The continuous advancement of remote sensing technology and artificial intelligence has led to the development of UAV(unmanned aerial vehicle)-based crop phenotyping technology,which is becoming increasingly significa...The continuous advancement of remote sensing technology and artificial intelligence has led to the development of UAV(unmanned aerial vehicle)-based crop phenotyping technology,which is becoming increasingly significant in agricultural research.The hardware,algorithms and application contexts associated with UAV phenotyping technology were comprehensively reviewed as well as its future developments.The characteristics of sensors mounted on UAVs and the types of images they capture were introduced,including RGB(red,gueen,blue),infrared,multispectral and fluorescence imaging sensors.The working principles of these sensors and their applications in phenotyping research were subsequently outlined.For example,RGB imaging sensors were utilized for monitoring plant growth status,while infrared sensors were employed for thermal imaging surveillance.Furthermore,the detailed review of the applications of UAV technology in assessing crop field performance were conducted,estimating plant biomass,addressing biotic and abiotic stresses.In conjunction with UAV technology and genome-wide association study(GWAS),the potential for advancing genetic breeding were explored by identifying genes associated with specific crop traits.Finally,the shortcomings of current UAV technology and propose future prospects and recommendations were concluded to enhance its reliability and effectiveness in supporting agricultural production and research.展开更多
Shenzhen,a major city in southern China,has experienced rapid advancements in Unmanned Aerial Vehicle(UAV)technology,resulting in extensive logistics networks with thousands of daily flights.However,frequent disruptio...Shenzhen,a major city in southern China,has experienced rapid advancements in Unmanned Aerial Vehicle(UAV)technology,resulting in extensive logistics networks with thousands of daily flights.However,frequent disruptions due to its subtropical monsoon climate,including typhoons and gusty winds,present ongoing challenges.Despite the growing focus on operational costs and third-party risks,research on low-altitude urban wind fields remains scarce.This study addresses this gap by integrating wind field analysis into UAV path planning,introducing key innovations to the classical model.First,UAV wind resistance and turbulence constraints are analyzed,mapping high-wind-speed and turbulence-prone zones in the airspace.Second,wind dynamics are incorporated into path planning by considering airspeed and groundspeed variation,optimizing waypoint selection and flight speed adjustments to improve overall energy efficiency.Additionally,a wind-aware Theta*algorithm is proposed,leveraging wind vectors to expedite search process,while Computational Fluid Dynamics(CFD)techniques are employed to calculate wind fields.A case study of Shenzhen,examining wind patterns over the past decade,demonstrates a 6.23%improvement in groundspeed and a 7.69%reduction in energy consumption compared to wind-agnostic models.This framework advances UAV logistics by enhancing route safety and energy efficiency,contributing to more cost-effective operations.展开更多
Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combinin...Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.展开更多
Residual loop-interference(LI)poses a significant challenge for the full-duplex(FD)unmanned aerial vehicle(UAV).To address the issue of residual LI,this paper proposes an amplify-and-forward(AaF)FD-UAV relay system ba...Residual loop-interference(LI)poses a significant challenge for the full-duplex(FD)unmanned aerial vehicle(UAV).To address the issue of residual LI,this paper proposes an amplify-and-forward(AaF)FD-UAV relay system based on a novel orthogonal frequency division multiplexing(OFDM)technique,in which a signal model of infinite impulse response(IIR)is established,instead of the classical finite impulse response(FIR).In the proposed scheme,the residual LI is considered a useful signal and can be combined with the novel OFDM to establish the IIR signal model.Meanwhile,the guard interval(GI)is designed to maintain the circular convolution structure,which differs from the cyclic prefix(CP)applied by the classical OFDM.At the receiver,the IIR signals are influenced only by Gaussian white noise.The proposed FD-UAV relay system can maintain a satisfactory bit error rate(BER)even in the presence of significant residual LI,compared to conventional solutions for suppressing LI on FD-UAV relay.Numerical simulations validate that our proposed scheme offers a fresh solution to the residual LI problem in FD-UAV communication.展开更多
