Traditional weather observation methods have limitations in detecting low-altitude,small-scale areas and sudden weather events.They often have insufficient coverage,slow response,or high costs.Multi-rotor unmanned aer...Traditional weather observation methods have limitations in detecting low-altitude,small-scale areas and sudden weather events.They often have insufficient coverage,slow response,or high costs.Multi-rotor unmanned aerial vehicles(UAVs),with their strong vertical take-off and landing ability,precise hovering,flexible movement,and ability to carry various small sensors,are gradually becoming key tools to fill these gaps and build three-dimensional weather observation networks.They show important value in medium-and small-scale weather monitoring and emergency weather support.This paper reviews the main sensors for multi-rotor weather drones,their operating modes,and key supporting technologies,summarizes the current state of technology,and provides references for future development.展开更多
This paper shows that the aerodynamic effects can be compensated in a quadrotor system by means of a control allocation approach using neural networks.Thus,the system performance can be improved by replacing the class...This paper shows that the aerodynamic effects can be compensated in a quadrotor system by means of a control allocation approach using neural networks.Thus,the system performance can be improved by replacing the classic allocation matrix,without using the aerodynamic inflow equations directly.The network training is performed offline,which requires low computational power.The target system is a Parrot MAMBO drone whose flight control is composed of PD-PID controllers followed by the proposed neural network control allocation algorithm.Such a quadrotor is particularly susceptible to the aerodynamics effects of interest to this work,because of its small size.We compared the mechanical torques commanded by the flight controller,i.e.,the control input,to those actually generated by the actuators and established at the aircraft.It was observed that the proposed neural network was able to closely match them,while the classic allocation matrix could not achieve that.The allocation error was also determined in both cases.Furthermore,the closed-loop performance also improved with the use of the proposed neural network control allocation,as well as the quality of the thrust and torque signals,in which we perceived a much less noisy behavior.展开更多
A fusion chemical reaction optimization algorithm based on random molecules(RMCRO) is proposed to meet the special demand of power transmission line inspection. This new algorithm improves the shortcomings of chemical...A fusion chemical reaction optimization algorithm based on random molecules(RMCRO) is proposed to meet the special demand of power transmission line inspection. This new algorithm improves the shortcomings of chemical reaction algorithm by merging the idea of repellent-attractant rule and accelerates convergence by using difference algorithm. The molecules in this algorithm avoid obstacles and search optimal path of transmission line inspection by using sensors on multi-rotor unmanned aerial vehicle(UAV). The option of optimal path is based on potential energy of molecules and cost function without repeated parameter adjustment and complicated computation. By compared with an improved particle swarm optimization(IMPSO) in different circumstances of simulation, it can be concluded that the new algorithm presented not only can obtain more optimal path and avoid to trap in local minimum, but also can keep related sensors in a more stable status.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
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
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
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
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
(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbi...(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbitrary-elevation one-cylinder model.The derived results include a closed-form expression for the space-time correlation function and some quasi-closed-form ones for the space-Doppler power spectrum density,the level crossing rate,and the average fading duration,which are shown to be the generalizations of those previously obtained from the two-dimensional(2-D)one-ring model and the 3-D low-elevation one-cylinder model for terrestrial mobile-to-mobile channels.The close agreements between the theoretical results and the simulations as well as the measurements validate the utility of the derived channel statistics.Based on the derived expressions,the impacts of some parameters on the channel characteristics are investigated in an effective,efficient,and explicable way,which leads to a general guideline on the manual parameter estimation from the measurement description.展开更多
