Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequent...Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.展开更多
The increasing presence of drones seen on the battlefields in modern conflicts poses new threats to manned military aircraft or rotorcraft.In order to assess this potential threat,this manuscript first summarizes all ...The increasing presence of drones seen on the battlefields in modern conflicts poses new threats to manned military aircraft or rotorcraft.In order to assess this potential threat,this manuscript first summarizes all confirmed and suspected collisions between drones and aerostructures and the damage resulting from these collisions.Furthermore,this manuscript reviews experimental and numerical investigations on collision of drones with aerostructures.Additionally,some light is shed onto current regulation for drone operations intended to avoid collisions between drones and aircraft.Whilst these regulatory measures can prevent commercial aircraft to collide with drones,the authors believe that there is an inherent threat for civil and military rotorcraft due to their structural design and the fact that it is not possible to completely separate the airspace between drone operations and rotorcraft operations,in particular in the context of rescue missions in an urban or hostile environment.Furthermore,the stealth capability of 5th generation fighters may be compromised by damage suffered from collision with drones.展开更多
To address the issue of neglecting scenarios involving joint operations and collaborative drone swarm operations in air combat target intent recognition.This paper proposes a transfer learning-based intention predicti...To address the issue of neglecting scenarios involving joint operations and collaborative drone swarm operations in air combat target intent recognition.This paper proposes a transfer learning-based intention prediction model for drone formation targets in air combat.This model recognizes the intentions of multiple aerial targets by extracting spatial features among the targets at each moment.Simulation results demonstrate that,compared to classical intention recognition models,the proposed model in this paper achieves higher accuracy in identifying the intentions of drone swarm targets in air combat scenarios.展开更多
Sleeping site selection is essential for understanding primate behavioral ecology and survival.Identifying where species sleep helps determine priority areas and critical resources for targeted conservation efforts.Ho...Sleeping site selection is essential for understanding primate behavioral ecology and survival.Identifying where species sleep helps determine priority areas and critical resources for targeted conservation efforts.However,observing sleeping sites at night is challenging,especially for species sensitive to human disturbance.Thermal infrared imaging(TIR)with drones is increasingly used for detecting and counting primates,yet it has not been utilized to investigate ecological strategies.This study investigates the sleeping site selection of the Critically Endangered black-shanked douc langur(Pygathrix nigripes)in Cát Tiên National Park,Vietnam.Our aim is to assess the feasibility of using a TIR drone to test sleeping site selection strategies in non-nesting primates,specifically examining hypotheses related to predation avoidance and food proximity.Between January and April 2023,we conducted 120 drone flights along 22 transects(~1-km long)and identified 114 sleeping sites via thermal imaging.We established 116 forest structure plots along 29 transects in non-selected sites and 65 plots within douc langur sleeping sites.Our observations reveal that douc langurs selected tall and large trees that may provide protection against predators.Additionally,they selected sleeping sites with increased access to food,such as Afzelia xylocarpa,which serves as a preferred food source during the dry season.These results highlight the effective use of TIR drones for studying douc langur sleeping site selection with minimal disturbance.Besides offering valuable insights into habitat selection and behavioral ecology for conservation,TIR drones hold great promise for the noninvasive and long-term monitoring of large-bodied arboreal species.展开更多
As commercial drone delivery becomes increasingly popular,the extension of the vehicle routing problem with drones(VRPD)is emerging as an optimization problem of inter-ests.This paper studies a variant of VRPD in mult...As commercial drone delivery becomes increasingly popular,the extension of the vehicle routing problem with drones(VRPD)is emerging as an optimization problem of inter-ests.This paper studies a variant of VRPD in multi-trip and multi-drop(VRP-mmD).The problem aims at making schedules for the trucks and drones such that the total travel time is minimized.This paper formulate the problem with a mixed integer program-ming model and propose a two-phase algorithm,i.e.,a parallel route construction heuristic(PRCH)for the first phase and an adaptive neighbor searching heuristic(ANSH)for the second phase.The PRCH generates an initial solution by con-currently assigning as many nodes as possible to the truck–drone pair to progressively reduce the waiting time at the rendezvous node in the first phase.Then the ANSH improves the initial solution by adaptively exploring the neighborhoods in the second phase.Numerical tests on some benchmark data are conducted to verify the performance of the algorithm.The results show that the proposed algorithm can found better solu-tions than some state-of-the-art methods for all instances.More-over,an extensive analysis highlights the stability of the pro-posed algorithm.展开更多