The investigation of the Akchour landslide(AKL)demands precise examination on a local scale,which necessitates field surveys that are often hindered by the landslide's steep and extensive nature of the landslide(1...The investigation of the Akchour landslide(AKL)demands precise examination on a local scale,which necessitates field surveys that are often hindered by the landslide's steep and extensive nature of the landslide(1100 m×400 m,ΔZ of 300 m).Digital Elevation Models(DEMs)are among the key datasets used to achieve this objective.A comparative study between freely available DEMs such as Shuttel Radar Topography Mission(SRTM)(30 m×30 m)and Phased Array type L-band Synthetic Aperture Radar(PALSAR)(12.5 m×12.5 m),alongside those generated by unmanned aerial vehicles(UAVs)demonstrates their significant potential for both geomorphological and geomorphometric analysis.Indeed,scaling issues can lead to the oversight of crucial geological elements.Aerial photos at a 1/20000 scale,previously utilized for anaglyph,provide a broad overview but lack detailed information.To address this limitation,we employed the UAV to capture high-resolution aerial views(with a ground resolution of 17 cm).This approach enabled exploration of inaccessible areas,photogrammetry for orthophotos,and the generation of precise DEM supported geomorphological studies.The orthophoto allowed for detailed visual assessment,while the DEM facilitated geomorphological study.The dynamic behaviors within the landslide.Furthermore,the former irrigation network likely exacerbates the situation.Fractures delineating an unstable area are prominent along the main scarp suggesting the possibility of further sliding.This UAV-mapping revealed three distinct zones with varying based approach significantly enhances our understanding of the AKL,surpassing the limitations of traditional methods and providing critical insights into its morphology and potential risks.展开更多
The modern world remains vulnerable to natural disasters,including floods,earthquakes,wildfires,and others.These events remain unpredictable and inevitable,and recovering quickly and effectively requires significant e...The modern world remains vulnerable to natural disasters,including floods,earthquakes,wildfires,and others.These events remain unpredictable and inevitable,and recovering quickly and effectively requires significant effort and expense.Monitoring is becoming more efficient thanks to technologies such as Unmanned Aerial Vehicles(UAVs),which can access hard-to-reach areas and provide real-time data.However,in disaster-affected areas,these monitoring systems may encounter many obstacles when communicating with servers or transmitting monitored data.This paper proposes an adaptive communication model to overcome the challenges faced in disaster-affected areas.A base station is responsible for collecting data(such as images and videos)captured by UAVs performing surveillance within its communication range.This station is typically a tower providing fixed cellular network service.However,in the absence of such a tower,a selected UAV may serve as the station,depending on the situation.If surveillance needs to be performed outside the coverage area,it can continue to communicate via nearby UAVs through cooperative communication.UAVs with internet support,known as the Internet of Flying Things(IoFT),will also be utilized to enhance communication capacity and efficiency.The proposed communication model is validated through experiments,showing superior data transmission performance and higher throughput.Analysis indicates it outperforms traditional systems,even in rural areas,with or without internet access.展开更多
Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic top...Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic topology of Flying Ad Hoc Networks(FANETs)present significant challenges for maintaining reliable,low-latency communication.Conventional geographic routing protocols often struggle in situations where link quality varies and mobility patterns are unpredictable.To overcome these limitations,this paper proposes an improved routing protocol based on reinforcement learning.This new approach integrates Q-learning with mechanisms that are both link-aware and mobility-aware.The proposed method optimizes the selection of relay nodes by using an adaptive reward function that takes into account energy consumption,delay,and link quality.Additionally,a Kalman filter is integrated to predict UAV mobility,improving the stability of communication links under dynamic network conditions.Simulation experiments were conducted using realistic scenarios,varying the number of UAVs to assess scalability.An analysis was conducted on key performance metrics,including the packet delivery ratio,end-to-end delay,and total energy consumption.The results demonstrate that the proposed approach significantly improves the packet delivery ratio by 12%–15%and reduces delay by up to 25.5%when compared to conventional GEO and QGEO protocols.However,this improvement comes at the cost of higher energy consumption due to additional computations and control overhead.Despite this trade-off,the proposed solution ensures reliable and efficient communication,making it well-suited for large-scale UAV networks operating in complex urban environments.展开更多
