The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and...The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and efficient communication among vehicles of different types and capabilities.This paper proposes a data-driven vehicular heterogeneity-based intelligent collision avoidance system for IoV.The system leverages Vehicle-to-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication to collect real-time data about the environment and the vehicles.The data is collected to acknowledge the heterogeneity of vehicles and human behavior.The data is analyzed using machine learning algorithms to identify potential collision risks and recommend appropriate actions to avoid collisions.The system takes into account the heterogeneity of vehicles,such as their size,speed,and maneuverability,to optimize collision avoidance strategies.The proposed system is experimented with real-time datasets and compared with existing collision avoidance systems.The results are shown using the evaluation metrics that show the proposed system can significantly reduce the number of collisions and improve the overall safety and efficiency of IoV with an accuracy of 96.5%using the SVM algorithm.The trial outcomes demonstrated that the new system,incorporating vehicular,weather,and human behavior factors,outperformed previous systems that only considered vehicular and weather aspects.This innovative approach is poised to lead transportation efforts,reducing accident rates and improving the quality of transportation systems in smart cities.By offering predictive capabilities,the proposed model not only helps control accident rates but also prevents them in advance,ensuring road safety.展开更多
Dear Editor,This letter addresses the formation control problem for unmanned surface vehicles(USVs)under GPS-denied environments.A novel visual servo formation control scheme,utilizing a monocular camera on the follow...Dear Editor,This letter addresses the formation control problem for unmanned surface vehicles(USVs)under GPS-denied environments.A novel visual servo formation control scheme,utilizing a monocular camera on the follower to obtain the leader’s global position,is developed,which is also capable of guaranteeing collision avoidance and visibility maintenance(CA&VM)raised by the requirement of actual formation navigation.展开更多
The intelligent transportation systems require secure,low-latency,and reliable communication architectures to enable the real-time vehicular application.This paper proposes an edge-intelligent semantic aggregation(EIS...The intelligent transportation systems require secure,low-latency,and reliable communication architectures to enable the real-time vehicular application.This paper proposes an edge-intelligent semantic aggregation(EISA)framework for 6G unmanned aerial vehicle(UAV)-assisted Internet of vehicles(IoV)networks that integrates task-driven semantic communication,deep reinforcement learning(DRL)-based edge intelligence,and blockchain-based semantic validation across 6G terahertz(THz)links.UAVs in the proposed architecture serve as adaptive edge nodes that receive semantically vital information about the vehicle at any given stage,optimize aggregation and transmission parameters dynamically,and guarantee data integrity through a structured,lightweight consortium blockchain that signs semantically detailed representations rather than raw packets.Simulation results from a hybrid NS-3,MATLAB,and Python environment indicate that the proposed framework can achieve up to 45%reduction in end-to-end latency,an approximately 70%increase in throughput,and semantic efficiency with blockchain verification delays of less than 20 ms(more than 98%).These findings support the effectiveness of the proposed co-design for achieving context-aware,energy-efficient,and reliable communication under heavy-traffic conditions.The proposed framework provides a flexible and scalable foundation for next-generation 6G-enabled automotive networks,with subsequent growth toward federated learning-based collaborative intelligence,digital-twinassisted traffic modeling,and quantum-safe blockchain mechanisms to enhance scalability,intelligence,and long-term security.展开更多
Efficient torque allocation in over-actuated vehicles poses a central challenge in the domain of advanced vehicle control.These vehicles,featuring redundant actuators,provide an exceptional avenue for enhancing perfor...Efficient torque allocation in over-actuated vehicles poses a central challenge in the domain of advanced vehicle control.These vehicles,featuring redundant actuators,provide an exceptional avenue for enhancing performance,stability,and efficiency.This paper presents a pioneering tendency for torque allocation in the context of over-actuated vehicles,particularly inwheel motor(IWM)driven electric vehicles.We introduce a systematic methodology grounded in analytical modeling,allowing for the efficient reconciliation of multiple,often conflicting objectives.The explicit functions are analytically modeled to enhance stability and energy economy.Additionally,a fuzzy logic-based torque allocation strategy is developed and compared,along with other literature methods,with the analytical models.Simulations are conducted in a joint simulation between Simulink/MATLAB and SCANeR Studio vehicle dynamics simulator,followed by validation on a real-world dataset.Our findings elucidate the proficiency of the analytical models on vehicle performance,stability,computational efficiency,and energy consumption.展开更多
Blockchain offers a promising solution to the security challenges faced by the Internet of Vehicles(IoV).However,due to the dynamic connectivity of IoV,blockchain based on a single-chain structure or Directed Acyclic ...Blockchain offers a promising solution to the security challenges faced by the Internet of Vehicles(IoV).However,due to the dynamic connectivity of IoV,blockchain based on a single-chain structure or Directed Acyclic Graph(DAG)structure often suffer from performance limitations.The DAG lattice structure is a novel blockchain model in which each node maintains its own account chain,and only the node itself is allowed to update it.This feature makes the DAG lattice structure particularly suitable for addressing the challenges in dynamically connected IoV environment.In this paper,we propose a blockchain architecture based on the DAG lattice structure,specifically designed for dynamically connected IoV.In the proposed system,nodes must obtain authorization from a trusted authority before joining,forming a permissioned blockchain.Each node is assigned an individual account chain,allowing vehicles with limited storage capacity to participate in the blockchain by storing transactions only from nearby vehicles’account chains.Every transmitted message is treated as a transaction and added to the blockchain,enablingmore efficient data transmission in a dynamic network environment.Areputation-based incentivemechanism is introduced to encourage nodes to behave normally.Experimental results demonstrate that the proposed architecture achieves better performance compared with traditional single-chain and DAG-based approaches in terms of average transmission delay and storage cost.展开更多
