Ammonia(NH3)has been widely recognized as a key precursor of atmospheric secondary aerosol formation.Vehicle emission is a major source of urban atmospheric NH3.With the tightening of emission standards and the growin...Ammonia(NH3)has been widely recognized as a key precursor of atmospheric secondary aerosol formation.Vehicle emission is a major source of urban atmospheric NH3.With the tightening of emission standards and the growing trend of vehicle fleet electrification,it is imperative to update the emission factors for NH3 from real-world on-road fleets.In this study,a tunnel measurement was conducted in the urban area of Tianjin,China.The fleet-average NH3 emission factor(EF)was 11.2 mg/(km·veh),significantly lower than those in previous studies,showing the benefit of emission standard updating.Through a multiple linear regression analysis,the EFs of light-duty gasoline vehicles,light-duty diesel vehicles,and heavy-duty diesel vehicles(HDDVs)were estimated to be 5.7±0.6 mg/(km·veh),40.8±5.1 mg/(km·veh),and 160.2±16.6 mg/(km·veh),respectively.Based on the results from this study,we found that HDDVs,which comprise<3%of the total vehicles may contribute approximately 22%of total NH3 emissions in Tianjin.Our results highlight NH3 emissions from HDDVs,a previously potentially overlooked source of NH3 emissions in urban areas.The actual on-road NH3 emissions from HDDVs may exceed current expectations,posing a growing concern for the future.展开更多
With the rapid proliferation of electric vehicles,their charging loads pose new challenges to power grid stability and operational efficiency.To address this,this study employs a Monte Carlo simulation model to analyz...With the rapid proliferation of electric vehicles,their charging loads pose new challenges to power grid stability and operational efficiency.To address this,this study employs a Monte Carlo simulation model to analyze the charging load characteristics of six battery electric vehicle categories in Hebei Province,leveraging multi-source probabilistic distribution data under typical operational scenarios.The findings reveal that electric vehicle charging loads are primarily concentrated during midday and nighttime periods,with significant load fluctuations exerting substantial pressure on the grid.In response,this paper proposes strategic interventions including optimized charging infrastructure planning,time-of-use electricity pricing mechanisms,and smart charging technologies to balance grid loads.The results provide a theoretical foundation for electric vehicle load forecasting,smart grid dispatching,and vehicle-grid integration,thereby enhancing grid operational efficiency and sustainability.展开更多
To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobje...To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobjective optimization.First,a dual-convolution enhanced improved Crossformer prediction model is constructed,which employs parallel 1×1 global and 3×3 local convolutionmodules(Integrated Convolution Block,ICB)formultiscale feature extraction,combinedwith anAdaptive Spectral Block(ASB)to enhance time-series fluctuationmodeling.Based on high-precision predictions,a carbon-electricity cost joint optimization model is further designed to balance economic,environmental,and grid-friendly objectives.The model’s superiority was validated through a case study using real-world data from a renewable-heavy grid.Simulation results show that the proposed multi-objective strategy demonstrated a superior balance compared to baseline and benchmark models,achieving a 15.8%reduction in carbon emissions and a 5.2%reduction in economic costs,while still providing a substantial 22.2%reduction in the peak-valley difference.Its balanced performance significantly outperformed both a single-objective strategy and a state-of-the-art Model Predictive Control(MPC)benchmark,highlighting the advantage of a global optimization approach.This study provides theoretical and technical pathways for dynamic carbon factor-driven EV charging optimization.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