Desert shrubs are indispensable in maintaining ecological stability by reducing soil erosion,enhancing water retention,and boosting soil fertility,which are critical factors in mitigating desertification processes.Due...Desert shrubs are indispensable in maintaining ecological stability by reducing soil erosion,enhancing water retention,and boosting soil fertility,which are critical factors in mitigating desertification processes.Due to the complex topography,variable climate,and challenges in field surveys in desert regions,this paper proposes YOLO-Desert-Shrub(YOLO-DS),a detection method for identifying desert shrubs in UAV remote sensing images based on an enhanced YOLOv8n framework.This method accurately identifying shrub species,locations,and coverage.To address the issue of small individual plants dominating the dataset,the SPDconv convolution module is introduced in the Backbone and Neck layers of the YOLOv8n model,replacing conventional convolutions.This structural optimization mitigates information degradation in fine-grained data while strengthening discriminative feature capture across spatial scales within desert shrub datasets.Furthermore,a structured state-space model is integrated into the main network,and the MambaLayer is designed to dynamically extract and refine shrub-specific features from remote sensing images,effectively filtering out background noise and irrelevant interference to enhance feature representation.Benchmark evaluations reveal the YOLO-DS framework attains 79.56%mAP40weight,demonstrating 2.2%absolute gain versus the baseline YOLOv8n architecture,with statistically significant advantages over contemporary detectors in cross-validation trials.The predicted plant coverage exhibits strong consistency with manually measured coverage,with a coefficient of determination(R^(2))of 0.9148 and a Root Mean Square Error(RMSE)of1.8266%.The proposed UAV-based remote sensing method utilizing the YOLO-DS effectively identify and locate desert shrubs,monitor canopy sizes and distribution,and provide technical support for automated desert shrub monitoring.展开更多
Detecting small forest fire targets in unmanned aerial vehicle(UAV)images is difficult,as flames typically cover only a very limited portion of the visual scene.This study proposes Context-guided Compact Lightweight N...Detecting small forest fire targets in unmanned aerial vehicle(UAV)images is difficult,as flames typically cover only a very limited portion of the visual scene.This study proposes Context-guided Compact Lightweight Network(CCLNet),an end-to-end lightweight model designed to detect small forest fire targets while ensuring efficient inference on devices with constrained computational resources.CCLNet employs a three-stage network architecture.Its key components include three modules.C3F-Convolutional Gated Linear Unit(C3F-CGLU)performs selective local feature extraction while preserving fine-grained high-frequency flame details.Context-Guided Feature Fusion Module(CGFM)replaces plain concatenation with triplet-attention interactions to emphasize subtle flame patterns.Lightweight Shared Convolution with Separated Batch Normalization Detection(LSCSBD)reduces parameters through separated batch normalization while maintaining scale-specific statistics.We build TF-11K,an 11,139-image dataset combining 9139 self-collected UAV images from subtropical forests and 2000 re-annotated frames from the FLAME dataset.On TF-11K,CCLNet attains 85.8%mAP@0.5,45.5%mean Average Precision(mAP)@[0.5:0.95],87.4%precision,and 79.1%recall with 2.21 M parameters and 5.7 Giga Floating-point Operations Per Second(GFLOPs).The ablation study confirms that each module contributes to both accuracy and efficiency.Cross-dataset evaluation on DFS yields 77.5%mAP@0.5 and 42.3%mAP@[0.5:0.95],indicating good generalization to unseen scenes.These results suggest that CCLNet offers a practical balance between accuracy and speed for small-target forest fire monitoring with UAVs.展开更多
Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always ...Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always influenced by external factors,making the original path planning solution ineffective.In this paper,the multi-depot multi-UAV path planning problem with uncertain flight time is modeled as a robust optimization model with a budget uncertainty set.Then,the robust optimization model is transformed into a mixed integer linear programming model by the strong duality theorem,which makes the problem easy to solve.To effectively solve large-scale instances,a simulated annealing algorithm with a robust feasibility check(SA-RFC)is developed.The numerical experiment shows that the SA-RFC can find high-quality solutions within a few seconds.Moreover,the effect of the task location distribution,depot counts,and variations in robustness parameters on the robust optimization solution is analyzed by using Monte Carlo experiments.The results demonstrate that the proposed robust model can effectively reduce the risk of the UAV failing to return to the depot without significantly compromising the profit.展开更多
With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods ...With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.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
The identification of rock mass hazard sources is fundamental for preventing rockfall and landslide disasters in mountainous regions,with rock mass structural characteristics playing a vital role in hazard assessment....The identification of rock mass hazard sources is fundamental for preventing rockfall and landslide disasters in mountainous regions,with rock mass structural characteristics playing a vital role in hazard assessment.In this study,terrestrial laser scanning(TLS)and unmanned aerial vehicle(UAV)technologies were integrated to enhance the evaluation methodology for rock mass hazard sources,focusing on the Sichuan Yanjiang Expressway project in China.The findings demonstrate that TLS-UAV technology enhanced both spatial coverage and data density in slope modeling.Through integrated algorithmic analysis,rock discontinuities within heterogeneous datasets were systematically identified,enabling quantitative extraction and statistical analysis of key geometric parameters,including orientation,trace length,spacing,and roughness.Furthermore,quantitative models were developed for cohesion,friction angle and the morphology parameter M of in situ discontinuities,respectively,facilitating efficient mechanical parameter acquisition.A novel rock mass hazard index(RHI)was developed incorporating discontinuity geometric rating(DGR),discontinuity mechanical rating(DMR),and slope mass rating(SMR).Field validation confirmed the methodology's effectiveness in evaluating risk levels and spatial heterogeneity of rock mass hazard sources,revealing the contribution of different discontinuity sets to the rock mass hazard and identifying the primary discontinuity sets controlling instability mechanisms.This study is of great significance for evaluating discontinuity-controlled rock mass hazard sources and preventing rockfall disasters.展开更多