The Internet of Vehicles,or IoV,is expected to lessen pollution,ease traffic,and increase road safety.IoV entities’interconnectedness,however,raises the possibility of cyberattacks,which can have detrimental effects....The Internet of Vehicles,or IoV,is expected to lessen pollution,ease traffic,and increase road safety.IoV entities’interconnectedness,however,raises the possibility of cyberattacks,which can have detrimental effects.IoV systems typically send massive volumes of raw data to central servers,which may raise privacy issues.Additionally,model training on IoV devices with limited resources normally leads to slower training times and reduced service quality.We discuss a privacy-preserving Federated Split Learning with Tiny Machine Learning(TinyML)approach,which operates on IoV edge devices without sharing sensitive raw data.Specifically,we focus on integrating split learning(SL)with federated learning(FL)and TinyML models.FL is a decentralisedmachine learning(ML)technique that enables numerous edge devices to train a standard model while retaining data locally collectively.The article intends to thoroughly discuss the architecture and challenges associated with the increasing prevalence of SL in the IoV domain,coupled with FL and TinyML.The approach starts with the IoV learning framework,which includes edge computing,FL,SL,and TinyML,and then proceeds to discuss how these technologies might be integrated.We elucidate the comprehensive operational principles of Federated and split learning by examining and addressingmany challenges.We subsequently examine the integration of SL with FL and various applications of TinyML.Finally,exploring the potential integration of FL and SL with TinyML in the IoV domain is referred to as FSL-TM.It is a superior method for preserving privacy as it conducts model training on individual devices or edge nodes,thereby obviating the necessity for centralised data aggregation,which presents considerable privacy threats.The insights provided aim to help both researchers and practitioners understand the complicated terrain of FL and SL,hence facilitating advancement in this swiftly progressing domain.展开更多
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain...Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.展开更多
[Objectives]To determine the optimal concentration of topping agents applied by unmanned aerial vehicles(UAVs)to effectively regulate cotton growth and improve production efficiency.[Methods]A field experiment was con...[Objectives]To determine the optimal concentration of topping agents applied by unmanned aerial vehicles(UAVs)to effectively regulate cotton growth and improve production efficiency.[Methods]A field experiment was conducted in Shihezi City,Xinjiang,employing a randomized block design.Five UAV-based chemical topping treatments were applied at dosages of 0.300,0.525,0.750,0.975,and 1.200 L/hm 2,designated as H1,H2,H3,H4,and H5,respectively.Additionally,manual topping(CK1)and tractor topping(CK2)treatments,both at a concentration of 0.750 L/hm 2,were included as control treatments.During the first 20 d following topping,parameters including primary agronomic traits of cotton(plant height,leaf age,number of fruit branches),dry matter accumulation and distribution,leaf area boll load(LAB),root-to-shoot ratio(RSR),leaf mass area(LMA),and leaf area index(LAI)were examined.At harvest,yield components,lint cotton yield,harvest index,and fiber quality were evaluated.[Results]Twenty days after topping,the concentration of the topping agent applied via UAV did not significantly affect cotton leaf age or the number of fruit branches.Additionally,no significant differences in plant height were observed among the five concentration treatments compared to CK2.However,plants treated with H1 exhibited significantly greater height compared to those treated with H5 and CK1,indicating that H1 was the least effective in controlling vegetative growth.Total dry matter accumulation(TDM),boll dry matter accumulation(BDM),LAB,and LMA all demonstrated an initial increase followed by a decrease as the spraying concentration increased.The highest TDM and reproductive organ dry matter ratio(RRDM)were observed in the H3 treatment.No significant differences were found among treatments for LMA,RSR,or LAI;however,LAB and single boll weight were greatest in the H3 treatment.Fiber quality parameters,including fiber length uniformity,micronaire(MIC),specific strength,and fiber maturity,initially increased and then decreased with increasing spraying concentration,whereas fiber elongation rate exhibited the opposite trend.The H3 treatment yielded the highest average fiber length uniformity and specific strength.[Conclusions]At optimal spraying concentrations,UAV-based application more effectively controls vegetative growth,promotes dry matter accumulation and distribution in cotton bolls,increases single boll weight,and enhances the MIC,specific strength,and fiber elongation rate of cotton fibers compared to manual and tractor spraying of topping agents.In summary,the use of UAVs for spraying chemical topping agents is recommended,with a suggested dosage range of 0.750 and 0.975 L/hm 2.展开更多
Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination syst...Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles(CAVs)at single-lane intersections,particularly in the context of left-hand side driving on roads.The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks.We consider that all approaching vehicles share relevant information through vehicular communications.The Intersection Coordination Unit(ICU)processes this information and communicates the optimal crossing or turning times to the vehicles.The primary objective of this coordination is to minimize overall traffic delays,which also helps improve the fuel consumption of vehicles.By considering information from upcoming vehicles at the intersection,the coordination system solves an optimization problem to determine the best timing for executing right turns,ultimately minimizing the total delay for all vehicles.The proposed coordination system is evaluated at a typical urban intersection,and its performance is compared to traditional traffic systems.Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.展开更多
Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based...Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based car-following(CF)framework employing the Deep Deterministic Policy Gradient(DDPG)algorithm,which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning.Utilizing real-world driving data from the highD dataset,the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios.The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control(MPC-ACC)controller.Results show that theDRLmodel significantly enhances safety,achieving zero collisions and a higher average time-to-collision(TTC)of 8.45 s,compared to 5.67 s for MPC and 6.12 s for human drivers.For efficiency,the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases.In terms of energy optimization,the proposed approach reduces fuel consumption by 5.4% relative to MPC.Additionally,it enhances passenger comfort by lowering jerk values by 65%,achieving 0.12 m/s3 vs.0.34 m/s3 for human drivers.A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics.Moreover,the findings underscore the potential of DRL in advancing autonomous vehicle control,offering a robust and sustainable solution for safer,more efficient,and more comfortable transportation systems.展开更多