As urban landscapes evolve and vehicular volumes soar,traditional traffic monitoring systems struggle to scale,often failing under the complexities of dense,dynamic,and occluded environments.This paper introduces a no...As urban landscapes evolve and vehicular volumes soar,traditional traffic monitoring systems struggle to scale,often failing under the complexities of dense,dynamic,and occluded environments.This paper introduces a novel,unified deep learning framework for vehicle detection,tracking,counting,and classification in aerial imagery designed explicitly for modern smart city infrastructure demands.Our approach begins with adaptive histogram equalization to optimize aerial image clarity,followed by a cutting-edge scene parsing technique using Mask2Former,enabling robust segmentation even in visually congested settings.Vehicle detection leverages the latest YOLOv11 architecture,delivering superior accuracy in aerial contexts by addressing occlusion,scale variance,and fine-grained object differentiation.We incorporate the highly efficient ByteTrack algorithm for tracking,enabling seamless identity preservation across frames.Vehicle counting is achieved through an unsupervised DBSCAN-based method,ensuring adaptability to varying traffic densities.We further introduce a hybrid feature extraction module combining Convolutional Neural Networks(CNNs)with Zernike Moments,capturing both deep semantic and geometric signatures of vehicles.The final classification is powered by NASNet,a neural architecture search-optimized model,ensuring high accuracy across diverse vehicle types and orientations.Extensive evaluations of the VAID benchmark dataset demonstrate the system’s outstanding performance,achieving 96%detection,94%tracking,and 96.4%classification accuracy.On the UAVDT dataset,the system attains 95%detection,93%tracking,and 95%classification accuracy,confirming its robustness across diverse aerial traffic scenarios.These results establish new benchmarks in aerial traffic analysis and validate the framework’s scalability,making it a powerful and adaptable solution for next-generation intelligent transportation systems and urban surveillance.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
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
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
针对复杂水下环境中水下自主航行器(autonomous underwater vehicle,AUV)路径规划问题,提出一种改进启发式快速随机扩展树(rapidly-exploring random trees,RRT)的路径规划算法。针对路径点采样过程中缺乏目标导向性的问题,采用目标点...针对复杂水下环境中水下自主航行器(autonomous underwater vehicle,AUV)路径规划问题,提出一种改进启发式快速随机扩展树(rapidly-exploring random trees,RRT)的路径规划算法。针对路径点采样过程中缺乏目标导向性的问题,采用目标点概率偏置采样策略与目标偏向扩展策略,可使目标节点在随机采样时成为采样点。在路径点扩展过程中,使非目标采样点的扩展结点位置偏向于目标点的方向,从而增强算法在随机采样与扩展过程中的目标搜索能力。为解决水下路径规划过程中存在过多无效搜索空间的问题,在随机采样过程中引入启发式采样策略,构建包含所有初始路径的采样空间子集,减小采样空间范围,从而提高算法的空间搜索效率。针对AUV在水下环境中抗洋流扰动能力不足的问题,采用速度矢量合成法,使AUV速度矢量与洋流速度矢量合成后指向期望路径的方向,从而抵消水流的影响。在山峰地形中叠加多个Lamb涡流模拟水下流场环境,进行多次仿真实验。实验结果表明:改进启发式RRT算法解决了采样过程中随机性问题,显著缩小了搜索空间,兼顾了路径的安全性与平滑性,并使AUV具有良好的抗洋流扰动能力。展开更多
针对多自主水下航行器(Autonomous Underwater Vehicle,AUV)的全覆盖路径规划问题,提出了一种考虑随机初始位置约束的多AUV覆盖路径规划方法(Dividing Areas based on Robots Initial Positions CPP,DARIP-CPP)。根据多自主水下机器人...针对多自主水下航行器(Autonomous Underwater Vehicle,AUV)的全覆盖路径规划问题,提出了一种考虑随机初始位置约束的多AUV覆盖路径规划方法(Dividing Areas based on Robots Initial Positions CPP,DARIP-CPP)。根据多自主水下机器人的随机初始位置对工作海域进行均衡区域划分,将划分所得的不重叠区域分配给多AUV进行独立覆盖路径规划,每台AUV利用生物启发神经网络(Bio-inspired Neural Network)优化各个区域的全覆盖路径。为了克服传统全覆盖路径规划中的“死区”问题,采用A^(*)路径规划算法进行“死区”逃离,沿着较短的路径快速到达未覆盖区域点。仿真结果表明,所提出的DARIPCPP方法可有效提高多AUV全覆盖目标区域的工作效率。展开更多