The growing field of urban monitoring has increasingly recognized the potential of utilizing autonomous technologies,particularly in drone swarms.The deployment of intelligent drone swarms offers promising solutions f...The growing field of urban monitoring has increasingly recognized the potential of utilizing autonomous technologies,particularly in drone swarms.The deployment of intelligent drone swarms offers promising solutions for enhancing the efficiency and scope of urban condition assessments.In this context,this paper introduces an innovative algorithm designed to navigate a swarm of drones through urban landscapes for monitoring tasks.The primary challenge addressed by the algorithm is coordinating drone movements from one location to another while circumventing obstacles,such as buildings.The algorithm incorporates three key components to optimize the obstacle detection,navigation,and energy efficiency within a drone swarm.First,the algorithm utilizes a method to calculate the position of a virtual leader,acting as a navigational beacon to influence the overall direction of the swarm.Second,the algorithm identifies observers within the swarm based on the current orientation.To further refine obstacle avoidance,the third component involves the calculation of angular velocity using fuzzy logic.This approach considers the proximity of detected obstacles through operational rangefinders and the target’s location,allowing for a nuanced and adaptable computation of angular velocity.The integration of fuzzy logic enables the drone swarm to adapt to diverse urban conditions dynamically,ensuring practical obstacle avoidance.The proposed algorithm demonstrates enhanced performance in the obstacle detection and navigation accuracy through comprehensive simulations.The results suggest that the intelligent obstacle avoidance algorithm holds promise for the safe and efficient deployment of autonomous mobile drones in urban monitoring applications.展开更多
Protection of urban critical infrastructures(CIs)from GPS-denied,bomb-carrying kamikaze drones(G-BKDs)is very challenging.Previous approaches based on drone jamming,spoofing,communication interruption and hijacking ca...Protection of urban critical infrastructures(CIs)from GPS-denied,bomb-carrying kamikaze drones(G-BKDs)is very challenging.Previous approaches based on drone jamming,spoofing,communication interruption and hijacking cannot be applied in the case under examination,since G-B-KDs are uncontrolled.On the other hand,drone capturing schemes and electromagnetic pulse(EMP)weapons seem to be effective.However,again,existing approaches present various limitations,while most of them do not examine the case of G-B-KDs.This paper,focuses on the aforementioned under-researched field,where the G-B-KD is confronted by two defensive drones.The first neutralizes and captures the kamikaze drone,while the second captures the bomb.Both defensive drones are equipped with a net-gun and an innovative algorithm,which,among others,estimates the locations of interception,using a real-world trajectory model.Additionally,one of the defensive drones is also equipped with an EMP weapon to damage the electronics equipment of the kamikaze drone and reduce the capturing time and the overall risk.Extensive simulated experiments and comparisons to state-of-art methods,reveal the advantages and limitations of the proposed approach.More specifically,compared to state-of-art,the proposed approach improves:(a)time to neutralize the target by at least 6.89%,(b)maximum number of missions by at least 1.27%and(c)total cost by at least 5.15%.展开更多
Drone-based small object detection is of great significance in practical applications such as military actions, disaster rescue, transportation, etc. However, the severe scale differences in objects captured by drones...Drone-based small object detection is of great significance in practical applications such as military actions, disaster rescue, transportation, etc. However, the severe scale differences in objects captured by drones and lack of detail information for small-scale objects make drone-based small object detection a formidable challenge. To address these issues, we first develop a mathematical model to explore how changing receptive fields impacts the polynomial fitting results. Subsequently, based on the obtained conclusions, we propose a simple but effective Hybrid Receptive Field Network (HRFNet), whose modules include Hybrid Feature Augmentation (HFA), Hybrid Feature Pyramid (HFP) and Dual Scale Head (DSH). Specifically, HFA employs parallel dilated convolution kernels of different sizes to extend shallow features with different receptive fields, committed to improving the multi-scale adaptability of the network;HFP enhances the perception of small objects by capturing contextual information across layers, while DSH reconstructs the original prediction head utilizing a set of high-resolution features and ultrahigh-resolution features. In addition, in order to train HRFNet, the corresponding dual-scale loss function is designed. Finally, comprehensive evaluation results on public benchmarks such as VisDrone-DET and TinyPerson demonstrate the robustness of the proposed method. Most impressively, the proposed HRFNet achieves a mAP of 51.0 on VisDrone-DET with 29.3 M parameters, which outperforms the extant state-of-the-art detectors. HRFNet also performs excellently in complex scenarios captured by drones, achieving the best performance on the CS-Drone dataset we built.展开更多