This review sums up existing information on the environmental issues of critical materials in new energy vehicles(NEVs)on a combined mining to mobility approach.With the increase in the rate of NEV adoption,the enviro...This review sums up existing information on the environmental issues of critical materials in new energy vehicles(NEVs)on a combined mining to mobility approach.With the increase in the rate of NEV adoption,the environmental cost of operating vehicles will decline as the burden moves to upstream and downstream material life-cycle activities,such as extraction,beneficiation,refining,component manufacturing,use-phase performance,and end-of-life management.We focus on key material categories that provide electrified mobility,such as battery-related material(e.g.,Li,Ni,Co,Mn,graphite),high-performance motor-related material(e.g.,rare earth elements),conductive and lightweighting material(e.g.,Cu and Al).In the supply chain,the prevailing environmental forces consist of high energy requirements and related greenhouse gas emissions,excessive water consumption and water pollution risks,toxicity and human health issues pertaining to chemical inputs and metal discharges,land-use shift,and ecosystem and biodiversity effects.The review notes that there is high regional heterogeneity,which is fueled by ore grades,processing technologies,electricity mixes,and governance capacity,and that when measurements are narrowed to carbon measures,there is a risk of shifting the problem across geographies and categories of impacts.Mitigation pathways are analyzed,such as cleaner extraction and refining,material substitution and dematerialization,battery design,longevity and recyclability,and also the strategies of the circular economy,such as recycling and second-life use.Lastly,we establish research gaps in important areas of supply-chain data disclosure,multi-impact life-cycle assessment approaches,and integrated environmental-social analysis to enable sound policy formulation that can be used to achieve sustainable electrified mobility.展开更多
Anomaly detection is a vibrant research direction in controller area networks,which provides the fundamental real-time data transmission underpinning in-vehicle data interaction for the internet of vehicles.However,ex...Anomaly detection is a vibrant research direction in controller area networks,which provides the fundamental real-time data transmission underpinning in-vehicle data interaction for the internet of vehicles.However,existing unsupervised learning methods suffer from insufficient temporal and spatial constraints on shallow features,resulting in fragmented feature representations that compromise model stability and accuracy.To improve the extraction of valuable features,this paper investigates the influence of clustering constraints on shallow feature convergence paths at the model level and further proposes an end-to-end intrusion detection system based on efficient deep embedded subspace clustering(EDESC-IDS).Following the standard learning approach,continuous messages are encoded into two-dimensional data frames via a frame builder,which are then input into an extended convolutional autoencoder for extracting shallow features from high-dimensional data.On this basis,the dual constraints of these output features and the embedding clustering module facilitate end-to-end training of the EDESC-IDS in various attack scenarios.Extensive experimental results show that such a system exhibits significant detection performance on four types of attack datasets,including DoS,Gear,Fuzzy,and RPM,with precision,recall,and F1 scores consistently above 97.79%,while maintaining a false negative rate(FNR)and an error rate(ER)below 2.22%.展开更多
To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the conc...To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the concept of particle sight distance,an improved algorithm,called SD-IPSO,is proposed for the real-time autonomous navigation of USVs in marine environments.The algorithm refines the individual behavior pattern of particles in the population,effectively improving both local and global search capabilities while avoiding premature convergence.The effectiveness of the algorithm is validated using standard test functions from CEC-2017 function library,assessing it from multiple dimensions.Sensitivity analysis is conducted on key parameters in the algorithm,including particle sight distance and population size.Results indicate that compared with PSO,SD-IPSO demonstrates significant advantages in optimization accuracy and convergence speed.The application of SD-IPSO in path planning is further investigated through a 14-point traveling salesman problem(TSP)example and navigation autonomous tests of USVs in marine environments.Findings demonstrate that the proposed algorithm exhibits superior optimization capabilities and can effectively address the path planning challenges of USVs.展开更多
Autonomous vehicles operate without direct human intervention,which introduces safety risks that differ from those of conventional vehicles.Although many studies have examined safety issues related to autonomous drivi...Autonomous vehicles operate without direct human intervention,which introduces safety risks that differ from those of conventional vehicles.Although many studies have examined safety issues related to autonomous driving,high-risk situations have often been defined using single indicators,making it difficult to capture the complex and evolving nature of accident risk.To address this limitation,this study proposes a structured framework for defining and analyzing high-risk situations throughout the traffic accident process.High-risk situations are described using three complementary indicators:accident likelihood,accident severity,and accident duration.These indicators explain how risk emerges,increases,and persists over time.Based on this concept,a framework for traffic accident visualization analysis is developed to support phase-specific risk assessment and visualization.The framework combines accident-phase information with factor-level risk contributions,allowing systematic identification of key factors and their interactions across different accident stages.Using combinations of the three indicators,high-risk situations are classified into twenty-seven distinct types,providing a clear typology for complex accident scenarios involving autonomous vehicles.The applicability of the proposed framework is demonstrated through two representative accident scenarioswith different risk characteristics.The results showthat the framework effectively captures interactions among multiple risk factors,explains how risk levels change from pre-crash to post-crash phases,and identifies contributing factors that are difficult to detect using conventional traffic accident investigation methods.Overall,the proposed framework offers a practical basis for autonomous vehicle accident analysis,safety evaluation,and policy-related decision-making.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
