To maintain air quality during the 31st World University Games,Chengdu employed a range of monitoring and control strategies in 2023.High-resolution regional pollutant distributions were reconstructed by integrating t...To maintain air quality during the 31st World University Games,Chengdu employed a range of monitoring and control strategies in 2023.High-resolution regional pollutant distributions were reconstructed by integrating the vertical column densities(VCDs)from mobile MAX-DOAS measurements with Gaussian process regression analysis.The correlation between the spatial distribution derived from MAX-DOAS and that of GEMS and TROPOMI satellite data exceeded 0.6.This paper explores the impact of air quality improvements during the games on the sources of HCHO and the formation process of HONO.During the control period,primary emissions and secondary formations of HCHO contributed 50.85%±24.24%and 42.81%±7.57%to the total atmospheric HCHO,respectively.The study indicates that with improved air quality,HCHO primary emissions decrease while secondary emissions and atmospheric radiation transmission intensities rise.It is found that HONO always appears under the condition of high aerosol optical depth(AOD)and NO_(2),but high NO_(2) concentration and AOD are not necessarily accompanied by high concentrations of HONO.To assess the influence of temperature and humidity on the formation of HONO from NO_(2),we calculated the emission ratesΔHONO∕ΔNO_(2) to quantify the impact of primary sources on total HONO concentrations.The analysis results show that the turning point of relative humidity is 65%(60%–70%)and the turning point of temperature is 31℃(30–32℃).Lower temperatures and higher humidity levels were found to decrease the rate of secondary HONO formation from NO_(2).展开更多
1 Introduction The growing connectivity with mobile internet has significantly enhanced our day-to-day life support through various services and applications with on-demand availability at any time or anywhere.As emer...1 Introduction The growing connectivity with mobile internet has significantly enhanced our day-to-day life support through various services and applications with on-demand availability at any time or anywhere.As emerging technologies with continuous revolutions in the digital transformations,various add-on technologies such as quantum computing,AI,and next-generation networks such as 6G are becoming an integral support to mobile internet systems.The emerging technologies in the next-generation mobile internet bring a lot of new security and privacy challenges.展开更多
At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown ...At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments,this paper proposes an Attention-Enhanced Dueling Deep Q-Network(ADDueling DQN),which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework.A multi-objective reward function,centered on energy efficiency,is designed to comprehensively consider path length,terrain slope,motion smoothness,and obstacle avoidance,enabling optimal low-energy trajectory generation in 3D space from the source.The incorporation of a multihead attention mechanism allows the model to dynamically focus on energy-critical state features—such as slope gradients and obstacle density—thereby significantly improving its ability to recognize and avoid energy-intensive paths.Additionally,the prioritized experience replay mechanism accelerates learning from key decision-making experiences,suppressing inefficient exploration and guiding the policy toward low-energy solutions more rapidly.The effectiveness of the proposed path planning algorithm is validated through simulation experiments conducted in multiple off-road scenarios.Results demonstrate that AD-Dueling DQN consistently achieves the lowest average energy consumption across all tested environments.Moreover,the proposed method exhibits faster convergence and greater training stability compared to baseline algorithms,highlighting its global optimization capability under energy-aware objectives in complex terrains.This study offers an efficient and scalable intelligent control strategy for the development of energy-conscious autonomous navigation systems.展开更多
Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This stu...Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This study presents a cognitive control strategy that integrates a Numerical Feedforward Inverse Dynamic Controller(NFIDC)with a Feedback Radial Basis Function Neural Network(FRBFNN).The robot’s mechanical structure was designed in SolidWorks 2022 SP2.0 and validated under operational loads using finite element analysis in ANSYS 2022 R1.The NFIDC-FRBFNN framework merges proactive inverse dynamic compensation with adaptive neural learning to achieve smooth torque responses and accurate motion control.A two-stage simulation evaluation was conducted.In the first stage,the controller was tested in a simulated hospital environment under both ideal and non-ideal conditions.In the second,it was benchmarked against four established controllers-Neural Network Model Reference Adaptive(NNMRA),Z-number Fuzzy Logic(Z-FL),Adaptive Dynamic Controller(ADC),and Fuzzy Logic-PID(FL-PID)—using circular and lemniscate trajectories.Across ten runs,the proposed controller achieved the lowest tracking errors under all conditions.Under ideal conditions,it achieved average improvements of 55.24%,75.75%,and 55.20%in integral absolute error(IAE),integral squared error(ISE),and mean absolute error(MAE),respectively,with coefficient of variation(CV)reductions above 55%.Under non-ideal conditions,average improvements exceeded 64%in IAE,77%in ISE,and 66%in MAE,while maintaining CV reductions above 57%.These results confirm that the NFIDC-FRBFNN controller offers superior accuracy,robustness,and consistency for real-time path tracking in healthcare robotics.展开更多
SQL Server Mobile为移动设备和嵌入式设备提供了一个压缩的数据库解决方案。通过使用Microsoft VisualStudio工具,开发者可以将SQL Server的数据库管理能力扩展到移动设备上。介绍了在基于WINCE的移动设备上开发SQL Server Mobile数据...SQL Server Mobile为移动设备和嵌入式设备提供了一个压缩的数据库解决方案。通过使用Microsoft VisualStudio工具,开发者可以将SQL Server的数据库管理能力扩展到移动设备上。介绍了在基于WINCE的移动设备上开发SQL Server Mobile数据库的方法。并结合多媒体词典数据库的开发,详细分析了在系统开发中涉及的几项关键技术,对于嵌入式移动数据库的开发具有一定的参考价值。展开更多
基于移动终端的物流管理系统具备信息实时更新、在线查询、物资实时订阅等功能.其核心是移动终端的数据库访问技术,系统中提出的移动物流方案采用微软SQL Server Mobile 2005数据库,利用合并复制、RDA(远程数据访问)等技术建立远程访问...基于移动终端的物流管理系统具备信息实时更新、在线查询、物资实时订阅等功能.其核心是移动终端的数据库访问技术,系统中提出的移动物流方案采用微软SQL Server Mobile 2005数据库,利用合并复制、RDA(远程数据访问)等技术建立远程访问模型.采用Internet有线网络和GSM无线网络相结合,发挥移动终端的潜力,提高物流的实时性和管理人员的工作效率.展开更多
随着移动计算技术的发展,移动数据库已逐步走向应用,在嵌入式操作系统中移动数据库更显示出其优越性。本文分析了移动计算环境的几种模式、SQL Server Mobile移动数据库的体系结构,探讨了SQL Server Mobile移动数据库在应用中关键点,发...随着移动计算技术的发展,移动数据库已逐步走向应用,在嵌入式操作系统中移动数据库更显示出其优越性。本文分析了移动计算环境的几种模式、SQL Server Mobile移动数据库的体系结构,探讨了SQL Server Mobile移动数据库在应用中关键点,发展现状及应用前景。展开更多
In this paper,a robust tracking control scheme based on nonlinear disturbance observer is developed for the self-balancing mobile robot with external unknown disturbances.A desired velocity control law is firstly desi...In this paper,a robust tracking control scheme based on nonlinear disturbance observer is developed for the self-balancing mobile robot with external unknown disturbances.A desired velocity control law is firstly designed using the Lyapunov analysis method and the arctan function.To improve the tracking control performance,a nonlinear disturbance observer is developed to estimate the unknown disturbance of the self-balancing mobile robot.Using the output of the designed disturbance observer,the robust tracking control scheme is presented employing the sliding mode method for the selfbalancing mobile robot.Numerical simulation results further demonstrate the effectiveness of the proposed robust tracking control scheme for the self-balancing mobile robot subject to external unknown disturbances.展开更多
Current you only look once(YOLO)-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board(PCB)defect detection application scenario.In order t...Current you only look once(YOLO)-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board(PCB)defect detection application scenario.In order to solve this problem,we propose a new method,which combined the lightweight network mobile vision transformer(Mobile Vi T)with the convolutional block attention module(CBAM)mechanism and the new regression loss function.This method needed less computation resources,making it more suitable for embedded edge detection devices.Meanwhile,the new loss function improved the positioning accuracy of the bounding box and enhanced the robustness of the model.In addition,experiments on public datasets demonstrate that the improved model achieves an average accuracy of 87.9%across six typical defect detection tasks,while reducing computational costs by nearly 90%.It significantly reduces the model's computational requirements while maintaining accuracy,ensuring reliable performance for edge deployment.展开更多
Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important a...Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.展开更多
This paper proposes a multivariable fixed-time leaderfollower formation control method for a group of nonholonomic mobile robots, which has the ability to estimate multiple uncertainties. Firstly, based on the state s...This paper proposes a multivariable fixed-time leaderfollower formation control method for a group of nonholonomic mobile robots, which has the ability to estimate multiple uncertainties. Firstly, based on the state space model of the leader-follower formation, a multivariable fixed-time formation kinematics controller is designed. Secondly, to overcome uncertainties existing in the nonholonomic mobile robot system, such as load change,friction, external disturbance, a multivariable fixed-time torque controller based on the fixed-time disturbance observer at the dynamic level is designed. The designed torque controller is cascaded with the formation controller and finally realizes accurate estimation of the uncertain part of the system, the follower tracking of reference velocity and the desired formation of the leader and the follower in a fixed-time. The fixed-time upper bound is completely determined by the controller parameters, which is independent of the initial state of the system. The multivariable fixed-time control theory and the Lyapunov method are adopted to ensure the system stability.Finally, the effectiveness of the proposed algorithm is verified by the experimental simulation.展开更多
1.Introduction Mobile communications have catalyzed a new era of informa-tion technology revolution,significantly broadening and deepen-ing human-to-human,human-to-machine,and machine-to-machine connections.With their...1.Introduction Mobile communications have catalyzed a new era of informa-tion technology revolution,significantly broadening and deepen-ing human-to-human,human-to-machine,and machine-to-machine connections.With their incredible speed of development and wide-reaching impact,mobile communications serve as the cornerstone of the Internet of Everything,profoundly reshaping human cognitive abilities and ways of thinking.Furthermore,mobile communications are altering the patterns of production and life,driving leaps in productivity quality,and strongly promot-ing innovation within human civilization.展开更多
Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has b...Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has been constrained by high computational demands.Here,we developed GBiDC-PEST,a mobile application that incorporates an improved,lightweight detection algorithm based on the You Only Look Once(YOLO)series singlestage architecture,for real-time detection of four tiny pests(wheat mites,sugarcane aphids,wheat aphids,and rice planthoppers).GBiDC-PEST incorporates several innovative modules,including GhostNet for lightweight feature extraction and architecture optimization by reconstructing the backbone,the bi-directional feature pyramid network(BiFPN)for enhanced multiscale feature fusion,depthwise convolution(DWConv)layers to reduce computational load,and the convolutional block attention module(CBAM)to enable precise feature focus.The newly developed GBiDC-PEST was trained and validated using a multitarget agricultural tiny pest dataset(Tpest-3960)that covered various field environments.GBiDC-PEST(2.8 MB)significantly reduced the model size to only 20%of the original model size,offering a smaller size than the YOLO series(v5-v10),higher detection accuracy than YOLOv10n and v10s,and faster detection speed than v8s,v9c,v10m and v10b.In Android deployment experiments,GBiDCPEST demonstrated enhanced performance in detecting pests against complex backgrounds,and the accuracy for wheat mites and rice planthoppers was improved by 4.5-7.5%compared with the original model.The GBiDC-PEST optimization algorithm and its mobile deployment proposed in this study offer a robust technical framework for the rapid,onsite identification and localization of tiny pests.This advancement provides valuable insights for effective pest monitoring,counting,and control in various agricultural settings.展开更多