With the increasing global population and mounting pressures on agricultural production,precise pest monitoring has become a critical factor in ensuring food security.Traditional monitoring methods,often inefficient,s...With the increasing global population and mounting pressures on agricultural production,precise pest monitoring has become a critical factor in ensuring food security.Traditional monitoring methods,often inefficient,struggle to meet the demands of modern agriculture.Drone remote sensing technology,leveraging its high efficiency and flexibility,demonstrates significant potential in pest monitoring.Equipped with multispectral,hyperspectral,and thermal infrared sensors,drones can rapidly cover large agricultural fields,capturing high-resolution imagery and data to detect spectral variations in crops.This enables effective differentiation between healthy and infested plants,facilitating early pest identification and targeted control.This paper systematically reviews the current applications of drone remote sensing technology in pest monitoring by examining different sensor types and their use in monitoring major crop pests and diseases.It also discusses existing challenges,aiming to provide insights and references for future research.展开更多
Object detection plays a critical role in drone imagery analysis,especially in remote sensing applications where accurate and efficient detection of small objects is essential.Despite significant advancements in drone...Object detection plays a critical role in drone imagery analysis,especially in remote sensing applications where accurate and efficient detection of small objects is essential.Despite significant advancements in drone imagery detection,most models still struggle with small object detection due to challenges such as object size,complex backgrounds.To address these issues,we propose a robust detection model based on You Only Look Once(YOLO)that balances accuracy and efficiency.The model mainly contains several major innovation:feature selection pyramid network,Inner-Shape Intersection over Union(ISIoU)loss function and small object detection head.To overcome the limitations of traditional fusion methods in handling multi-level features,we introduce a Feature Selection Pyramid Network integrated into the Neck component,which preserves shallow feature details critical for detecting small objects.Additionally,recognizing that deep network structures often neglect or degrade small object features,we design a specialized small object detection head in the shallow layers to enhance detection accuracy for these challenging targets.To effectively model both local and global dependencies,we introduce a Conv-Former module that simulates Transformer mechanisms using a convolutional structure,thereby improving feature enhancement.Furthermore,we employ ISIoU to address object imbalance and scale variation This approach accelerates model conver-gence and improves regression accuracy.Experimental results show that,compared to the baseline model,the proposed method significantly improves small object detection performance on the VisDrone2019 dataset,with mAP@50 increasing by 4.9%and mAP@50-95 rising by 6.7%.This model also outperforms other state-of-the-art algorithms,demonstrating its reliability and effectiveness in both small object detection and remote sensing image fusion tasks.展开更多
Inspection is a fundamental task for water plants,yet traditional methods are often labor-intensive,time-consuming,and costly.The rapid advancement of drone technology has significantly transformed environmental inspe...Inspection is a fundamental task for water plants,yet traditional methods are often labor-intensive,time-consuming,and costly.The rapid advancement of drone technology has significantly transformed environmental inspections,particularly in water plant assessments.Digital twins enhance modeling and simulation capabilities by integrating real-time data and feedback.This paper presents an intelligent water plant detection system based on YOLOv10 and drone technology.The system aims to monitor environmental conditions around water facilities and automatically identify anomalies in real time.The design utilizes dataset images of construction vehicles,maintenance hole covers,and pipe leaks collected from publicly accessible websites.The system integrates real-time drone inspection data into a digital twin platform for dynamic monitoring.展开更多
Communications system has a signifi-cant impact on both operational safety and logisti-cal efficiency within low-altitude drone logistics net-works.Aiming at providing a systematic investiga-tion of real-world communi...Communications system has a signifi-cant impact on both operational safety and logisti-cal efficiency within low-altitude drone logistics net-works.Aiming at providing a systematic investiga-tion of real-world communication requirements and challenges encountered in Meituan UAV’s daily oper-ations,this article first introduces the operational sce-narios within current drone logistics networks and an-alyzes the related communication requirements.Then,the current communication solution and its inherent bottlenecks are elaborated.Finally,this paper explores emerging technologies and examines their application prospects in drone logistics networks.展开更多
This paper discusses the design and software-in-theloop implementation of adaptive formation controllers for fixedwing unmanned aerial vehicles(UAVs) with parametric uncertainty in their structure, namely uncertain ma...This paper discusses the design and software-in-theloop implementation of adaptive formation controllers for fixedwing unmanned aerial vehicles(UAVs) with parametric uncertainty in their structure, namely uncertain mass and inertia. In fact, when aiming at autonomous flight, such parameters cannot assumed to be known as they might vary during the mission(e.g.depending on the payload). Modeling and autopilot design for such autonomous fixed-wing UAVs are presented. The modeling is implemented in Matlab, while the autopilot is based on ArduPilot, a popular open-source autopilot suite. Specifically, the ArduP ilot functionalities are emulated in Matlab according to the Ardupilot documentation and code, which allows us to perform software-in-the-loop simulations of teams of UAVs embedded with actual autopilot protocols. An overview of realtime path planning, trajectory tracking and formation control resulting from the proposed platform is given. The software-inthe-loop simulations show the capability of achieving different UAV formations while handling uncertain mass and inertia.展开更多