Hypersonic morphing vehicle(HMV)can reconfigure aerodynamic geometries in real time,adapting to diverse needs like multi-mission profiles and wide-speed-range flight,spanwise morphing and sweep angle variation are rep...Hypersonic morphing vehicle(HMV)can reconfigure aerodynamic geometries in real time,adapting to diverse needs like multi-mission profiles and wide-speed-range flight,spanwise morphing and sweep angle variation are representative large-scale wing reconfiguration modes.To meet the HMV's need for an increased lift and a lift to drag ratio during hypersonic maneuverability and cruise or reentry equilibrium glide,this paper proposes an innovative single-DOF coupled morphing-wing system.We then systematically analyze its open-loop kinematics and closed-loop connectivity constraints,and the proposed system integrates three functional modules:the preset locking/release mechanism,the coupled morphing-wing mechanism,and the integrated wing locking with active stiffness control mechanism.Experimental validation confirms stable,continuous morphing under simulated aerodynamic loads.The experimental results indicate:(i)SMA actuators exhibit response times ranging from 18 s to 160 s,providing sufficient force output for wing unlocking;(ii)The integrated wing locking with active stiffness control mechanism effectively secures wing positions while eliminating airframe clearance via SMA actuation,improving the first-order natural frequency by more than 17%;(iii)The distributed aerodynamic loading system enables precise multi-stage follow-up loading during morphing,with the coupled morphing wing maintaining stable,continuous operation under 0-3500 N normal loads and 110-140 N axial force.The proposed single-DOF coupled morphing mechanism not only simplifies and improves structural efficiency but also demonstrates superior performance in locking control,stiffness enhancement,and aerodynamic responsiveness.This establishes a foundational framework for the design of future intelligent morphing configurations and the implementation of flight control systems.展开更多
Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instabili...Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.展开更多
As carrier aircraft sortie frequency and flight deck operational density increase,autonomous dispatch trajectory planning for carrier-based vehicles demands efficient,safe,and kinematically feasible solutions.This pap...As carrier aircraft sortie frequency and flight deck operational density increase,autonomous dispatch trajectory planning for carrier-based vehicles demands efficient,safe,and kinematically feasible solutions.This paper presents an Iterative Safe Dispatch Corridor(iSDC)framework,addressing the suboptimality of the traditional SDC method caused by static corridor construction and redundant obstacle exploration.First,a Kinodynamic-Informed-Bidirectional Rapidly-exploring Random Tree Star(KIBRRT^(*))algorithm is proposed for the front-end coarse planning.By integrating bidirectional tree expansion,goal-biased elliptical sampling,and artificial potential field guidance,it reduces unnecessary exploration near concave obstacles and generates kinematically admissible paths.Secondly,the traditional SDC is implemented in an iterative manner,and the obtained trajectory in the current iteration is fed into the next iteration for corridor generation,thus progressively improving the quality of withincorridor constraints.For tractors,a reverse-motion penalty function is incorporated into the back-end optimizer to prioritize forward driving,aligning with mechanical constraints and human operational preferences.Numerical validations on the data of Gerald R.Ford-class carrier demonstrate that the KIBRRT^(*)reduces average computational time by 75%and expansion nodes by 25%compared to conventional RRT^(*)algorithms.Meanwhile,the iSDC framework yields more time-efficient trajectories for both carrier aircraft and tractors,with the dispatch time reduced by 31.3%and tractor reverse motion proportion decreased by 23.4%relative to traditional SDC.The presented framework offers a scalable solution for autonomous dispatch in confined and safety-critical environment,and an illustrative animation is available at bilibili.com/video/BV1tZ7Zz6Eyz.Moreover,the framework can be easily extended to three-dimension scenarios,and thus applicable for trajectory planning of aerial and underwater vehicles.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
Flapping-Wing Micro Air Vehicles(FMAVs)are compact and agile,capable of accessing narrow spaces that conventional vehicles struggle to reach,such as ruins,caves,or the interiors of complex structures,making them ideal...Flapping-Wing Micro Air Vehicles(FMAVs)are compact and agile,capable of accessing narrow spaces that conventional vehicles struggle to reach,such as ruins,caves,or the interiors of complex structures,making them ideal tools for reconnaissance and rescue missions.1 However,the operation of FMAVs relies on coordinating multiple forces with different scaling effects,posing challenges to miniaturization design.展开更多
文摘The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and efficient communication among vehicles of different types and capabilities.This paper proposes a data-driven vehicular heterogeneity-based intelligent collision avoidance system for IoV.The system leverages Vehicle-to-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication to collect real-time data about the environment and the vehicles.The data is collected to acknowledge the heterogeneity of vehicles and human behavior.The data is analyzed using machine learning algorithms to identify potential collision risks and recommend appropriate actions to avoid collisions.The system takes into account the heterogeneity of vehicles,such as their size,speed,and maneuverability,to optimize collision avoidance strategies.The proposed system is experimented with real-time datasets and compared with existing collision avoidance systems.The results are shown using the evaluation metrics that show the proposed system can significantly reduce the number of collisions and improve the overall safety and efficiency of IoV with an accuracy of 96.5%using the SVM algorithm.The trial outcomes demonstrated that the new system,incorporating vehicular,weather,and human behavior factors,outperformed previous systems that only considered vehicular and weather aspects.This innovative approach is poised to lead transportation efforts,reducing accident rates and improving the quality of transportation systems in smart cities.By offering predictive capabilities,the proposed model not only helps control accident rates but also prevents them in advance,ensuring road safety.