The fast growth of mobile autonomous machines from traditional equipment to unmanned autonomous vehicles has fueled the demand for accurate and reliable localization solutions in diverse application domains.Ultra Wide...The fast growth of mobile autonomous machines from traditional equipment to unmanned autonomous vehicles has fueled the demand for accurate and reliable localization solutions in diverse application domains.Ultra Wide Band(UWB)technology has emerged as a promising candidate for addressing this need,offering high precision,immunity to multipath interference,and robust performance in challenging environments.In this comprehensive survey,we systematically explore UWB-based localization for mobile autonomous machines,spanning from fundamental principles to future trends.To the best of our knowledge,this review paper stands as the pioneer in systematically dissecting the algorithms of UWB-based localization for mobile autonomous machines,covering a spectrum from bottom-ranging schemes to advanced sensor fusion,error mitigation,and optimization techniques.By synthesizing existing knowledge,evaluating current methodologies,and highlighting future trends,this review aims to catalyze progress and innovation in the field,unlocking new opportunities for mobile autonomous machine applications across diverse industries and domains.Thus,it serves as a valuable resource for researchers,practitioners,and stakeholders interested in advancing the state-of-the-art UWB-based localization for mobile autonomous machines.展开更多
This paper presents a robust finite-time visual servo control strategy for the tracking problem of omni-directional mobile manipulators(OMMs)subject to mismatched disturbances.First,the nonlinear kinematic model of vi...This paper presents a robust finite-time visual servo control strategy for the tracking problem of omni-directional mobile manipulators(OMMs)subject to mismatched disturbances.First,the nonlinear kinematic model of visual servoing for OMMs with mismatched disturbances is explicitly presented to solve the whole-body inverse kinematic problem.Second,a sliding mode observer augmented with an integral terminal sliding mode controller is proposed to handle these uncertainties and ensure that the system converges to a small region around the equilibrium point.The boundary layer technique is employed to mitigate the chattering phenomenon.Furthermore,a strict finite-time Lyapunov stability analysis is conducted.An experimental comparison between the proposed algorithm and a traditional position-based visual servo controller is carried out,and the results demonstrate the superiority of the proposed control algorithm.展开更多
基金supported by the National Key Research and Development Program of China(No.2022YFC3704200)the National Natural Science Foundation of China(Nos.42207113,42475148 and U21A2027)the Presidential Foundation of the Hefei Institutes of Physical Science,Chinese Academy Sciences(No.YZJJQY202401).
文摘To maintain air quality during the 31st World University Games,Chengdu employed a range of monitoring and control strategies in 2023.High-resolution regional pollutant distributions were reconstructed by integrating the vertical column densities(VCDs)from mobile MAX-DOAS measurements with Gaussian process regression analysis.The correlation between the spatial distribution derived from MAX-DOAS and that of GEMS and TROPOMI satellite data exceeded 0.6.This paper explores the impact of air quality improvements during the games on the sources of HCHO and the formation process of HONO.During the control period,primary emissions and secondary formations of HCHO contributed 50.85%±24.24%and 42.81%±7.57%to the total atmospheric HCHO,respectively.The study indicates that with improved air quality,HCHO primary emissions decrease while secondary emissions and atmospheric radiation transmission intensities rise.It is found that HONO always appears under the condition of high aerosol optical depth(AOD)and NO_(2),but high NO_(2) concentration and AOD are not necessarily accompanied by high concentrations of HONO.To assess the influence of temperature and humidity on the formation of HONO from NO_(2),we calculated the emission ratesΔHONO∕ΔNO_(2) to quantify the impact of primary sources on total HONO concentrations.The analysis results show that the turning point of relative humidity is 65%(60%–70%)and the turning point of temperature is 31℃(30–32℃).Lower temperatures and higher humidity levels were found to decrease the rate of secondary HONO formation from NO_(2).
