[Objectives]To investigate the effects of silicon fertilizer spraying on the growth,yield,quality,and overall benefits of rice cultivation.[Methods]A systematic experiment involving the single-spray multi-promotion te...[Objectives]To investigate the effects of silicon fertilizer spraying on the growth,yield,quality,and overall benefits of rice cultivation.[Methods]A systematic experiment involving the single-spray multi-promotion technology of silicon fertilizer via unmanned aerial vehicles(UAVs)was conducted in three representative rice-growing areas:Ma'an Town,Shuikou Subdistrict,and Luzhou Town.[Results]The spraying of silicon fertilizer markedly enhanced the root development of rice,resulting in increased tiller number,plant height,stem thickness,panicle length,and 1000-grain weight,thereby effectively improving both yield and quality.This treatment exerted six primary beneficial effects:promoting robust and stable seedling growth,enhancing stress resistance,reducing reliance on chemical fertilizers,improving quality,increasing economic benefits,and significantly advancing ecological and social benefits.[Conclusions]The application of silicon fertilizer through spraying is an effective agronomic practice that simultaneously promotes increased rice yield,improved quality,enhanced efficiency,and the sustainable development of resources and the environment.展开更多
To overcome the limitations of conventional approaches that adopt monolithic architectures and overlook critical dynamic interactions in evaluating combat effectiveness and subsystem contributions within amphibious op...To overcome the limitations of conventional approaches that adopt monolithic architectures and overlook critical dynamic interactions in evaluating combat effectiveness and subsystem contributions within amphibious operations,this paper proposes an integrated framework combining complex system network modeling with dynamic adversarial simulation for evaluating mission-critical system-of-systems(SoS).Specifically,the contribution rate of unmanned aerial vehicles(UAVs)to the amphibious joint landing SoS(AJLSoS)is quantified.Firstly,a standardized network topology model is developed using operation loop theory,systematically characterizing node functionalities and their interdependencies.Secondly,the ideal Lanchester equation is augmented according to the model’s static operational capability,and an amphibious operational simulation model is constructed based on the modified equation,enabling dynamic simulation of force attrition and engagement duration as key performance indicators of AJLSoS.To validate the theoretical framework,a battalion-level amphibious campaign scenario is developed to compute effectiveness metrics across multiple control scenarios and the contribution rate of UAVs to AJLSoS is analyzed.This study not only provides actionable insights for operational mission planning of UAVs in the context of amphibious operations but also demonstrates high adaptability to diverse operational contexts.展开更多
Benefitting from UAVs’characteristics of flexible deployment and controllable movement in 3D space,odor source localization with multiple UAVs has been a hot research area in recent years.Considering the limited reso...Benefitting from UAVs’characteristics of flexible deployment and controllable movement in 3D space,odor source localization with multiple UAVs has been a hot research area in recent years.Considering the limited resources and insufficient battery capacities of UAVs,it is necessary to fast locate the odor source with low-complexity computation and minimal interaction under complicated environmental states.To this end,we propose a multi-UAV collaboration based odor source localization(MUC-OSL)method,where source estimation and UAV navigation are iteratively performed,aiming to accelerate the searching process and reduce the resource consumption of UAVs.Specifically,in the source estimation phase,we present a collaborative particle filter algorithm on the basis of UAVs’cognitive difference and collaborative information to improve source estimation accuracy.In the following navigation phase,an adaptive path planning algorithm is designed based on partially observable Markov decision process to distributedly determine the subsequent flying direction and moving steps of each UAV.The results of experiments conducted on two simulation platforms demonstrate that MUC-OSL outperforms existing efforts in terms of mean search time and success rate,and effectively reduces the resource consumption of UAVs.展开更多