For mission-oriented unmanned aerial vehicle(UAV)swarms,mission capability assessment provides an important reference in the design and development process,and is a precondition for mission success.For this multi-crit...For mission-oriented unmanned aerial vehicle(UAV)swarms,mission capability assessment provides an important reference in the design and development process,and is a precondition for mission success.For this multi-criteria decisionmaking(MCDM)problem,the current literature lacks a way to unambiguously present criteria and the popular fuzzy analytic network process(ANP)approaches neglect the hesitancy of subjective judgments.To fill these research gaps,an MCDM method based on unified architecture framework(UAF)and interval-valued spherical fuzzy ANP(IVSF-ANP)is proposed in this paper.Firstly,selected viewpoints in UAF are extended to construct criteria models with standardized representation.Secondly,interval-valued spherical fuzzy sets are introduced to ANP to weight interdependent criteria,handling fuzziness and hesitancy in pairwise comparisons.A method of adjusting weights of experts based on their decision similarities is also included in this process to reduce ambiguity brought by multiple experts.Next,performance characteristics are non-linearly transformed regarding to expectations to get final results.This proposition is applied to assess the mission capability of UAV swarms to search and strike surface vessels.Comparative analysis shows that the proposed method is valid and reasonable.展开更多
基金supported by the High-Level Talent Foundation of Natural Science Research Funding Project for Ordinary Universities in Jiangsu Province(grant number.25KJD520004)Jinling Institute of Technology(grant number.JIT-B-202413).
文摘Traditional weather observation methods have limitations in detecting low-altitude,small-scale areas and sudden weather events.They often have insufficient coverage,slow response,or high costs.Multi-rotor unmanned aerial vehicles(UAVs),with their strong vertical take-off and landing ability,precise hovering,flexible movement,and ability to carry various small sensors,are gradually becoming key tools to fill these gaps and build three-dimensional weather observation networks.They show important value in medium-and small-scale weather monitoring and emergency weather support.This paper reviews the main sensors for multi-rotor weather drones,their operating modes,and key supporting technologies,summarizes the current state of technology,and provides references for future development.
文摘This paper shows that the aerodynamic effects can be compensated in a quadrotor system by means of a control allocation approach using neural networks.Thus,the system performance can be improved by replacing the classic allocation matrix,without using the aerodynamic inflow equations directly.The network training is performed offline,which requires low computational power.The target system is a Parrot MAMBO drone whose flight control is composed of PD-PID controllers followed by the proposed neural network control allocation algorithm.Such a quadrotor is particularly susceptible to the aerodynamics effects of interest to this work,because of its small size.We compared the mechanical torques commanded by the flight controller,i.e.,the control input,to those actually generated by the actuators and established at the aircraft.It was observed that the proposed neural network was able to closely match them,while the classic allocation matrix could not achieve that.The allocation error was also determined in both cases.Furthermore,the closed-loop performance also improved with the use of the proposed neural network control allocation,as well as the quality of the thrust and torque signals,in which we perceived a much less noisy behavior.
基金the Fund of the Aeronautics Science(No.20162852031)the National Natural Science Foundation of China(No.61473144)
文摘A fusion chemical reaction optimization algorithm based on random molecules(RMCRO) is proposed to meet the special demand of power transmission line inspection. This new algorithm improves the shortcomings of chemical reaction algorithm by merging the idea of repellent-attractant rule and accelerates convergence by using difference algorithm. The molecules in this algorithm avoid obstacles and search optimal path of transmission line inspection by using sensors on multi-rotor unmanned aerial vehicle(UAV). The option of optimal path is based on potential energy of molecules and cost function without repeated parameter adjustment and complicated computation. By compared with an improved particle swarm optimization(IMPSO) in different circumstances of simulation, it can be concluded that the new algorithm presented not only can obtain more optimal path and avoid to trap in local minimum, but also can keep related sensors in a more stable status.