基金supported by the National Natural Science Foundation of China(62421004,U24A20279,62473243,62533004)。
文摘Dear Editor,This letter addresses the formation control problem for unmanned surface vehicles(USVs)under GPS-denied environments.A novel visual servo formation control scheme,utilizing a monocular camera on the follower to obtain the leader’s global position,is developed,which is also capable of guaranteeing collision avoidance and visibility maintenance(CA&VM)raised by the requirement of actual formation navigation.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)-Information Technology Research Center(ITRC)under Grant No.IITP-2025-RS-2023-00259004the Basic Science Research Program through the National Research Foundation of Korea(NRF)under Grant No.RS-2025-25434261.
文摘The intelligent transportation systems require secure,low-latency,and reliable communication architectures to enable the real-time vehicular application.This paper proposes an edge-intelligent semantic aggregation(EISA)framework for 6G unmanned aerial vehicle(UAV)-assisted Internet of vehicles(IoV)networks that integrates task-driven semantic communication,deep reinforcement learning(DRL)-based edge intelligence,and blockchain-based semantic validation across 6G terahertz(THz)links.UAVs in the proposed architecture serve as adaptive edge nodes that receive semantically vital information about the vehicle at any given stage,optimize aggregation and transmission parameters dynamically,and guarantee data integrity through a structured,lightweight consortium blockchain that signs semantically detailed representations rather than raw packets.Simulation results from a hybrid NS-3,MATLAB,and Python environment indicate that the proposed framework can achieve up to 45%reduction in end-to-end latency,an approximately 70%increase in throughput,and semantic efficiency with blockchain verification delays of less than 20 ms(more than 98%).These findings support the effectiveness of the proposed co-design for achieving context-aware,energy-efficient,and reliable communication under heavy-traffic conditions.The proposed framework provides a flexible and scalable foundation for next-generation 6G-enabled automotive networks,with subsequent growth toward federated learning-based collaborative intelligence,digital-twinassisted traffic modeling,and quantum-safe blockchain mechanisms to enhance scalability,intelligence,and long-term security.
基金carried out within the framework of the V3EA Project“Electric,Energy Efficient,and Autonomous Vehicle”(2021-2025)supported by the Research National Agency(ANR)of the French Government。
文摘Efficient torque allocation in over-actuated vehicles poses a central challenge in the domain of advanced vehicle control.These vehicles,featuring redundant actuators,provide an exceptional avenue for enhancing performance,stability,and efficiency.This paper presents a pioneering tendency for torque allocation in the context of over-actuated vehicles,particularly inwheel motor(IWM)driven electric vehicles.We introduce a systematic methodology grounded in analytical modeling,allowing for the efficient reconciliation of multiple,often conflicting objectives.The explicit functions are analytically modeled to enhance stability and energy economy.Additionally,a fuzzy logic-based torque allocation strategy is developed and compared,along with other literature methods,with the analytical models.Simulations are conducted in a joint simulation between Simulink/MATLAB and SCANeR Studio vehicle dynamics simulator,followed by validation on a real-world dataset.Our findings elucidate the proficiency of the analytical models on vehicle performance,stability,computational efficiency,and energy consumption.
基金funded in part by the Supported by Natural Science Foundation of Inner Mongolia Autonomous Region of China under Grants 2024QN06022 and 2023QN06008in part by the First-Class Discipline Research Special Project under Grant YLXKZX-NGD-015in part by the Inner Mongolia University of Technology Scientific Research Start-Up Project under Grant BS2024067.