为解决基于深度强化学习的AUV跟踪控制器在面临新任务时需从零开始训练、训练速度慢、稳定性差等问题,设计一种基于元强化学习的AUV多任务快速自适应控制算法——R-SAC(Reptile-Soft Actor Critic)算法。R-SAC算法将元学习与强化学习相...为解决基于深度强化学习的AUV跟踪控制器在面临新任务时需从零开始训练、训练速度慢、稳定性差等问题,设计一种基于元强化学习的AUV多任务快速自适应控制算法——R-SAC(Reptile-Soft Actor Critic)算法。R-SAC算法将元学习与强化学习相结合,结合水下机器人运动学及动力学方程对跟踪任务进行建模,利用RSAC算法在训练阶段为AUV跟踪控制器获得一组最优初始值模型参数,使模型在面临不同的任务时,基于该组参数进行训练时能够快速收敛,实现快速自适应不同任务。仿真结果表明,所提出的方法与随机初始化强化学习控制器相比,收敛速度最低提高了1.6倍,跟踪误差保持在2.8%以内。展开更多
Autonomous underwater vehicles （AUVs） navigating on the sea surface are usually required to complete the communication tasks in complex sea conditions. The movement forms and flow field characteristics of a multi-mo...Autonomous underwater vehicles （AUVs） navigating on the sea surface are usually required to complete the communication tasks in complex sea conditions. The movement forms and flow field characteristics of a multi-moving state AUV navigating in head sea at high speed were studied. The mathematical model on longitudinal motion of the high-speed AUV in head sea was established with considering the hydrodynamic lift based on strip theory, which was solved to get the heave and pitch of the AUV by Gaussian elimination method. Based on this, computational fluid dynamics （CFD） method was used to establish the mathematical model of the unsteady viscous flow around the AUV with considering free surface effort by using the Reynolds-averaged Navier-Stokes （RANS） equations, shear-stress transport （SST） k-w model and volume of fluid （VOF） model. The three-dimensional numerical wave in the computational field was realized through defining the unsteady inlet boundary condition. The motion forms of the AUV navigating in head sea at high speed were carried out by the program source code of user-defined function （UDF） based on dynamic mesh. The hydrodynamic parameters of the AUV such as drag, lift, pitch torque, velocity, pressure and wave profile were got, which reflect well the real ambient flow field of the AUV navigating in head sea at high speed. The computational wave profile agrees well with the experimental phenomenon of a wave-piercing surface vehicle. The force law of the AUV under the impacts of waves was analyzed qualitatively and quantitatively, which provides an effective theoretical guidance and technical support for the dynamics research and shape design of the AUV in real complex environnaent.展开更多
基金supported by the National key research and development program of China(No.2022YFE0135000)the National Natural Science Foundation of China(No.42175123)the Natural Science Foundation of Tianjin(No.23JCJQJC00170).
文摘Ammonia(NH3)has been widely recognized as a key precursor of atmospheric secondary aerosol formation.Vehicle emission is a major source of urban atmospheric NH3.With the tightening of emission standards and the growing trend of vehicle fleet electrification,it is imperative to update the emission factors for NH3 from real-world on-road fleets.In this study,a tunnel measurement was conducted in the urban area of Tianjin,China.The fleet-average NH3 emission factor(EF)was 11.2 mg/(km·veh),significantly lower than those in previous studies,showing the benefit of emission standard updating.Through a multiple linear regression analysis,the EFs of light-duty gasoline vehicles,light-duty diesel vehicles,and heavy-duty diesel vehicles(HDDVs)were estimated to be 5.7±0.6 mg/(km·veh),40.8±5.1 mg/(km·veh),and 160.2±16.6 mg/(km·veh),respectively.Based on the results from this study,we found that HDDVs,which comprise<3%of the total vehicles may contribute approximately 22%of total NH3 emissions in Tianjin.Our results highlight NH3 emissions from HDDVs,a previously potentially overlooked source of NH3 emissions in urban areas.The actual on-road NH3 emissions from HDDVs may exceed current expectations,posing a growing concern for the future.
基金funded by Humanities and Social Sciences of Ministry of Education Planning Fund of China,grant number 21YJA790009National Natural Science Foundation of China,grant number 72140001.