The Internet of Unmanned Aerial Vehicles(I-UAVs)is expected to execute latency-sensitive tasks,but limited by co-channel interference and malicious jamming.In the face of unknown prior environmental knowledge,defendin...The Internet of Unmanned Aerial Vehicles(I-UAVs)is expected to execute latency-sensitive tasks,but limited by co-channel interference and malicious jamming.In the face of unknown prior environmental knowledge,defending against jamming and interference through spectrum allocation becomes challenging,especially when each UAV pair makes decisions independently.In this paper,we propose a cooperative multi-agent reinforcement learning(MARL)-based anti-jamming framework for I-UAVs,enabling UAV pairs to learn their own policies cooperatively.Specifically,we first model the problem as a modelfree multi-agent Markov decision process(MAMDP)to maximize the long-term expected system throughput.Then,for improving the exploration of the optimal policy,we resort to optimizing a MARL objective function with a mutual-information(MI)regularizer between states and actions,which can dynamically assign the probability for actions frequently used by the optimal policy.Next,through sharing their current channel selections and local learning experience(their soft Q-values),the UAV pairs can learn their own policies cooperatively relying on only preceding observed information and predicting others’actions.Our simulation results show that for both sweep jamming and Markov jamming patterns,the proposed scheme outperforms the benchmarkers in terms of throughput,convergence and stability for different numbers of jammers,channels and UAV pairs.展开更多
The estimation of orientation parameters and correction of lens distortion are crucial problems in the field of Unmanned Aerial Vehicles(UAVs)photogrammetry.In recent years,the utilization of UAVs for aerial photogram...The estimation of orientation parameters and correction of lens distortion are crucial problems in the field of Unmanned Aerial Vehicles(UAVs)photogrammetry.In recent years,the utilization of UAVs for aerial photogrammetry has witnessed a surge in popularity.Typically,UAVs are equipped with low-cost non-metric cameras and a Position and Orientation System(POS).Unfortunately,the Interior Orientation Parameters(IOPs)of the non-metric cameras are not fixed.Whether the lens distortions are large or small,they effect the image coordinates accordingly.Additionally,Inertial Measurement Units(IMUs)often have observation errors.To address these challenges and improve parameter estimation for UAVs Light Detection and Ranging(LiDAR)and photogrammetry,this paper analyzes the accuracy of POS observations obtained from Global Navigation Satellite System Real Time Kinematic(GNSS-RTK)and IMU data.A method that incorporates additional known conditions for parameter estimation,a series of algorithms to simultaneously solve for IOPs,Exterior Orientation Parameters(EOPs),and camera lens distortion correction parameters are proposed.Extensive experiments demonstrate that the coordinates measured by GNSS-RTK can be directly used as linear EOPs;however,angular EOP measurements from IMUs exhibit relatively large errors compared to adjustment results and require correction during the adjustment process.The IOPs of non-metric cameras vary slightly between images but need to be treated as unknown parameters in high precision applications.Furthermore,it is found that the Ebner systematic error model is sensitive to the choice of the magnification parameter of the photographic baseline length in images,it should be set as less than or equal to one third of the photographic baseline to ensure stable solutions.展开更多
Complex multi-area collaborative coverage path planning in dynamic environments poses a significant challenge for multi-fixed-wing UAVs(multi-UAV).This study establishes a comprehensive framework that incorporates UAV...Complex multi-area collaborative coverage path planning in dynamic environments poses a significant challenge for multi-fixed-wing UAVs(multi-UAV).This study establishes a comprehensive framework that incorporates UAV capabilities,terrain,complex areas,and mission dynamics.A novel dynamic collaborative path planning algorithm is introduced,designed to ensure complete coverage of designated areas.This algorithm meticulously optimizes the operation,entry,and transition paths for each UAV,while also establishing evaluation metrics to refine coverage sequences for each area.Additionally,a three-dimensional path is computed utilizing an altitude descent method,effectively integrating twodimensional coverage paths with altitude constraints.The efficacy of the proposed approach is validated through digital simulations and mixed-reality semi-physical experiments across a variety of dynamic scenarios,including both single-area and multi-area coverage by multi-UAV.Results show that the coverage paths generated by this method significantly reduce both computation time and path length,providing a reliable solution for dynamic multi-UAV mission planning in semi-physical environments.展开更多