文摘Blockchain offers a promising solution to the security challenges faced by the Internet of Vehicles(IoV).However,due to the dynamic connectivity of IoV,blockchain based on a single-chain structure or Directed Acyclic Graph(DAG)structure often suffer from performance limitations.The DAG lattice structure is a novel blockchain model in which each node maintains its own account chain,and only the node itself is allowed to update it.This feature makes the DAG lattice structure particularly suitable for addressing the challenges in dynamically connected IoV environment.In this paper,we propose a blockchain architecture based on the DAG lattice structure,specifically designed for dynamically connected IoV.In the proposed system,nodes must obtain authorization from a trusted authority before joining,forming a permissioned blockchain.Each node is assigned an individual account chain,allowing vehicles with limited storage capacity to participate in the blockchain by storing transactions only from nearby vehicles’account chains.Every transmitted message is treated as a transaction and added to the blockchain,enablingmore efficient data transmission in a dynamic network environment.Areputation-based incentivemechanism is introduced to encourage nodes to behave normally.Experimental results demonstrate that the proposed architecture achieves better performance compared with traditional single-chain and DAG-based approaches in terms of average transmission delay and storage cost.
文摘The Internet of Vehicles,or IoV,is expected to lessen pollution,ease traffic,and increase road safety.IoV entities’interconnectedness,however,raises the possibility of cyberattacks,which can have detrimental effects.IoV systems typically send massive volumes of raw data to central servers,which may raise privacy issues.Additionally,model training on IoV devices with limited resources normally leads to slower training times and reduced service quality.We discuss a privacy-preserving Federated Split Learning with Tiny Machine Learning(TinyML)approach,which operates on IoV edge devices without sharing sensitive raw data.Specifically,we focus on integrating split learning(SL)with federated learning(FL)and TinyML models.FL is a decentralisedmachine learning(ML)technique that enables numerous edge devices to train a standard model while retaining data locally collectively.The article intends to thoroughly discuss the architecture and challenges associated with the increasing prevalence of SL in the IoV domain,coupled with FL and TinyML.The approach starts with the IoV learning framework,which includes edge computing,FL,SL,and TinyML,and then proceeds to discuss how these technologies might be integrated.We elucidate the comprehensive operational principles of Federated and split learning by examining and addressingmany challenges.We subsequently examine the integration of SL with FL and various applications of TinyML.Finally,exploring the potential integration of FL and SL with TinyML in the IoV domain is referred to as FSL-TM.It is a superior method for preserving privacy as it conducts model training on individual devices or edge nodes,thereby obviating the necessity for centralised data aggregation,which presents considerable privacy threats.The insights provided aim to help both researchers and practitioners understand the complicated terrain of FL and SL,hence facilitating advancement in this swiftly progressing domain.
基金supported by Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.
基金Supported by Xinjiang"Tianshan Talents"Program Project"Research and Demonstration of Key Technologies for Precise Monitoring and Pesticide Application by Unmanned Aerial Vehicle during the Cotton Topping Stage"(2023TSYCCX0126)Xinjiang Production and Construction Corps Science and Technology Innovation Project"Innovation Team Project for Intelligent Information Collection and Smart Management in Cotton Fields"(NCG202304).
文摘[Objectives]To determine the optimal concentration of topping agents applied by unmanned aerial vehicles(UAVs)to effectively regulate cotton growth and improve production efficiency.[Methods]A field experiment was conducted in Shihezi City,Xinjiang,employing a randomized block design.Five UAV-based chemical topping treatments were applied at dosages of 0.300,0.525,0.750,0.975,and 1.200 L/hm 2,designated as H1,H2,H3,H4,and H5,respectively.Additionally,manual topping(CK1)and tractor topping(CK2)treatments,both at a concentration of 0.750 L/hm 2,were included as control treatments.During the first 20 d following topping,parameters including primary agronomic traits of cotton(plant height,leaf age,number of fruit branches),dry matter accumulation and distribution,leaf area boll load(LAB),root-to-shoot ratio(RSR),leaf mass area(LMA),and leaf area index(LAI)were examined.At harvest,yield components,lint cotton yield,harvest index,and fiber quality were evaluated.[Results]Twenty days after topping,the concentration of the topping agent applied via UAV did not significantly affect cotton leaf age or the number of fruit branches.Additionally,no significant differences in plant height were observed among the five concentration treatments compared to CK2.However,plants treated with H1 exhibited significantly greater height compared to those treated with H5 and CK1,indicating that H1 was the least effective in controlling vegetative growth.Total dry matter accumulation(TDM),boll dry matter accumulation(BDM),LAB,and LMA all demonstrated an initial increase followed by a decrease as the spraying concentration increased.The highest TDM and reproductive organ dry matter ratio(RRDM)were observed in the H3 treatment.No significant differences were found among treatments for LMA,RSR,or LAI;however,LAB and single boll weight were greatest in the H3 treatment.Fiber quality parameters,including fiber length uniformity,micronaire(MIC),specific strength,and fiber maturity,initially increased and then decreased with increasing spraying concentration,whereas fiber elongation rate exhibited the opposite trend.The H3 treatment yielded the highest average fiber length uniformity and specific strength.[Conclusions]At optimal spraying concentrations,UAV-based application more effectively controls vegetative growth,promotes dry matter accumulation and distribution in cotton bolls,increases single boll weight,and enhances the MIC,specific strength,and fiber elongation rate of cotton fibers compared to manual and tractor spraying of topping agents.In summary,the use of UAVs for spraying chemical topping agents is recommended,with a suggested dosage range of 0.750 and 0.975 L/hm 2.
基金supported by the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(C)23K03898.