For the automatic tracking of unknown moving targets on the ground,most of the commonly used methods involve circling above the target.With such a tracking mode,there is a moving laser spot on the target,which will br...For the automatic tracking of unknown moving targets on the ground,most of the commonly used methods involve circling above the target.With such a tracking mode,there is a moving laser spot on the target,which will bring trouble for cooperative manned helicopters.In this paper,we propose a new way of tracking,where an unmanned aerial vehicle(UAV) circles on one side of the tracked target.A circular path algorithm is developed for monitoring the relative position between the UAV and the target considering the real-time range and the bearing angle.This can determine the center of the new circular path if the predicted range between the UAV and the target does not meet the monitoring requirements.A transition path algorithm is presented for planning the transition path between circular paths that constrain the turning radius of the UAV.The transition path algorithm can generate waypoints that meet the flight ability.In this paper,we analyze the entire method and detail the scope of applications.We formulate an observation angle as an evaluation index.A series of simulations and evaluation index comparisons verify the effectiveness of the proposed algorithms.展开更多
Solar drones have garnered considerably research attention in recent years due to their continuous cruising capability,and the feasibility of design schemes is sensitive to the weight of structure.Sandwich box beam co...Solar drones have garnered considerably research attention in recent years due to their continuous cruising capability,and the feasibility of design schemes is sensitive to the weight of structure.Sandwich box beam composed of carbon fiber and polymethacrylimide(PMI)foam is conducive to realize the lightweight of structure.In this study,a two-stage optimization design methodology for sandwich box beam is proposed.This methodology is primarily based on a low-order analytical method for evaluating stress/deflection and the linear buckling analysis method combined with experimental correction factor for predicting the buckling eigenvalues.Subsequently,a case study was conducted using an 18-m wingspan solar drone,where the results of mechanical test verified the optimization results.For validating the use of sandwich box beam in solar drones of other scales,additional analysis was conducted based on three aspects:(A)effects of stiffness and stability constraints on the design of sandwich box beam;(B)crucial role of the weight of foam inter layer and application scope of sandwich box beam;(C)best method to improve the buckling eigenvalue of sandwich box beam.Overall,the methodology and general rules presented in this paper can support the design of light wing beam for solar drones.展开更多
Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Fir...Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Firstly,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local oscillation.Then,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is proposed.Finally,several simulations are carried out to illustrate superiority and effectiveness.展开更多
This paper tackles the formation-containment control problem of fixed-wing unmanned aerial vehicle(UAV)swarm with model uncertainties for dynamic target tracking in three-dimensional space in the faulty case of UAVs’...This paper tackles the formation-containment control problem of fixed-wing unmanned aerial vehicle(UAV)swarm with model uncertainties for dynamic target tracking in three-dimensional space in the faulty case of UAVs’actuator and sensor.The fixed-wing UAV swarm under consideration is organized as a“multi-leader-multi-follower”structure,in which only several leaders can obtain the dynamic target information while others only receive the neighbors’information through the communication network.To simultaneously realize the formation,containment,and dynamic target tracking,a two-layer control framework is adopted to decouple the problem into two subproblems:reference trajectory generation and trajectory tracking.In the upper layer,a distributed finite-time estimator(DFTE)is proposed to generate each UAV’s reference trajectory in accordance with the control objective.Subsequently,a distributed composite robust fault-tolerant trajectory tracking controller is developed in the lower layer,where a novel adaptive extended super-twisting(AESTW)algorithm with a finite-time extended state observer(FTESO)is involved in solving the robust trajectory tracking control problem under model uncertainties,actuator,and sensor faults.The proposed controller simultaneously guarantees rapidness and enhances the system’s robustness with fewer chattering effects.Finally,corresponding simulations are carried out to demonstrate the effectiveness and competitiveness of the proposed two-layer fault-tolerant cooperative control scheme.展开更多
基金supported by the National Natural Science Foundation of China(No.62203256)。
文摘Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.