文摘1 Introduction The growing connectivity with mobile internet has significantly enhanced our day-to-day life support through various services and applications with on-demand availability at any time or anywhere.As emerging technologies with continuous revolutions in the digital transformations,various add-on technologies such as quantum computing,AI,and next-generation networks such as 6G are becoming an integral support to mobile internet systems.The emerging technologies in the next-generation mobile internet bring a lot of new security and privacy challenges.
文摘At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments,this paper proposes an Attention-Enhanced Dueling Deep Q-Network(ADDueling DQN),which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework.A multi-objective reward function,centered on energy efficiency,is designed to comprehensively consider path length,terrain slope,motion smoothness,and obstacle avoidance,enabling optimal low-energy trajectory generation in 3D space from the source.The incorporation of a multihead attention mechanism allows the model to dynamically focus on energy-critical state features—such as slope gradients and obstacle density—thereby significantly improving its ability to recognize and avoid energy-intensive paths.Additionally,the prioritized experience replay mechanism accelerates learning from key decision-making experiences,suppressing inefficient exploration and guiding the policy toward low-energy solutions more rapidly.The effectiveness of the proposed path planning algorithm is validated through simulation experiments conducted in multiple off-road scenarios.Results demonstrate that AD-Dueling DQN consistently achieves the lowest average energy consumption across all tested environments.Moreover,the proposed method exhibits faster convergence and greater training stability compared to baseline algorithms,highlighting its global optimization capability under energy-aware objectives in complex terrains.This study offers an efficient and scalable intelligent control strategy for the development of energy-conscious autonomous navigation systems.
基金supported by the Malaysia Ministry of Higher Education under Fundamental Research Grant Scheme with Project Code:FRGS/1/2024/TK07/USM/02/3.
文摘Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This study presents a cognitive control strategy that integrates a Numerical Feedforward Inverse Dynamic Controller(NFIDC)with a Feedback Radial Basis Function Neural Network(FRBFNN).The robot’s mechanical structure was designed in SolidWorks 2022 SP2.0 and validated under operational loads using finite element analysis in ANSYS 2022 R1.The NFIDC-FRBFNN framework merges proactive inverse dynamic compensation with adaptive neural learning to achieve smooth torque responses and accurate motion control.A two-stage simulation evaluation was conducted.In the first stage,the controller was tested in a simulated hospital environment under both ideal and non-ideal conditions.In the second,it was benchmarked against four established controllers-Neural Network Model Reference Adaptive(NNMRA),Z-number Fuzzy Logic(Z-FL),Adaptive Dynamic Controller(ADC),and Fuzzy Logic-PID(FL-PID)—using circular and lemniscate trajectories.Across ten runs,the proposed controller achieved the lowest tracking errors under all conditions.Under ideal conditions,it achieved average improvements of 55.24%,75.75%,and 55.20%in integral absolute error(IAE),integral squared error(ISE),and mean absolute error(MAE),respectively,with coefficient of variation(CV)reductions above 55%.Under non-ideal conditions,average improvements exceeded 64%in IAE,77%in ISE,and 66%in MAE,while maintaining CV reductions above 57%.These results confirm that the NFIDC-FRBFNN controller offers superior accuracy,robustness,and consistency for real-time path tracking in healthcare robotics.
文摘SQL Server Mobile为移动设备和嵌入式设备提供了一个压缩的数据库解决方案。通过使用Microsoft VisualStudio工具,开发者可以将SQL Server的数据库管理能力扩展到移动设备上。介绍了在基于WINCE的移动设备上开发SQL Server Mobile数据库的方法。并结合多媒体词典数据库的开发,详细分析了在系统开发中涉及的几项关键技术,对于嵌入式移动数据库的开发具有一定的参考价值。
文摘基于移动终端的物流管理系统具备信息实时更新、在线查询、物资实时订阅等功能.其核心是移动终端的数据库访问技术,系统中提出的移动物流方案采用微软SQL Server Mobile 2005数据库,利用合并复制、RDA(远程数据访问)等技术建立远程访问模型.采用Internet有线网络和GSM无线网络相结合,发挥移动终端的潜力,提高物流的实时性和管理人员的工作效率.