文摘Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles(CAVs)at single-lane intersections,particularly in the context of left-hand side driving on roads.The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks.We consider that all approaching vehicles share relevant information through vehicular communications.The Intersection Coordination Unit(ICU)processes this information and communicates the optimal crossing or turning times to the vehicles.The primary objective of this coordination is to minimize overall traffic delays,which also helps improve the fuel consumption of vehicles.By considering information from upcoming vehicles at the intersection,the coordination system solves an optimization problem to determine the best timing for executing right turns,ultimately minimizing the total delay for all vehicles.The proposed coordination system is evaluated at a typical urban intersection,and its performance is compared to traditional traffic systems.Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.
基金the Hebei Province Science and Technology Plan Project(19221909D)rincess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R308),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based car-following(CF)framework employing the Deep Deterministic Policy Gradient(DDPG)algorithm,which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning.Utilizing real-world driving data from the highD dataset,the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios.The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control(MPC-ACC)controller.Results show that theDRLmodel significantly enhances safety,achieving zero collisions and a higher average time-to-collision(TTC)of 8.45 s,compared to 5.67 s for MPC and 6.12 s for human drivers.For efficiency,the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases.In terms of energy optimization,the proposed approach reduces fuel consumption by 5.4% relative to MPC.Additionally,it enhances passenger comfort by lowering jerk values by 65%,achieving 0.12 m/s3 vs.0.34 m/s3 for human drivers.A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics.Moreover,the findings underscore the potential of DRL in advancing autonomous vehicle control,offering a robust and sustainable solution for safer,more efficient,and more comfortable transportation systems.
文摘This review sums up existing information on the environmental issues of critical materials in new energy vehicles(NEVs)on a combined mining to mobility approach.With the increase in the rate of NEV adoption,the environmental cost of operating vehicles will decline as the burden moves to upstream and downstream material life-cycle activities,such as extraction,beneficiation,refining,component manufacturing,use-phase performance,and end-of-life management.We focus on key material categories that provide electrified mobility,such as battery-related material(e.g.,Li,Ni,Co,Mn,graphite),high-performance motor-related material(e.g.,rare earth elements),conductive and lightweighting material(e.g.,Cu and Al).In the supply chain,the prevailing environmental forces consist of high energy requirements and related greenhouse gas emissions,excessive water consumption and water pollution risks,toxicity and human health issues pertaining to chemical inputs and metal discharges,land-use shift,and ecosystem and biodiversity effects.The review notes that there is high regional heterogeneity,which is fueled by ore grades,processing technologies,electricity mixes,and governance capacity,and that when measurements are narrowed to carbon measures,there is a risk of shifting the problem across geographies and categories of impacts.Mitigation pathways are analyzed,such as cleaner extraction and refining,material substitution and dematerialization,battery design,longevity and recyclability,and also the strategies of the circular economy,such as recycling and second-life use.Lastly,we establish research gaps in important areas of supply-chain data disclosure,multi-impact life-cycle assessment approaches,and integrated environmental-social analysis to enable sound policy formulation that can be used to achieve sustainable electrified mobility.
基金supported by the National Natural Science Foundation of China(Grant No.62172292).
文摘Anomaly detection is a vibrant research direction in controller area networks,which provides the fundamental real-time data transmission underpinning in-vehicle data interaction for the internet of vehicles.However,existing unsupervised learning methods suffer from insufficient temporal and spatial constraints on shallow features,resulting in fragmented feature representations that compromise model stability and accuracy.To improve the extraction of valuable features,this paper investigates the influence of clustering constraints on shallow feature convergence paths at the model level and further proposes an end-to-end intrusion detection system based on efficient deep embedded subspace clustering(EDESC-IDS).Following the standard learning approach,continuous messages are encoded into two-dimensional data frames via a frame builder,which are then input into an extended convolutional autoencoder for extracting shallow features from high-dimensional data.On this basis,the dual constraints of these output features and the embedding clustering module facilitate end-to-end training of the EDESC-IDS in various attack scenarios.Extensive experimental results show that such a system exhibits significant detection performance on four types of attack datasets,including DoS,Gear,Fuzzy,and RPM,with precision,recall,and F1 scores consistently above 97.79%,while maintaining a false negative rate(FNR)and an error rate(ER)below 2.22%.
文摘To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the concept of particle sight distance,an improved algorithm,called SD-IPSO,is proposed for the real-time autonomous navigation of USVs in marine environments.The algorithm refines the individual behavior pattern of particles in the population,effectively improving both local and global search capabilities while avoiding premature convergence.The effectiveness of the algorithm is validated using standard test functions from CEC-2017 function library,assessing it from multiple dimensions.Sensitivity analysis is conducted on key parameters in the algorithm,including particle sight distance and population size.Results indicate that compared with PSO,SD-IPSO demonstrates significant advantages in optimization accuracy and convergence speed.The application of SD-IPSO in path planning is further investigated through a 14-point traveling salesman problem(TSP)example and navigation autonomous tests of USVs in marine environments.Findings demonstrate that the proposed algorithm exhibits superior optimization capabilities and can effectively address the path planning challenges of USVs.
基金supported by the Korea Institute of Police Technology(No.:RS-2024-00405603).