With the growing deployment of unmanned aerial vehicles(UAVs)swarms in national defense,military operations,and emergency response,secure and reliable intra-swarm identity authentication has become critical for ensuri...With the growing deployment of unmanned aerial vehicles(UAVs)swarms in national defense,military operations,and emergency response,secure and reliable intra-swarm identity authentication has become critical for ensuring coordinated action and mission reliability.To address the drawbacks of public key infrastructure(PKI)based authentication in UAV swarms,namely,complex certificate management,strong dependence on centralized authorities,and authentication latency.We propose a certificateless identity authentication scheme for UAV swarms built on blockchain sharding.The scheme leverages sharding to execute authentication in parallel across multiple shards,significantly improving efficiency.Each UAV locally generates its public/private key pair and then adopts a registration-based encryption(RBE)mechanism:A registration algorithm binds the device identity to its key on the blockchain,ensuring public verifiability and immutability of identity mapping.On this basis,an authentication algorithm runs in which the initiator produces an authentication signature using a common reference string(CRS),on-chain public-key registration information,and its local private key,and the verifier rapidly validates the authentication message using the on-chain registration data and the identity of the initiator.The experimental results demonstrate that the proposed scheme achieves low-latency and high-throughput identity authentication in large-scale UAV swarm environments,providing a solid technical foundation and broad application prospects for trustworthy UAV swarm identity authentication.展开更多
This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential g...This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.展开更多
Cognitive unmanned aerial vehicle(UAV)is promising to tackle the spectrum scarcity problem faced by UAV communications.However,the secure information transmission is challenging due to the open nature of the spectrum ...Cognitive unmanned aerial vehicle(UAV)is promising to tackle the spectrum scarcity problem faced by UAV communications.However,the secure information transmission is challenging due to the open nature of the spectrum sharing.In order to tackle this issue,a cognitive UAV network with cooperative jamming is studied in this paper.A robust resource allocation and trajectory joint optimization problem is formulated by considering the practical case that the channel state information(CSI)cannot be accurately obtained.An iterative algorithm is proposed to address this challenging non-convex problem.Simulation results demonstrate that the worst case robust resource allocation design can realize the secure communications even under the imperfect CSI.Moreover,compared with other benchmark schemes,the proposed scheme can achieve secure performance improvement.展开更多
Dear Editor,This letter presents some control strategies for quadrotor unmanned aerial vehicle(UAV)leader-follower formation model,where the stochastic impulsive deception attacks are fully considered.Based on Lyapuno...Dear Editor,This letter presents some control strategies for quadrotor unmanned aerial vehicle(UAV)leader-follower formation model,where the stochastic impulsive deception attacks are fully considered.Based on Lyapunov method,the outer loop and the inner loop controllers of quadrotor UAV are designed,respectively.Moreover,a relationship between continuous control laws,stochastic impulsive sequences,and impulsive intensity is established in this letter.展开更多
Aiming at the challenges of low throughput,excessive consensus latency and high communication complexity in the Practical Byzantine Fault Tolerance(PBFT)algorithm in blockchain networks,its application in identity ver...Aiming at the challenges of low throughput,excessive consensus latency and high communication complexity in the Practical Byzantine Fault Tolerance(PBFT)algorithm in blockchain networks,its application in identity verification for distributed networking of a drone cluster is limited.Therefore,a lightweight blockchainbased identity authentication model for UAV swarms is designed,and a Credit-score and Grouping-mechanism Practical Byzantine Fault Tolerance(CG-PBFT)algorithm is proposed.CG-PBFT introduces a reputation score evaluation mechanism,classifies the reputation levels of nodes in the network,and optimizes the consensus process based on grouping consensus and BLS aggregate signature technology.Experimental results demonstrate that under identical experimental conditions,compared with the PBFT algorithm,CG-PBFT achieves a 250%increase in average throughput,a 70%reduction in average latency,and simultaneous enhancement in security,thus making it more suitable for UAV swarm networks.展开更多
With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-...With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-consuming and labor-intensive,but they also struggle to provide consistent,high-precision detection and realtime monitoring of pavement surface defects.To overcome these limitations,we propose an Automatic Recognition of PavementDefect(ARPD)algorithm,which leverages unmanned aerial vehicle(UAV)-based aerial imagery to automate the inspection process.The ARPD framework incorporates a backbone network based on the Selective State Space Model(S3M),which is designed to capture long-range temporal dependencies.This enables effective modeling of dynamic correlations among redundant and often repetitive structures commonly found in road imagery.Furthermore,a neck structure based on Semantics and Detail Infusion(SDI)is introduced to guide cross-scale feature fusion.The SDI module enhances the integration of low-level spatial details with high-level semantic cues,thereby improving feature expressiveness and defect localization accuracy.Experimental evaluations demonstrate that theARPDalgorithm achieves a mean average precision(mAP)of 86.1%on a custom-labeled pavement defect dataset,outperforming the state-of-the-art YOLOv11 segmentation model.The algorithm also maintains strong generalization ability on public datasets.These results confirm that ARPD is well-suited for diverse real-world applications in intelligent,large-scale highway defect monitoring and maintenance planning.展开更多