文摘The increasing presence of drones seen on the battlefields in modern conflicts poses new threats to manned military aircraft or rotorcraft.In order to assess this potential threat,this manuscript first summarizes all confirmed and suspected collisions between drones and aerostructures and the damage resulting from these collisions.Furthermore,this manuscript reviews experimental and numerical investigations on collision of drones with aerostructures.Additionally,some light is shed onto current regulation for drone operations intended to avoid collisions between drones and aircraft.Whilst these regulatory measures can prevent commercial aircraft to collide with drones,the authors believe that there is an inherent threat for civil and military rotorcraft due to their structural design and the fact that it is not possible to completely separate the airspace between drone operations and rotorcraft operations,in particular in the context of rescue missions in an urban or hostile environment.Furthermore,the stealth capability of 5th generation fighters may be compromised by damage suffered from collision with drones.
文摘To address the issue of neglecting scenarios involving joint operations and collaborative drone swarm operations in air combat target intent recognition.This paper proposes a transfer learning-based intention prediction model for drone formation targets in air combat.This model recognizes the intentions of multiple aerial targets by extracting spatial features among the targets at each moment.Simulation results demonstrate that,compared to classical intention recognition models,the proposed model in this paper achieves higher accuracy in identifying the intentions of drone swarm targets in air combat scenarios.
基金financial support of the Belgian National Fund for Scientific Research(FNRS)the Duesberg Foundation,and the University of Liège.
文摘Sleeping site selection is essential for understanding primate behavioral ecology and survival.Identifying where species sleep helps determine priority areas and critical resources for targeted conservation efforts.However,observing sleeping sites at night is challenging,especially for species sensitive to human disturbance.Thermal infrared imaging(TIR)with drones is increasingly used for detecting and counting primates,yet it has not been utilized to investigate ecological strategies.This study investigates the sleeping site selection of the Critically Endangered black-shanked douc langur(Pygathrix nigripes)in Cát Tiên National Park,Vietnam.Our aim is to assess the feasibility of using a TIR drone to test sleeping site selection strategies in non-nesting primates,specifically examining hypotheses related to predation avoidance and food proximity.Between January and April 2023,we conducted 120 drone flights along 22 transects(~1-km long)and identified 114 sleeping sites via thermal imaging.We established 116 forest structure plots along 29 transects in non-selected sites and 65 plots within douc langur sleeping sites.Our observations reveal that douc langurs selected tall and large trees that may provide protection against predators.Additionally,they selected sleeping sites with increased access to food,such as Afzelia xylocarpa,which serves as a preferred food source during the dry season.These results highlight the effective use of TIR drones for studying douc langur sleeping site selection with minimal disturbance.Besides offering valuable insights into habitat selection and behavioral ecology for conservation,TIR drones hold great promise for the noninvasive and long-term monitoring of large-bodied arboreal species.
文摘As commercial drone delivery becomes increasingly popular,the extension of the vehicle routing problem with drones(VRPD)is emerging as an optimization problem of inter-ests.This paper studies a variant of VRPD in multi-trip and multi-drop(VRP-mmD).The problem aims at making schedules for the trucks and drones such that the total travel time is minimized.This paper formulate the problem with a mixed integer program-ming model and propose a two-phase algorithm,i.e.,a parallel route construction heuristic(PRCH)for the first phase and an adaptive neighbor searching heuristic(ANSH)for the second phase.The PRCH generates an initial solution by con-currently assigning as many nodes as possible to the truck–drone pair to progressively reduce the waiting time at the rendezvous node in the first phase.Then the ANSH improves the initial solution by adaptively exploring the neighborhoods in the second phase.Numerical tests on some benchmark data are conducted to verify the performance of the algorithm.The results show that the proposed algorithm can found better solu-tions than some state-of-the-art methods for all instances.More-over,an extensive analysis highlights the stability of the pro-posed algorithm.