基金supported by the National Natural Science Foundation of China(61573184)the Specialized Research Fund for the Doctoral Program of Higher Education(20133218110013)+1 种基金the Six Talents Peak Project of Jainism Province(2012-XRAY-010)the Fundamental Research Funds for theCentral Universities(NE2016101)
文摘In this paper,a robust tracking control scheme based on nonlinear disturbance observer is developed for the self-balancing mobile robot with external unknown disturbances.A desired velocity control law is firstly designed using the Lyapunov analysis method and the arctan function.To improve the tracking control performance,a nonlinear disturbance observer is developed to estimate the unknown disturbance of the self-balancing mobile robot.Using the output of the designed disturbance observer,the robust tracking control scheme is presented employing the sliding mode method for the selfbalancing mobile robot.Numerical simulation results further demonstrate the effectiveness of the proposed robust tracking control scheme for the self-balancing mobile robot subject to external unknown disturbances.
基金supported by the National Natural Science Foundation of China(Nos.62373215,62373219 and 62073193)the Natural Science Foundation of Shandong Province(No.ZR2023MF100)+1 种基金the Key Projects of the Ministry of Industry and Information Technology(No.TC220H057-2022)the Independently Developed Instrument Funds of Shandong University(No.zy20240201)。
文摘Current you only look once(YOLO)-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board(PCB)defect detection application scenario.In order to solve this problem,we propose a new method,which combined the lightweight network mobile vision transformer(Mobile Vi T)with the convolutional block attention module(CBAM)mechanism and the new regression loss function.This method needed less computation resources,making it more suitable for embedded edge detection devices.Meanwhile,the new loss function improved the positioning accuracy of the bounding box and enhanced the robustness of the model.In addition,experiments on public datasets demonstrate that the improved model achieves an average accuracy of 87.9%across six typical defect detection tasks,while reducing computational costs by nearly 90%.It significantly reduces the model's computational requirements while maintaining accuracy,ensuring reliable performance for edge deployment.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2024-9/1).
文摘Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.
基金supported by the National Natural Science Foundation of China(61872204)the Natural Science Foundation of Heilongjiang Province of China(F2015025)。
文摘This paper proposes a multivariable fixed-time leaderfollower formation control method for a group of nonholonomic mobile robots, which has the ability to estimate multiple uncertainties. Firstly, based on the state space model of the leader-follower formation, a multivariable fixed-time formation kinematics controller is designed. Secondly, to overcome uncertainties existing in the nonholonomic mobile robot system, such as load change,friction, external disturbance, a multivariable fixed-time torque controller based on the fixed-time disturbance observer at the dynamic level is designed. The designed torque controller is cascaded with the formation controller and finally realizes accurate estimation of the uncertain part of the system, the follower tracking of reference velocity and the desired formation of the leader and the follower in a fixed-time. The fixed-time upper bound is completely determined by the controller parameters, which is independent of the initial state of the system. The multivariable fixed-time control theory and the Lyapunov method are adopted to ensure the system stability.Finally, the effectiveness of the proposed algorithm is verified by the experimental simulation.
基金supported by the National Key Research and Develop-ment Program of China(2019YFB1803400).
文摘1.Introduction Mobile communications have catalyzed a new era of informa-tion technology revolution,significantly broadening and deepen-ing human-to-human,human-to-machine,and machine-to-machine connections.With their incredible speed of development and wide-reaching impact,mobile communications serve as the cornerstone of the Internet of Everything,profoundly reshaping human cognitive abilities and ways of thinking.Furthermore,mobile communications are altering the patterns of production and life,driving leaps in productivity quality,and strongly promot-ing innovation within human civilization.