Nowadays,Unmanned Aerial Vehicles(UAVs)are making increasingly important contributions to numerous applications that enhance human quality of life,such as sensing and data collection,computing,and communication.Howeve...Nowadays,Unmanned Aerial Vehicles(UAVs)are making increasingly important contributions to numerous applications that enhance human quality of life,such as sensing and data collection,computing,and communication.However,communication between UAVs still faces challenges due to high-dynamic topology,volatile wireless links,and strict energy budgets.In this work,we introduce an improved communication scheme,namely Proximal Policy Optimization(PPO).Our solution casts hop–by–hop relay selection as aMarkov decision process and develops a decentralized Proximal Policy Optimization framework in an actor–critic form.Akey novelty is the design of the reward function,which jointly considers the delivery ratio,end-to-end delay,and energy efficiency,enabling flexible prioritization in dynamic environments.The simulation results across swarms of 20–70 UAVs show that,the proposed framework enhances delivery ratio to 5%over a Deep Q-Network baseline(reaching≈80%at 70 nodes),reduces latency by about 2–3ms inmedium-to-dense settings(from∼43 to 35–36ms),and attains comparable or slightly lower total energy consumption(typically 0.5%–2%lower).The results indicate that the proposed communication scheme,adaptive and scalable learning-based UAV scenarios,pave the way for re-world UAV deployments.展开更多
Unmanned aerial vehicles(UAVs)are widely utilized in area coverage tasks due to their flexibility and efficiency in geo-graphic information acquisition.However,complex boundary conditions in actual water area maps oft...Unmanned aerial vehicles(UAVs)are widely utilized in area coverage tasks due to their flexibility and efficiency in geo-graphic information acquisition.However,complex boundary conditions in actual water area maps often reduce coverage efficiency.To address this issue,this paper proposes a map preprocessing algorithm that linearizes boundary lines and processes concave areas into concave polygons,followed by gridding the map.Additionally,a collaborative area coverage method for UAV swarms is introduced based on region partitioning,which considers the comprehensive cost of energy consumption and time.An improved Hungarian algorithm is utilized for region partitioning,and a Dubins-A*-based plow-ing area full coverage path planning method is proposed to achieve path smoothing and collaborative coverage of each partition.Two sets of simulation experiments are conducted.The first experiment verifies the effectiveness of the map preprocessing algorithm,and the second compares the proposed collaborative area coverage algorithm with other methods,demonstrating its performance advantages.展开更多
This paper investigates the challenges of structural inconsistency,matching accuracy degradation,and trajectory interruptions caused by high-speed motion,frequent occlusions,and appearance variations of unmanned aeria...This paper investigates the challenges of structural inconsistency,matching accuracy degradation,and trajectory interruptions caused by high-speed motion,frequent occlusions,and appearance variations of unmanned aerial vehicle(UAV) targets in low-altitude airspace.A novel UAV visual tracking method is proposed for dynamic structural distortions,with a focus on structural consistency modeling to improve system robustness in complex scenarios.Unlike prior methods such as STARK,which rely on spatio-temporal prediction,and KeepTrack,which emphasize template maintenance,our approach enforces structural-level consistency between historical and current features,thereby addressing UAV-specific issues of rapid maneuvering and environmental complexity.The proposed framework features a structure-aware architecture that incorporates dual complementary mechanisms serving as spatial completion and temporal restoration components.First,a multi-scale structure extraction module with adaptive anchor scheduling is developed to dynamically perceive spatial target shape and generate high-quality proposals.Second,a structural memory module is designed to reconstruct local regions by leveraging high-confidence historical structural representations,thereby maintaining spatiotemporal coherence across frames.Furthermore,a structural verification mechanism coupled with a meta-learning-driven re-identification strategy is introduced to detect abrupt structural distortions and adaptively update templates,significantly improving resilience against disturbances.Overall,the main contributions of this paper can be summarized as follows:(1) introducing structural consistency modeling into UAV visual tracking for the first time;(2) designing a unified framework that combines adaptive proposal generation,full-image matching,and re-identification under structural constraints;and(3) achieving state-of-the-art performance on the anti-UAV benchmark,highlighting the method's practical value in real-world UAV surveillance applications.展开更多
基金supported by the National Key R&D Program of China(2021YFE0101400)National Natural Science Foundation of China(31871681).