文摘The growing field of urban monitoring has increasingly recognized the potential of utilizing autonomous technologies,particularly in drone swarms.The deployment of intelligent drone swarms offers promising solutions for enhancing the efficiency and scope of urban condition assessments.In this context,this paper introduces an innovative algorithm designed to navigate a swarm of drones through urban landscapes for monitoring tasks.The primary challenge addressed by the algorithm is coordinating drone movements from one location to another while circumventing obstacles,such as buildings.The algorithm incorporates three key components to optimize the obstacle detection,navigation,and energy efficiency within a drone swarm.First,the algorithm utilizes a method to calculate the position of a virtual leader,acting as a navigational beacon to influence the overall direction of the swarm.Second,the algorithm identifies observers within the swarm based on the current orientation.To further refine obstacle avoidance,the third component involves the calculation of angular velocity using fuzzy logic.This approach considers the proximity of detected obstacles through operational rangefinders and the target’s location,allowing for a nuanced and adaptable computation of angular velocity.The integration of fuzzy logic enables the drone swarm to adapt to diverse urban conditions dynamically,ensuring practical obstacle avoidance.The proposed algorithm demonstrates enhanced performance in the obstacle detection and navigation accuracy through comprehensive simulations.The results suggest that the intelligent obstacle avoidance algorithm holds promise for the safe and efficient deployment of autonomous mobile drones in urban monitoring applications.
基金supported in part by Interbit Research and in part by the European Union under(Grant No.2021-1-EL01-KA220-VET-000028082).
文摘Protection of urban critical infrastructures(CIs)from GPS-denied,bomb-carrying kamikaze drones(G-BKDs)is very challenging.Previous approaches based on drone jamming,spoofing,communication interruption and hijacking cannot be applied in the case under examination,since G-B-KDs are uncontrolled.On the other hand,drone capturing schemes and electromagnetic pulse(EMP)weapons seem to be effective.However,again,existing approaches present various limitations,while most of them do not examine the case of G-B-KDs.This paper,focuses on the aforementioned under-researched field,where the G-B-KD is confronted by two defensive drones.The first neutralizes and captures the kamikaze drone,while the second captures the bomb.Both defensive drones are equipped with a net-gun and an innovative algorithm,which,among others,estimates the locations of interception,using a real-world trajectory model.Additionally,one of the defensive drones is also equipped with an EMP weapon to damage the electronics equipment of the kamikaze drone and reduce the capturing time and the overall risk.Extensive simulated experiments and comparisons to state-of-art methods,reveal the advantages and limitations of the proposed approach.More specifically,compared to state-of-art,the proposed approach improves:(a)time to neutralize the target by at least 6.89%,(b)maximum number of missions by at least 1.27%and(c)total cost by at least 5.15%.
基金supported by the National Natural Science Foundation of China(Nos.62276204 and 62203343)the Fundamental Research Funds for the Central Universities(No.YJSJ24011)+1 种基金the Natural Science Basic Research Program of Shanxi,China(Nos.2022JM-340 and 2023-JC-QN-0710)the China Postdoctoral Science Foundation(Nos.2020T130494 and 2018M633470).
文摘Drone-based small object detection is of great significance in practical applications such as military actions, disaster rescue, transportation, etc. However, the severe scale differences in objects captured by drones and lack of detail information for small-scale objects make drone-based small object detection a formidable challenge. To address these issues, we first develop a mathematical model to explore how changing receptive fields impacts the polynomial fitting results. Subsequently, based on the obtained conclusions, we propose a simple but effective Hybrid Receptive Field Network (HRFNet), whose modules include Hybrid Feature Augmentation (HFA), Hybrid Feature Pyramid (HFP) and Dual Scale Head (DSH). Specifically, HFA employs parallel dilated convolution kernels of different sizes to extend shallow features with different receptive fields, committed to improving the multi-scale adaptability of the network;HFP enhances the perception of small objects by capturing contextual information across layers, while DSH reconstructs the original prediction head utilizing a set of high-resolution features and ultrahigh-resolution features. In addition, in order to train HRFNet, the corresponding dual-scale loss function is designed. Finally, comprehensive evaluation results on public benchmarks such as VisDrone-DET and TinyPerson demonstrate the robustness of the proposed method. Most impressively, the proposed HRFNet achieves a mAP of 51.0 on VisDrone-DET with 29.3 M parameters, which outperforms the extant state-of-the-art detectors. HRFNet also performs excellently in complex scenarios captured by drones, achieving the best performance on the CS-Drone dataset we built.