文摘With the rapid proliferation of electric vehicles,their charging loads pose new challenges to power grid stability and operational efficiency.To address this,this study employs a Monte Carlo simulation model to analyze the charging load characteristics of six battery electric vehicle categories in Hebei Province,leveraging multi-source probabilistic distribution data under typical operational scenarios.The findings reveal that electric vehicle charging loads are primarily concentrated during midday and nighttime periods,with significant load fluctuations exerting substantial pressure on the grid.In response,this paper proposes strategic interventions including optimized charging infrastructure planning,time-of-use electricity pricing mechanisms,and smart charging technologies to balance grid loads.The results provide a theoretical foundation for electric vehicle load forecasting,smart grid dispatching,and vehicle-grid integration,thereby enhancing grid operational efficiency and sustainability.
基金Supported by State Grid Corporation of China Science and Technology Project:Research on Key Technologies for Intelligent Carbon Metrology in Vehicle-to-Grid Interaction(Project Number:B3018524000Q).
文摘To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobjective optimization.First,a dual-convolution enhanced improved Crossformer prediction model is constructed,which employs parallel 1×1 global and 3×3 local convolutionmodules(Integrated Convolution Block,ICB)formultiscale feature extraction,combinedwith anAdaptive Spectral Block(ASB)to enhance time-series fluctuationmodeling.Based on high-precision predictions,a carbon-electricity cost joint optimization model is further designed to balance economic,environmental,and grid-friendly objectives.The model’s superiority was validated through a case study using real-world data from a renewable-heavy grid.Simulation results show that the proposed multi-objective strategy demonstrated a superior balance compared to baseline and benchmark models,achieving a 15.8%reduction in carbon emissions and a 5.2%reduction in economic costs,while still providing a substantial 22.2%reduction in the peak-valley difference.Its balanced performance significantly outperformed both a single-objective strategy and a state-of-the-art Model Predictive Control(MPC)benchmark,highlighting the advantage of a global optimization approach.This study provides theoretical and technical pathways for dynamic carbon factor-driven EV charging optimization.
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funded by the Open Access Initiative of the University of Bremen and the DFG via SuUB BremenThe authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Group Project under grant number(RGP2/367/46)+1 种基金This research is supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘As urban landscapes evolve and vehicular volumes soar,traditional traffic monitoring systems struggle to scale,often failing under the complexities of dense,dynamic,and occluded environments.This paper introduces a novel,unified deep learning framework for vehicle detection,tracking,counting,and classification in aerial imagery designed explicitly for modern smart city infrastructure demands.Our approach begins with adaptive histogram equalization to optimize aerial image clarity,followed by a cutting-edge scene parsing technique using Mask2Former,enabling robust segmentation even in visually congested settings.Vehicle detection leverages the latest YOLOv11 architecture,delivering superior accuracy in aerial contexts by addressing occlusion,scale variance,and fine-grained object differentiation.We incorporate the highly efficient ByteTrack algorithm for tracking,enabling seamless identity preservation across frames.Vehicle counting is achieved through an unsupervised DBSCAN-based method,ensuring adaptability to varying traffic densities.We further introduce a hybrid feature extraction module combining Convolutional Neural Networks(CNNs)with Zernike Moments,capturing both deep semantic and geometric signatures of vehicles.The final classification is powered by NASNet,a neural architecture search-optimized model,ensuring high accuracy across diverse vehicle types and orientations.Extensive evaluations of the VAID benchmark dataset demonstrate the system’s outstanding performance,achieving 96%detection,94%tracking,and 96.4%classification accuracy.On the UAVDT dataset,the system attains 95%detection,93%tracking,and 95%classification accuracy,confirming its robustness across diverse aerial traffic scenarios.These results establish new benchmarks in aerial traffic analysis and validate the framework’s scalability,making it a powerful and adaptable solution for next-generation intelligent transportation systems and urban surveillance.
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 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.