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.
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.
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 in part by the National Key Research and Development Program of China(2021YFB2900501)in part by the Shaanxi Science and Technology Innovation Team(2023-CX-TD-03)+3 种基金in part by the Science and Technology Program of Shaanxi Province(2021GXLH-Z-038)in part by the Natural Science Foundation of Hunan Province(2023JJ40607 and 2023JJ50045)in part by the Scientific Research Foundation of Hunan Provincial Education Department(23B0713 and 24B0603)in part by the National Natural Science Foundation of China(62401371,62101275,and 62372070).
文摘(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbitrary-elevation one-cylinder model.The derived results include a closed-form expression for the space-time correlation function and some quasi-closed-form ones for the space-Doppler power spectrum density,the level crossing rate,and the average fading duration,which are shown to be the generalizations of those previously obtained from the two-dimensional(2-D)one-ring model and the 3-D low-elevation one-cylinder model for terrestrial mobile-to-mobile channels.The close agreements between the theoretical results and the simulations as well as the measurements validate the utility of the derived channel statistics.Based on the derived expressions,the impacts of some parameters on the channel characteristics are investigated in an effective,efficient,and explicable way,which leads to a general guideline on the manual parameter estimation from the measurement description.
基金supported by the National Public Welfare Forest Desert Shrubbery Monitoring Project。
文摘Desert shrubs are indispensable in maintaining ecological stability by reducing soil erosion,enhancing water retention,and boosting soil fertility,which are critical factors in mitigating desertification processes.Due to the complex topography,variable climate,and challenges in field surveys in desert regions,this paper proposes YOLO-Desert-Shrub(YOLO-DS),a detection method for identifying desert shrubs in UAV remote sensing images based on an enhanced YOLOv8n framework.This method accurately identifying shrub species,locations,and coverage.To address the issue of small individual plants dominating the dataset,the SPDconv convolution module is introduced in the Backbone and Neck layers of the YOLOv8n model,replacing conventional convolutions.This structural optimization mitigates information degradation in fine-grained data while strengthening discriminative feature capture across spatial scales within desert shrub datasets.Furthermore,a structured state-space model is integrated into the main network,and the MambaLayer is designed to dynamically extract and refine shrub-specific features from remote sensing images,effectively filtering out background noise and irrelevant interference to enhance feature representation.Benchmark evaluations reveal the YOLO-DS framework attains 79.56%mAP40weight,demonstrating 2.2%absolute gain versus the baseline YOLOv8n architecture,with statistically significant advantages over contemporary detectors in cross-validation trials.The predicted plant coverage exhibits strong consistency with manually measured coverage,with a coefficient of determination(R^(2))of 0.9148 and a Root Mean Square Error(RMSE)of1.8266%.The proposed UAV-based remote sensing method utilizing the YOLO-DS effectively identify and locate desert shrubs,monitor canopy sizes and distribution,and provide technical support for automated desert shrub monitoring.
基金funded by the Natural Science Foundation of Hunan Province(Grant No.2025JJ80352)the National Natural Science Foundation Project of China(Grant No.32271879).
文摘Detecting small forest fire targets in unmanned aerial vehicle(UAV)images is difficult,as flames typically cover only a very limited portion of the visual scene.This study proposes Context-guided Compact Lightweight Network(CCLNet),an end-to-end lightweight model designed to detect small forest fire targets while ensuring efficient inference on devices with constrained computational resources.CCLNet employs a three-stage network architecture.Its key components include three modules.C3F-Convolutional Gated Linear Unit(C3F-CGLU)performs selective local feature extraction while preserving fine-grained high-frequency flame details.Context-Guided Feature Fusion Module(CGFM)replaces plain concatenation with triplet-attention interactions to emphasize subtle flame patterns.Lightweight Shared Convolution with Separated Batch Normalization Detection(LSCSBD)reduces parameters through separated batch normalization while maintaining scale-specific statistics.We build TF-11K,an 11,139-image dataset combining 9139 self-collected UAV images from subtropical forests and 2000 re-annotated frames from the FLAME dataset.On TF-11K,CCLNet attains 85.8%mAP@0.5,45.5%mean Average Precision(mAP)@[0.5:0.95],87.4%precision,and 79.1%recall with 2.21 M parameters and 5.7 Giga Floating-point Operations Per Second(GFLOPs).The ablation study confirms that each module contributes to both accuracy and efficiency.Cross-dataset evaluation on DFS yields 77.5%mAP@0.5 and 42.3%mAP@[0.5:0.95],indicating good generalization to unseen scenes.These results suggest that CCLNet offers a practical balance between accuracy and speed for small-target forest fire monitoring with UAVs.
