term,enabling stronger avoidance of steep obstacles.Third,a dynamic expansion strategy adaptively switches between 8 and 16 connected neighborhoods based on local obstacle density,striking an effective balance between search efficiency and collision-avoidance precision.In simulated off-road scenarios,HS-APF-RRT*is benchmarked against RRT*,GoalBiased RRT*,and APF-RRT*,and demonstrates significantly faster convergence,lower path-energy consumption,and enhanced safety margins.
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 by the National Natural Science Foundation of China (Grant No.52405033)。
文摘As unmanned underwater vehicles (UUVs) are increasingly designed to perform long-duration missions in highly complex and often extreme environments, traditional design methods face significant and growing challenges^([1,2]).
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(Grant No.72334003)the National Key Research and Development Program of China(Grant No.2022YFB2702804)+1 种基金the Shandong Key Research and Development Program(Grant No.2020ZLYS09)the Jinan Program(Grant No.2021GXRC084-2).
文摘With the continuous advancement of unmanned technology in various application domains,the development and deployment of blind-spot-free panoramic video systems have gained increasing importance.Such systems are particularly critical in battlefield environments,where advanced panoramic video processing and wireless communication technologies are essential to enable remote control and autonomous operation of unmanned ground vehicles(UGVs).However,conventional video surveillance systems suffer from several limitations,including limited field of view,high processing latency,low reliability,excessive resource consumption,and significant transmission delays.These shortcomings impede the widespread adoption of UGVs in battlefield settings.To overcome these challenges,this paper proposes a novel multi-channel video capture and stitching system designed for real-time video processing.The system integrates the Speeded-Up Robust Features(SURF)algorithm and the Fast Library for Approximate Nearest Neighbors(FLANN)algorithm to execute essential operations such as feature detection,descriptor computation,image matching,homography estimation,and seamless image fusion.The fused panoramic video is then encoded and assembled to produce a seamless output devoid of stitching artifacts and shadows.Furthermore,H.264 video compression is employed to reduce the data size of the video stream without sacrificing visual quality.Using the Real-Time Streaming Protocol(RTSP),the compressed stream is transmitted efficiently,supporting real-time remote monitoring and control of UGVs in dynamic battlefield environments.Experimental results indicate that the proposed system achieves high stability,flexibility,and low latency.With a wireless link latency of 30 ms,the end-to-end video transmission latency remains around 140 ms,enabling smooth video communication.The system can tolerate packet loss rates(PLR)of up to 20%while maintaining usable video quality(with latency around 200 ms).These properties make it well-suited for mobile communication scenarios demanding high real-time video performance.
基金supported in part by the International Science and Technology Project of Guangzhou Development District under Grant 2023GH08the Science and Technology Development Fund,MSAR,under Grants 0029/2022/AGJ and 0029/2023/RIA1the Program of Guangdong under Grant 2023A0505020003.
文摘To conduct marine surveys,multiple unmanned surface vessels(Multi-USV)with different capabilities perform collaborative mapping in multiple designated areas.This paper proposes a task allocation algorithm based on integer linear programming(ILP)with flow balance constraints,ensuring the fair and efficient distribution of sub-areas among USVs and maintaining strong connectivity of assigned regions.In the established gridmap,a search-based path planning algorithm is performed on the sub-areas according to the allocation scheme.It uses the greedy algorithm and the A*algorithm to achieve complete coverage of the barrier-free area and obtain an efficient trajectory of each USV.The greedy algorithm enables fast local traversal of unvisited grids,while the A*algorithm ensures navigation to escape from deadlock areas and maintains global path continuity.The comparison of task allocation results proves that the task allocation algorithm based on ILP improves the mapping efficiency and task distribution fairness.The proposed allocation method and result analysis provide a certain reference for the practical application ofMulti-USV to perform survey tasks collaboratively.