基金support of the Natural Science Foundation of Jiangsu Province,China(BK20240977)the China Scholarship Council(201606850024)+1 种基金the National High Technology Research and Development Program of China(2016YFD0701003)the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(SJCX23_1488)。
文摘Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has been constrained by high computational demands.Here,we developed GBiDC-PEST,a mobile application that incorporates an improved,lightweight detection algorithm based on the You Only Look Once(YOLO)series singlestage architecture,for real-time detection of four tiny pests(wheat mites,sugarcane aphids,wheat aphids,and rice planthoppers).GBiDC-PEST incorporates several innovative modules,including GhostNet for lightweight feature extraction and architecture optimization by reconstructing the backbone,the bi-directional feature pyramid network(BiFPN)for enhanced multiscale feature fusion,depthwise convolution(DWConv)layers to reduce computational load,and the convolutional block attention module(CBAM)to enable precise feature focus.The newly developed GBiDC-PEST was trained and validated using a multitarget agricultural tiny pest dataset(Tpest-3960)that covered various field environments.GBiDC-PEST(2.8 MB)significantly reduced the model size to only 20%of the original model size,offering a smaller size than the YOLO series(v5-v10),higher detection accuracy than YOLOv10n and v10s,and faster detection speed than v8s,v9c,v10m and v10b.In Android deployment experiments,GBiDCPEST demonstrated enhanced performance in detecting pests against complex backgrounds,and the accuracy for wheat mites and rice planthoppers was improved by 4.5-7.5%compared with the original model.The GBiDC-PEST optimization algorithm and its mobile deployment proposed in this study offer a robust technical framework for the rapid,onsite identification and localization of tiny pests.This advancement provides valuable insights for effective pest monitoring,counting,and control in various agricultural settings.
文摘The fast growth of mobile autonomous machines from traditional equipment to unmanned autonomous vehicles has fueled the demand for accurate and reliable localization solutions in diverse application domains.Ultra Wide Band(UWB)technology has emerged as a promising candidate for addressing this need,offering high precision,immunity to multipath interference,and robust performance in challenging environments.In this comprehensive survey,we systematically explore UWB-based localization for mobile autonomous machines,spanning from fundamental principles to future trends.To the best of our knowledge,this review paper stands as the pioneer in systematically dissecting the algorithms of UWB-based localization for mobile autonomous machines,covering a spectrum from bottom-ranging schemes to advanced sensor fusion,error mitigation,and optimization techniques.By synthesizing existing knowledge,evaluating current methodologies,and highlighting future trends,this review aims to catalyze progress and innovation in the field,unlocking new opportunities for mobile autonomous machine applications across diverse industries and domains.Thus,it serves as a valuable resource for researchers,practitioners,and stakeholders interested in advancing the state-of-the-art UWB-based localization for mobile autonomous machines.
基金supported by the Artificial Intelligence Innovation and Development Special Fund of Shanghai(No.2019RGZN01041)the National Natural Science Foundation of China(No.92048205).
文摘This paper presents a robust finite-time visual servo control strategy for the tracking problem of omni-directional mobile manipulators(OMMs)subject to mismatched disturbances.First,the nonlinear kinematic model of visual servoing for OMMs with mismatched disturbances is explicitly presented to solve the whole-body inverse kinematic problem.Second,a sliding mode observer augmented with an integral terminal sliding mode controller is proposed to handle these uncertainties and ensure that the system converges to a small region around the equilibrium point.The boundary layer technique is employed to mitigate the chattering phenomenon.Furthermore,a strict finite-time Lyapunov stability analysis is conducted.An experimental comparison between the proposed algorithm and a traditional position-based visual servo controller is carried out,and the results demonstrate the superiority of the proposed control algorithm.