基金supported by the National Natural Science Foundation of China(72571094,72271076,71871079)。
文摘Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always influenced by external factors,making the original path planning solution ineffective.In this paper,the multi-depot multi-UAV path planning problem with uncertain flight time is modeled as a robust optimization model with a budget uncertainty set.Then,the robust optimization model is transformed into a mixed integer linear programming model by the strong duality theorem,which makes the problem easy to solve.To effectively solve large-scale instances,a simulated annealing algorithm with a robust feasibility check(SA-RFC)is developed.The numerical experiment shows that the SA-RFC can find high-quality solutions within a few seconds.Moreover,the effect of the task location distribution,depot counts,and variations in robustness parameters on the robust optimization solution is analyzed by using Monte Carlo experiments.The results demonstrate that the proposed robust model can effectively reduce the risk of the UAV failing to return to the depot without significantly compromising the profit.
文摘With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.
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.
基金support from the National Natural Science Foundation of China(Grant Nos.42177142 and 52378477)the Key Research and Development Program of Shaanxi(Grant No.2023-YBSF-486).
文摘The identification of rock mass hazard sources is fundamental for preventing rockfall and landslide disasters in mountainous regions,with rock mass structural characteristics playing a vital role in hazard assessment.In this study,terrestrial laser scanning(TLS)and unmanned aerial vehicle(UAV)technologies were integrated to enhance the evaluation methodology for rock mass hazard sources,focusing on the Sichuan Yanjiang Expressway project in China.The findings demonstrate that TLS-UAV technology enhanced both spatial coverage and data density in slope modeling.Through integrated algorithmic analysis,rock discontinuities within heterogeneous datasets were systematically identified,enabling quantitative extraction and statistical analysis of key geometric parameters,including orientation,trace length,spacing,and roughness.Furthermore,quantitative models were developed for cohesion,friction angle and the morphology parameter M of in situ discontinuities,respectively,facilitating efficient mechanical parameter acquisition.A novel rock mass hazard index(RHI)was developed incorporating discontinuity geometric rating(DGR),discontinuity mechanical rating(DMR),and slope mass rating(SMR).Field validation confirmed the methodology's effectiveness in evaluating risk levels and spatial heterogeneity of rock mass hazard sources,revealing the contribution of different discontinuity sets to the rock mass hazard and identifying the primary discontinuity sets controlling instability mechanisms.This study is of great significance for evaluating discontinuity-controlled rock mass hazard sources and preventing rockfall disasters.
基金supported by the National Natural Science Foundation of China(62073267,61903305)the Fundamental Research Funds for the Central Universities(HXGJXM202214)。
文摘For mission-oriented unmanned aerial vehicle(UAV)swarms,mission capability assessment provides an important reference in the design and development process,and is a precondition for mission success.For this multi-criteria decisionmaking(MCDM)problem,the current literature lacks a way to unambiguously present criteria and the popular fuzzy analytic network process(ANP)approaches neglect the hesitancy of subjective judgments.To fill these research gaps,an MCDM method based on unified architecture framework(UAF)and interval-valued spherical fuzzy ANP(IVSF-ANP)is proposed in this paper.Firstly,selected viewpoints in UAF are extended to construct criteria models with standardized representation.Secondly,interval-valued spherical fuzzy sets are introduced to ANP to weight interdependent criteria,handling fuzziness and hesitancy in pairwise comparisons.A method of adjusting weights of experts based on their decision similarities is also included in this process to reduce ambiguity brought by multiple experts.Next,performance characteristics are non-linearly transformed regarding to expectations to get final results.This proposition is applied to assess the mission capability of UAV swarms to search and strike surface vessels.Comparative analysis shows that the proposed method is valid and reasonable.