文摘The continuous advancement of remote sensing technology and artificial intelligence has led to the development of UAV(unmanned aerial vehicle)-based crop phenotyping technology,which is becoming increasingly significant in agricultural research.The hardware,algorithms and application contexts associated with UAV phenotyping technology were comprehensively reviewed as well as its future developments.The characteristics of sensors mounted on UAVs and the types of images they capture were introduced,including RGB(red,gueen,blue),infrared,multispectral and fluorescence imaging sensors.The working principles of these sensors and their applications in phenotyping research were subsequently outlined.For example,RGB imaging sensors were utilized for monitoring plant growth status,while infrared sensors were employed for thermal imaging surveillance.Furthermore,the detailed review of the applications of UAV technology in assessing crop field performance were conducted,estimating plant biomass,addressing biotic and abiotic stresses.In conjunction with UAV technology and genome-wide association study(GWAS),the potential for advancing genetic breeding were explored by identifying genes associated with specific crop traits.Finally,the shortcomings of current UAV technology and propose future prospects and recommendations were concluded to enhance its reliability and effectiveness in supporting agricultural production and research.
基金supported by the National Natural Science Foundation of China(No.U2433214)。
文摘Shenzhen,a major city in southern China,has experienced rapid advancements in Unmanned Aerial Vehicle(UAV)technology,resulting in extensive logistics networks with thousands of daily flights.However,frequent disruptions due to its subtropical monsoon climate,including typhoons and gusty winds,present ongoing challenges.Despite the growing focus on operational costs and third-party risks,research on low-altitude urban wind fields remains scarce.This study addresses this gap by integrating wind field analysis into UAV path planning,introducing key innovations to the classical model.First,UAV wind resistance and turbulence constraints are analyzed,mapping high-wind-speed and turbulence-prone zones in the airspace.Second,wind dynamics are incorporated into path planning by considering airspeed and groundspeed variation,optimizing waypoint selection and flight speed adjustments to improve overall energy efficiency.Additionally,a wind-aware Theta*algorithm is proposed,leveraging wind vectors to expedite search process,while Computational Fluid Dynamics(CFD)techniques are employed to calculate wind fields.A case study of Shenzhen,examining wind patterns over the past decade,demonstrates a 6.23%improvement in groundspeed and a 7.69%reduction in energy consumption compared to wind-agnostic models.This framework advances UAV logistics by enhancing route safety and energy efficiency,contributing to more cost-effective operations.
文摘Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.
基金the National Natural Science Foundation of China(No.62001333,No.52207167)the Scientific Research Project of Education Department of Hubei Province(No.D20221702)Hunan Provincial Natural Science Foundation(No.2022JJ50181)。
文摘Residual loop-interference(LI)poses a significant challenge for the full-duplex(FD)unmanned aerial vehicle(UAV).To address the issue of residual LI,this paper proposes an amplify-and-forward(AaF)FD-UAV relay system based on a novel orthogonal frequency division multiplexing(OFDM)technique,in which a signal model of infinite impulse response(IIR)is established,instead of the classical finite impulse response(FIR).In the proposed scheme,the residual LI is considered a useful signal and can be combined with the novel OFDM to establish the IIR signal model.Meanwhile,the guard interval(GI)is designed to maintain the circular convolution structure,which differs from the cyclic prefix(CP)applied by the classical OFDM.At the receiver,the IIR signals are influenced only by Gaussian white noise.The proposed FD-UAV relay system can maintain a satisfactory bit error rate(BER)even in the presence of significant residual LI,compared to conventional solutions for suppressing LI on FD-UAV relay.Numerical simulations validate that our proposed scheme offers a fresh solution to the residual LI problem in FD-UAV communication.