文摘With the increasing global population and mounting pressures on agricultural production,precise pest monitoring has become a critical factor in ensuring food security.Traditional monitoring methods,often inefficient,struggle to meet the demands of modern agriculture.Drone remote sensing technology,leveraging its high efficiency and flexibility,demonstrates significant potential in pest monitoring.Equipped with multispectral,hyperspectral,and thermal infrared sensors,drones can rapidly cover large agricultural fields,capturing high-resolution imagery and data to detect spectral variations in crops.This enables effective differentiation between healthy and infested plants,facilitating early pest identification and targeted control.This paper systematically reviews the current applications of drone remote sensing technology in pest monitoring by examining different sensor types and their use in monitoring major crop pests and diseases.It also discusses existing challenges,aiming to provide insights and references for future research.
文摘Object detection plays a critical role in drone imagery analysis,especially in remote sensing applications where accurate and efficient detection of small objects is essential.Despite significant advancements in drone imagery detection,most models still struggle with small object detection due to challenges such as object size,complex backgrounds.To address these issues,we propose a robust detection model based on You Only Look Once(YOLO)that balances accuracy and efficiency.The model mainly contains several major innovation:feature selection pyramid network,Inner-Shape Intersection over Union(ISIoU)loss function and small object detection head.To overcome the limitations of traditional fusion methods in handling multi-level features,we introduce a Feature Selection Pyramid Network integrated into the Neck component,which preserves shallow feature details critical for detecting small objects.Additionally,recognizing that deep network structures often neglect or degrade small object features,we design a specialized small object detection head in the shallow layers to enhance detection accuracy for these challenging targets.To effectively model both local and global dependencies,we introduce a Conv-Former module that simulates Transformer mechanisms using a convolutional structure,thereby improving feature enhancement.Furthermore,we employ ISIoU to address object imbalance and scale variation This approach accelerates model conver-gence and improves regression accuracy.Experimental results show that,compared to the baseline model,the proposed method significantly improves small object detection performance on the VisDrone2019 dataset,with mAP@50 increasing by 4.9%and mAP@50-95 rising by 6.7%.This model also outperforms other state-of-the-art algorithms,demonstrating its reliability and effectiveness in both small object detection and remote sensing image fusion tasks.
文摘Inspection is a fundamental task for water plants,yet traditional methods are often labor-intensive,time-consuming,and costly.The rapid advancement of drone technology has significantly transformed environmental inspections,particularly in water plant assessments.Digital twins enhance modeling and simulation capabilities by integrating real-time data and feedback.This paper presents an intelligent water plant detection system based on YOLOv10 and drone technology.The system aims to monitor environmental conditions around water facilities and automatically identify anomalies in real time.The design utilizes dataset images of construction vehicles,maintenance hole covers,and pipe leaks collected from publicly accessible websites.The system integrates real-time drone inspection data into a digital twin platform for dynamic monitoring.
基金supported by Shenzhen Science and Technology Program(KJZD20230923115210021)。
文摘Communications system has a signifi-cant impact on both operational safety and logisti-cal efficiency within low-altitude drone logistics net-works.Aiming at providing a systematic investiga-tion of real-world communication requirements and challenges encountered in Meituan UAV’s daily oper-ations,this article first introduces the operational sce-narios within current drone logistics networks and an-alyzes the related communication requirements.Then,the current communication solution and its inherent bottlenecks are elaborated.Finally,this paper explores emerging technologies and examines their application prospects in drone logistics networks.