文摘Autonomous vehicles operate without direct human intervention,which introduces safety risks that differ from those of conventional vehicles.Although many studies have examined safety issues related to autonomous driving,high-risk situations have often been defined using single indicators,making it difficult to capture the complex and evolving nature of accident risk.To address this limitation,this study proposes a structured framework for defining and analyzing high-risk situations throughout the traffic accident process.High-risk situations are described using three complementary indicators:accident likelihood,accident severity,and accident duration.These indicators explain how risk emerges,increases,and persists over time.Based on this concept,a framework for traffic accident visualization analysis is developed to support phase-specific risk assessment and visualization.The framework combines accident-phase information with factor-level risk contributions,allowing systematic identification of key factors and their interactions across different accident stages.Using combinations of the three indicators,high-risk situations are classified into twenty-seven distinct types,providing a clear typology for complex accident scenarios involving autonomous vehicles.The applicability of the proposed framework is demonstrated through two representative accident scenarioswith different risk characteristics.The results showthat the framework effectively captures interactions among multiple risk factors,explains how risk levels change from pre-crash to post-crash phases,and identifies contributing factors that are difficult to detect using conventional traffic accident investigation methods.Overall,the proposed framework offers a practical basis for autonomous vehicle accident analysis,safety evaluation,and policy-related decision-making.
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(Grant No.52405257)the China Postdoctoral Science Foundation(Grant No.2024M764201).
文摘Hypersonic morphing vehicle(HMV)can reconfigure aerodynamic geometries in real time,adapting to diverse needs like multi-mission profiles and wide-speed-range flight,spanwise morphing and sweep angle variation are representative large-scale wing reconfiguration modes.To meet the HMV's need for an increased lift and a lift to drag ratio during hypersonic maneuverability and cruise or reentry equilibrium glide,this paper proposes an innovative single-DOF coupled morphing-wing system.We then systematically analyze its open-loop kinematics and closed-loop connectivity constraints,and the proposed system integrates three functional modules:the preset locking/release mechanism,the coupled morphing-wing mechanism,and the integrated wing locking with active stiffness control mechanism.Experimental validation confirms stable,continuous morphing under simulated aerodynamic loads.The experimental results indicate:(i)SMA actuators exhibit response times ranging from 18 s to 160 s,providing sufficient force output for wing unlocking;(ii)The integrated wing locking with active stiffness control mechanism effectively secures wing positions while eliminating airframe clearance via SMA actuation,improving the first-order natural frequency by more than 17%;(iii)The distributed aerodynamic loading system enables precise multi-stage follow-up loading during morphing,with the coupled morphing wing maintaining stable,continuous operation under 0-3500 N normal loads and 110-140 N axial force.The proposed single-DOF coupled morphing mechanism not only simplifies and improves structural efficiency but also demonstrates superior performance in locking control,stiffness enhancement,and aerodynamic responsiveness.This establishes a foundational framework for the design of future intelligent morphing configurations and the implementation of flight control systems.
基金Supported by the National Defense Basic Scientific Research Program of China.
文摘Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.
基金support of the National Key Research and Development Plan(Grant No.2021YFB3302501)the financial support of the National Science Foundation of China(Grant No.12161076)the financial support of the Fundamental Research Funds for the Central Universities(Grant No.DUT24LAB129).
文摘As carrier aircraft sortie frequency and flight deck operational density increase,autonomous dispatch trajectory planning for carrier-based vehicles demands efficient,safe,and kinematically feasible solutions.This paper presents an Iterative Safe Dispatch Corridor(iSDC)framework,addressing the suboptimality of the traditional SDC method caused by static corridor construction and redundant obstacle exploration.First,a Kinodynamic-Informed-Bidirectional Rapidly-exploring Random Tree Star(KIBRRT^(*))algorithm is proposed for the front-end coarse planning.By integrating bidirectional tree expansion,goal-biased elliptical sampling,and artificial potential field guidance,it reduces unnecessary exploration near concave obstacles and generates kinematically admissible paths.Secondly,the traditional SDC is implemented in an iterative manner,and the obtained trajectory in the current iteration is fed into the next iteration for corridor generation,thus progressively improving the quality of withincorridor constraints.For tractors,a reverse-motion penalty function is incorporated into the back-end optimizer to prioritize forward driving,aligning with mechanical constraints and human operational preferences.Numerical validations on the data of Gerald R.Ford-class carrier demonstrate that the KIBRRT^(*)reduces average computational time by 75%and expansion nodes by 25%compared to conventional RRT^(*)algorithms.Meanwhile,the iSDC framework yields more time-efficient trajectories for both carrier aircraft and tractors,with the dispatch time reduced by 31.3%and tractor reverse motion proportion decreased by 23.4%relative to traditional SDC.The presented framework offers a scalable solution for autonomous dispatch in confined and safety-critical environment,and an illustrative animation is available at bilibili.com/video/BV1tZ7Zz6Eyz.Moreover,the framework can be easily extended to three-dimension scenarios,and thus applicable for trajectory planning of aerial and underwater vehicles.
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 by the Scientific Research Innovation Capability Support Project for Young Faculty,China(No.ZYGXQNJSKYCXNLZCXM-D1)the National Natural Science Foundation of China(No.52272384).
文摘Flapping-Wing Micro Air Vehicles(FMAVs)are compact and agile,capable of accessing narrow spaces that conventional vehicles struggle to reach,such as ruins,caves,or the interiors of complex structures,making them ideal tools for reconnaissance and rescue missions.1 However,the operation of FMAVs relies on coordinating multiple forces with different scaling effects,posing challenges to miniaturization design.