文摘The investigation of the Akchour landslide(AKL)demands precise examination on a local scale,which necessitates field surveys that are often hindered by the landslide's steep and extensive nature of the landslide(1100 m×400 m,ΔZ of 300 m).Digital Elevation Models(DEMs)are among the key datasets used to achieve this objective.A comparative study between freely available DEMs such as Shuttel Radar Topography Mission(SRTM)(30 m×30 m)and Phased Array type L-band Synthetic Aperture Radar(PALSAR)(12.5 m×12.5 m),alongside those generated by unmanned aerial vehicles(UAVs)demonstrates their significant potential for both geomorphological and geomorphometric analysis.Indeed,scaling issues can lead to the oversight of crucial geological elements.Aerial photos at a 1/20000 scale,previously utilized for anaglyph,provide a broad overview but lack detailed information.To address this limitation,we employed the UAV to capture high-resolution aerial views(with a ground resolution of 17 cm).This approach enabled exploration of inaccessible areas,photogrammetry for orthophotos,and the generation of precise DEM supported geomorphological studies.The orthophoto allowed for detailed visual assessment,while the DEM facilitated geomorphological study.The dynamic behaviors within the landslide.Furthermore,the former irrigation network likely exacerbates the situation.Fractures delineating an unstable area are prominent along the main scarp suggesting the possibility of further sliding.This UAV-mapping revealed three distinct zones with varying based approach significantly enhances our understanding of the AKL,surpassing the limitations of traditional methods and providing critical insights into its morphology and potential risks.
文摘The modern world remains vulnerable to natural disasters,including floods,earthquakes,wildfires,and others.These events remain unpredictable and inevitable,and recovering quickly and effectively requires significant effort and expense.Monitoring is becoming more efficient thanks to technologies such as Unmanned Aerial Vehicles(UAVs),which can access hard-to-reach areas and provide real-time data.However,in disaster-affected areas,these monitoring systems may encounter many obstacles when communicating with servers or transmitting monitored data.This paper proposes an adaptive communication model to overcome the challenges faced in disaster-affected areas.A base station is responsible for collecting data(such as images and videos)captured by UAVs performing surveillance within its communication range.This station is typically a tower providing fixed cellular network service.However,in the absence of such a tower,a selected UAV may serve as the station,depending on the situation.If surveillance needs to be performed outside the coverage area,it can continue to communicate via nearby UAVs through cooperative communication.UAVs with internet support,known as the Internet of Flying Things(IoFT),will also be utilized to enhance communication capacity and efficiency.The proposed communication model is validated through experiments,showing superior data transmission performance and higher throughput.Analysis indicates it outperforms traditional systems,even in rural areas,with or without internet access.
基金funded by Hung Yen University of Technology and Education under grand number UTEHY.L.2025.62.
文摘Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic topology of Flying Ad Hoc Networks(FANETs)present significant challenges for maintaining reliable,low-latency communication.Conventional geographic routing protocols often struggle in situations where link quality varies and mobility patterns are unpredictable.To overcome these limitations,this paper proposes an improved routing protocol based on reinforcement learning.This new approach integrates Q-learning with mechanisms that are both link-aware and mobility-aware.The proposed method optimizes the selection of relay nodes by using an adaptive reward function that takes into account energy consumption,delay,and link quality.Additionally,a Kalman filter is integrated to predict UAV mobility,improving the stability of communication links under dynamic network conditions.Simulation experiments were conducted using realistic scenarios,varying the number of UAVs to assess scalability.An analysis was conducted on key performance metrics,including the packet delivery ratio,end-to-end delay,and total energy consumption.The results demonstrate that the proposed approach significantly improves the packet delivery ratio by 12%–15%and reduces delay by up to 25.5%when compared to conventional GEO and QGEO protocols.However,this improvement comes at the cost of higher energy consumption due to additional computations and control overhead.Despite this trade-off,the proposed solution ensures reliable and efficient communication,making it well-suited for large-scale UAV networks operating in complex urban environments.
基金supported by the National Natural Science Foundation of China under Grant No.62472075the Innovation Theory and Technology Group Fund of the Southwest China Institute of Electronic Technology under Grant No.2024jsq0207.
文摘With the growing deployment of unmanned aerial vehicles(UAVs)swarms in national defense,military operations,and emergency response,secure and reliable intra-swarm identity authentication has become critical for ensuring coordinated action and mission reliability.To address the drawbacks of public key infrastructure(PKI)based authentication in UAV swarms,namely,complex certificate management,strong dependence on centralized authorities,and authentication latency.We propose a certificateless identity authentication scheme for UAV swarms built on blockchain sharding.The scheme leverages sharding to execute authentication in parallel across multiple shards,significantly improving efficiency.Each UAV locally generates its public/private key pair and then adopts a registration-based encryption(RBE)mechanism:A registration algorithm binds the device identity to its key on the blockchain,ensuring public verifiability and immutability of identity mapping.On this basis,an authentication algorithm runs in which the initiator produces an authentication signature using a common reference string(CRS),on-chain public-key registration information,and its local private key,and the verifier rapidly validates the authentication message using the on-chain registration data and the identity of the initiator.The experimental results demonstrate that the proposed scheme achieves low-latency and high-throughput identity authentication in large-scale UAV swarm environments,providing a solid technical foundation and broad application prospects for trustworthy UAV swarm identity authentication.