基金supported by the Fundamental Research Funds for the Central Universities(4007019109)(RECON-STRUCT)the Special Guiding Funds for Double First-class(4007019201)the Joint TU Delft-CSSC Project ‘Multi-agent Coordination with Networked Constraints’(MULTI-COORD)
文摘This paper discusses the design and software-in-theloop implementation of adaptive formation controllers for fixedwing unmanned aerial vehicles(UAVs) with parametric uncertainty in their structure, namely uncertain mass and inertia. In fact, when aiming at autonomous flight, such parameters cannot assumed to be known as they might vary during the mission(e.g.depending on the payload). Modeling and autopilot design for such autonomous fixed-wing UAVs are presented. The modeling is implemented in Matlab, while the autopilot is based on ArduPilot, a popular open-source autopilot suite. Specifically, the ArduP ilot functionalities are emulated in Matlab according to the Ardupilot documentation and code, which allows us to perform software-in-the-loop simulations of teams of UAVs embedded with actual autopilot protocols. An overview of realtime path planning, trajectory tracking and formation control resulting from the proposed platform is given. The software-inthe-loop simulations show the capability of achieving different UAV formations while handling uncertain mass and inertia.
基金the Deanship of Scientific Research at King Saud University through research group number(RG-1440-048)。
文摘For the automatic tracking of unknown moving targets on the ground,most of the commonly used methods involve circling above the target.With such a tracking mode,there is a moving laser spot on the target,which will bring trouble for cooperative manned helicopters.In this paper,we propose a new way of tracking,where an unmanned aerial vehicle(UAV) circles on one side of the tracked target.A circular path algorithm is developed for monitoring the relative position between the UAV and the target considering the real-time range and the bearing angle.This can determine the center of the new circular path if the predicted range between the UAV and the target does not meet the monitoring requirements.A transition path algorithm is presented for planning the transition path between circular paths that constrain the turning radius of the UAV.The transition path algorithm can generate waypoints that meet the flight ability.In this paper,we analyze the entire method and detail the scope of applications.We formulate an observation angle as an evaluation index.A series of simulations and evaluation index comparisons verify the effectiveness of the proposed algorithms.
文摘Solar drones have garnered considerably research attention in recent years due to their continuous cruising capability,and the feasibility of design schemes is sensitive to the weight of structure.Sandwich box beam composed of carbon fiber and polymethacrylimide(PMI)foam is conducive to realize the lightweight of structure.In this study,a two-stage optimization design methodology for sandwich box beam is proposed.This methodology is primarily based on a low-order analytical method for evaluating stress/deflection and the linear buckling analysis method combined with experimental correction factor for predicting the buckling eigenvalues.Subsequently,a case study was conducted using an 18-m wingspan solar drone,where the results of mechanical test verified the optimization results.For validating the use of sandwich box beam in solar drones of other scales,additional analysis was conducted based on three aspects:(A)effects of stiffness and stability constraints on the design of sandwich box beam;(B)crucial role of the weight of foam inter layer and application scope of sandwich box beam;(C)best method to improve the buckling eigenvalue of sandwich box beam.Overall,the methodology and general rules presented in this paper can support the design of light wing beam for solar drones.
文摘Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Firstly,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local oscillation.Then,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is proposed.Finally,several simulations are carried out to illustrate superiority and effectiveness.
基金the National Natural Science Foundation of China(61933010)the Natural Science Basic Research Plan in Shaanxi Province of China(2023-JC-QN-0733).
文摘This paper tackles the formation-containment control problem of fixed-wing unmanned aerial vehicle(UAV)swarm with model uncertainties for dynamic target tracking in three-dimensional space in the faulty case of UAVs’actuator and sensor.The fixed-wing UAV swarm under consideration is organized as a“multi-leader-multi-follower”structure,in which only several leaders can obtain the dynamic target information while others only receive the neighbors’information through the communication network.To simultaneously realize the formation,containment,and dynamic target tracking,a two-layer control framework is adopted to decouple the problem into two subproblems:reference trajectory generation and trajectory tracking.In the upper layer,a distributed finite-time estimator(DFTE)is proposed to generate each UAV’s reference trajectory in accordance with the control objective.Subsequently,a distributed composite robust fault-tolerant trajectory tracking controller is developed in the lower layer,where a novel adaptive extended super-twisting(AESTW)algorithm with a finite-time extended state observer(FTESO)is involved in solving the robust trajectory tracking control problem under model uncertainties,actuator,and sensor faults.The proposed controller simultaneously guarantees rapidness and enhances the system’s robustness with fewer chattering effects.Finally,corresponding simulations are carried out to demonstrate the effectiveness and competitiveness of the proposed two-layer fault-tolerant cooperative control scheme.