文摘针对复杂水下环境中水下自主航行器(autonomous underwater vehicle,AUV)路径规划问题,提出一种改进启发式快速随机扩展树(rapidly-exploring random trees,RRT)的路径规划算法。针对路径点采样过程中缺乏目标导向性的问题,采用目标点概率偏置采样策略与目标偏向扩展策略,可使目标节点在随机采样时成为采样点。在路径点扩展过程中,使非目标采样点的扩展结点位置偏向于目标点的方向,从而增强算法在随机采样与扩展过程中的目标搜索能力。为解决水下路径规划过程中存在过多无效搜索空间的问题,在随机采样过程中引入启发式采样策略,构建包含所有初始路径的采样空间子集,减小采样空间范围,从而提高算法的空间搜索效率。针对AUV在水下环境中抗洋流扰动能力不足的问题,采用速度矢量合成法,使AUV速度矢量与洋流速度矢量合成后指向期望路径的方向,从而抵消水流的影响。在山峰地形中叠加多个Lamb涡流模拟水下流场环境,进行多次仿真实验。实验结果表明:改进启发式RRT算法解决了采样过程中随机性问题,显著缩小了搜索空间,兼顾了路径的安全性与平滑性,并使AUV具有良好的抗洋流扰动能力。
文摘针对多自主水下航行器(Autonomous Underwater Vehicle,AUV)的全覆盖路径规划问题,提出了一种考虑随机初始位置约束的多AUV覆盖路径规划方法(Dividing Areas based on Robots Initial Positions CPP,DARIP-CPP)。根据多自主水下机器人的随机初始位置对工作海域进行均衡区域划分,将划分所得的不重叠区域分配给多AUV进行独立覆盖路径规划,每台AUV利用生物启发神经网络(Bio-inspired Neural Network)优化各个区域的全覆盖路径。为了克服传统全覆盖路径规划中的“死区”问题,采用A^(*)路径规划算法进行“死区”逃离,沿着较短的路径快速到达未覆盖区域点。仿真结果表明,所提出的DARIPCPP方法可有效提高多AUV全覆盖目标区域的工作效率。
文摘为解决基于深度强化学习的AUV跟踪控制器在面临新任务时需从零开始训练、训练速度慢、稳定性差等问题,设计一种基于元强化学习的AUV多任务快速自适应控制算法——R-SAC(Reptile-Soft Actor Critic)算法。R-SAC算法将元学习与强化学习相结合,结合水下机器人运动学及动力学方程对跟踪任务进行建模,利用RSAC算法在训练阶段为AUV跟踪控制器获得一组最优初始值模型参数,使模型在面临不同的任务时,基于该组参数进行训练时能够快速收敛,实现快速自适应不同任务。仿真结果表明,所提出的方法与随机初始化强化学习控制器相比,收敛速度最低提高了1.6倍,跟踪误差保持在2.8%以内。
基金Project(2006AA09Z235)supported by the National High Technology Research and Development Program of ChinaProject(CX2009B003)supported by Hunan Provincial Innovation Foundation For Postgraduate,China
文摘Autonomous underwater vehicles （AUVs） navigating on the sea surface are usually required to complete the communication tasks in complex sea conditions. The movement forms and flow field characteristics of a multi-moving state AUV navigating in head sea at high speed were studied. The mathematical model on longitudinal motion of the high-speed AUV in head sea was established with considering the hydrodynamic lift based on strip theory, which was solved to get the heave and pitch of the AUV by Gaussian elimination method. Based on this, computational fluid dynamics （CFD） method was used to establish the mathematical model of the unsteady viscous flow around the AUV with considering free surface effort by using the Reynolds-averaged Navier-Stokes （RANS） equations, shear-stress transport （SST） k-w model and volume of fluid （VOF） model. The three-dimensional numerical wave in the computational field was realized through defining the unsteady inlet boundary condition. The motion forms of the AUV navigating in head sea at high speed were carried out by the program source code of user-defined function （UDF） based on dynamic mesh. The hydrodynamic parameters of the AUV such as drag, lift, pitch torque, velocity, pressure and wave profile were got, which reflect well the real ambient flow field of the AUV navigating in head sea at high speed. The computational wave profile agrees well with the experimental phenomenon of a wave-piercing surface vehicle. The force law of the AUV under the impacts of waves was analyzed qualitatively and quantitatively, which provides an effective theoretical guidance and technical support for the dynamics research and shape design of the AUV in real complex environnaent.