文摘This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.
基金National Key R&D Program of China under Grant 2020YFB1807602the National Natural Science Foundation of China under Grant 62222107,Grant 62071223,Grant 62031012Young Elite Scientist Sponsorship Program by CAST。
文摘Cognitive unmanned aerial vehicle(UAV)is promising to tackle the spectrum scarcity problem faced by UAV communications.However,the secure information transmission is challenging due to the open nature of the spectrum sharing.In order to tackle this issue,a cognitive UAV network with cooperative jamming is studied in this paper.A robust resource allocation and trajectory joint optimization problem is formulated by considering the practical case that the channel state information(CSI)cannot be accurately obtained.An iterative algorithm is proposed to address this challenging non-convex problem.Simulation results demonstrate that the worst case robust resource allocation design can realize the secure communications even under the imperfect CSI.Moreover,compared with other benchmark schemes,the proposed scheme can achieve secure performance improvement.
基金supported by the National Natural Science Foundation of China(62173215)the Project for the Integrated Development of the City and Universities in Jinan(JNSX2024016)。
文摘Dear Editor,This letter presents some control strategies for quadrotor unmanned aerial vehicle(UAV)leader-follower formation model,where the stochastic impulsive deception attacks are fully considered.Based on Lyapunov method,the outer loop and the inner loop controllers of quadrotor UAV are designed,respectively.Moreover,a relationship between continuous control laws,stochastic impulsive sequences,and impulsive intensity is established in this letter.
基金supported by the following projects:Fund for technical areas of infrastructure strengthening plan projects under Grant 2023-JCJQ-JJ-0772.
文摘Aiming at the challenges of low throughput,excessive consensus latency and high communication complexity in the Practical Byzantine Fault Tolerance(PBFT)algorithm in blockchain networks,its application in identity verification for distributed networking of a drone cluster is limited.Therefore,a lightweight blockchainbased identity authentication model for UAV swarms is designed,and a Credit-score and Grouping-mechanism Practical Byzantine Fault Tolerance(CG-PBFT)algorithm is proposed.CG-PBFT introduces a reputation score evaluation mechanism,classifies the reputation levels of nodes in the network,and optimizes the consensus process based on grouping consensus and BLS aggregate signature technology.Experimental results demonstrate that under identical experimental conditions,compared with the PBFT algorithm,CG-PBFT achieves a 250%increase in average throughput,a 70%reduction in average latency,and simultaneous enhancement in security,thus making it more suitable for UAV swarm networks.
基金supported in part by the Technical Service for the Development and Application of an Intelligent Visual Management Platformfor Expressway Construction Progress Based on BIM Technology(grant NO.JKYZLX-2023-09)in partby the Technical Service for the Development of an Early Warning Model in the Research and Application of Key Technologies for Tunnel Operation Safety Monitoring and Early Warning Based on Digital Twin(grant NO.JK-S02-ZNGS-202412-JISHU-FA-0035)sponsored by Yunnan Transportation Science Research Institute Co.,Ltd.
文摘With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-consuming and labor-intensive,but they also struggle to provide consistent,high-precision detection and realtime monitoring of pavement surface defects.To overcome these limitations,we propose an Automatic Recognition of PavementDefect(ARPD)algorithm,which leverages unmanned aerial vehicle(UAV)-based aerial imagery to automate the inspection process.The ARPD framework incorporates a backbone network based on the Selective State Space Model(S3M),which is designed to capture long-range temporal dependencies.This enables effective modeling of dynamic correlations among redundant and often repetitive structures commonly found in road imagery.Furthermore,a neck structure based on Semantics and Detail Infusion(SDI)is introduced to guide cross-scale feature fusion.The SDI module enhances the integration of low-level spatial details with high-level semantic cues,thereby improving feature expressiveness and defect localization accuracy.Experimental evaluations demonstrate that theARPDalgorithm achieves a mean average precision(mAP)of 86.1%on a custom-labeled pavement defect dataset,outperforming the state-of-the-art YOLOv11 segmentation model.The algorithm also maintains strong generalization ability on public datasets.These results confirm that ARPD is well-suited for diverse real-world applications in intelligent,large-scale highway defect monitoring and maintenance planning.
基金funded byHung YenUniversity of Technology and Education under grant number UTEHY.L.2026.05.
文摘Nowadays,Unmanned Aerial Vehicles(UAVs)are making increasingly important contributions to numerous applications that enhance human quality of life,such as sensing and data collection,computing,and communication.However,communication between UAVs still faces challenges due to high-dynamic topology,volatile wireless links,and strict energy budgets.In this work,we introduce an improved communication scheme,namely Proximal Policy Optimization(PPO).Our solution casts hop–by–hop relay selection as aMarkov decision process and develops a decentralized Proximal Policy Optimization framework in an actor–critic form.Akey novelty is the design of the reward function,which jointly considers the delivery ratio,end-to-end delay,and energy efficiency,enabling flexible prioritization in dynamic environments.The simulation results across swarms of 20–70 UAVs show that,the proposed framework enhances delivery ratio to 5%over a Deep Q-Network baseline(reaching≈80%at 70 nodes),reduces latency by about 2–3ms inmedium-to-dense settings(from∼43 to 35–36ms),and attains comparable or slightly lower total energy consumption(typically 0.5%–2%lower).The results indicate that the proposed communication scheme,adaptive and scalable learning-based UAV scenarios,pave the way for re-world UAV deployments.
基金National Natural Science Foundation of China(62402020,62303022)Beijing Nova Program(20240484720)+1 种基金Project of Cultivation for Young Top-Notch Talents of Beijing Municipal Institutions(BPHR202203043)BTBU Digital Business Platform Project byBMEC.
文摘Unmanned aerial vehicles(UAVs)are widely utilized in area coverage tasks due to their flexibility and efficiency in geo-graphic information acquisition.However,complex boundary conditions in actual water area maps often reduce coverage efficiency.To address this issue,this paper proposes a map preprocessing algorithm that linearizes boundary lines and processes concave areas into concave polygons,followed by gridding the map.Additionally,a collaborative area coverage method for UAV swarms is introduced based on region partitioning,which considers the comprehensive cost of energy consumption and time.An improved Hungarian algorithm is utilized for region partitioning,and a Dubins-A*-based plow-ing area full coverage path planning method is proposed to achieve path smoothing and collaborative coverage of each partition.Two sets of simulation experiments are conducted.The first experiment verifies the effectiveness of the map preprocessing algorithm,and the second compares the proposed collaborative area coverage algorithm with other methods,demonstrating its performance advantages.
基金Supported by the National Science Foundation of China (No.62571164)the Natural Science Foundation of Heilongjiang Province (No.PL2024F025)the Fundamental Scientific Research Funds of Heilongjiang Province (No.2022-KYYWF-1050)。
文摘This paper investigates the challenges of structural inconsistency,matching accuracy degradation,and trajectory interruptions caused by high-speed motion,frequent occlusions,and appearance variations of unmanned aerial vehicle(UAV) targets in low-altitude airspace.A novel UAV visual tracking method is proposed for dynamic structural distortions,with a focus on structural consistency modeling to improve system robustness in complex scenarios.Unlike prior methods such as STARK,which rely on spatio-temporal prediction,and KeepTrack,which emphasize template maintenance,our approach enforces structural-level consistency between historical and current features,thereby addressing UAV-specific issues of rapid maneuvering and environmental complexity.The proposed framework features a structure-aware architecture that incorporates dual complementary mechanisms serving as spatial completion and temporal restoration components.First,a multi-scale structure extraction module with adaptive anchor scheduling is developed to dynamically perceive spatial target shape and generate high-quality proposals.Second,a structural memory module is designed to reconstruct local regions by leveraging high-confidence historical structural representations,thereby maintaining spatiotemporal coherence across frames.Furthermore,a structural verification mechanism coupled with a meta-learning-driven re-identification strategy is introduced to detect abrupt structural distortions and adaptively update templates,significantly improving resilience against disturbances.Overall,the main contributions of this paper can be summarized as follows:(1) introducing structural consistency modeling into UAV visual tracking for the first time;(2) designing a unified framework that combines adaptive proposal generation,full-image matching,and re-identification under structural constraints;and(3) achieving state-of-the-art performance on the anti-UAV benchmark,highlighting the method's practical value in real-world UAV surveillance applications.