The environment of low-altitude urban airspace is complex and variable due to numerous obstacles,non-cooperative aircraft,and birds.Unmanned Aerial Vehicles(UAVs)leveraging environmental information to achieve three-d...The environment of low-altitude urban airspace is complex and variable due to numerous obstacles,non-cooperative aircraft,and birds.Unmanned Aerial Vehicles(UAVs)leveraging environmental information to achieve three-dimension collision-free trajectory planning is the prerequisite to ensure airspace security.However,the timely information of surrounding situation is difficult to acquire by UAVs,which further brings security risks.As a mature technology leveraged in traditional civil aviation,the Automatic Dependent Surveillance-Broadcast(ADS-B)realizes continuous surveillance of the information of aircraft.Consequently,we leverage ADS-B for surveillance and information broadcasting,and divide the aerial airspace into multiple sub-airspaces to improve flight safety in UAV trajectory planning.In detail,we propose the secure Sub-airSpaces Planning(SSP)algorithm and Particle Swarm Optimization Rapidly-exploring Random Trees(PSO-RRT)algorithm for the UAV trajectory planning in law-altitude airspace.The performance of the proposed algorithm is verified by simulations and the results show that SSP reduces both the maximum number of UAVs in the sub-airspace and the length of the trajectory,and PSO-RRT reduces the cost of UAV trajectory in the sub-airspace.展开更多
This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary obj...This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.展开更多
The unmanned aerial vehicle(UAV)images captured under low-light conditions are often suffering from noise and uneven illumination.To address these issues,we propose a low-light image enhancement algorithm for UAV imag...The unmanned aerial vehicle(UAV)images captured under low-light conditions are often suffering from noise and uneven illumination.To address these issues,we propose a low-light image enhancement algorithm for UAV images,which is inspired by the Retinex theory and guided by a light weighted map.Firstly,we propose a new network for reflectance component processing to suppress the noise in images.Secondly,we construct an illumination enhancement module that uses a light weighted map to guide the enhancement process.Finally,the processed reflectance and illumination components are recombined to obtain the enhancement results.Experimental results show that our method can suppress the noise in images while enhancing image brightness,and prevent over enhancement in bright regions.Code and data are available at https://gitee.com/baixiaotong2/uav-images.git.展开更多
The design of unmanned aerial vehicles(UAVs)revolves around the careful selection of materials that are both lightweight and robust.Carbon fiber-reinforced polymer(CFRP)emerged as an ideal option for wing construction...The design of unmanned aerial vehicles(UAVs)revolves around the careful selection of materials that are both lightweight and robust.Carbon fiber-reinforced polymer(CFRP)emerged as an ideal option for wing construction,with its mechanical qualities thoroughly investigated.In this study,we developed and optimized a conceptual UAV wing to withstand structural loads by establishing progressive composite stacking sequences,and we conducted a series of experimental characterizations on the resulting material.In the optimization phase,the objective was defined as weight reduction,while the Hashin damage criterion was established as the constraint for the optimization process.The optimization algorithm adaptively monitors regional damage criterion values,implementing necessary adjustments to facilitate the mitigation process in a cost-effective manner.Optimization of the analytical model using Simulia Abaqus~(TM)and a Python-based user-defined sub-routine resulted in a 34.7%reduction in the wing's structural weight after 45 iterative rounds.Then,the custom-developed optimization algorithm was compared with a genetic algorithm optimization.This comparison has demonstrated that,although the genetic algorithm explores numerous possibilities through hybridization,the custom-developed algorithm is more result-oriented and achieves optimization in a reduced number of steps.To validate the structural analysis,test specimens were fabricated from the wing's most critically loaded segment,utilizing the identical stacking sequence employed in the optimization studies.Rigorous mechanical testing revealed unexpectedly high compressive strength,while tensile and bending strengths fell within expected ranges.All observed failure loads remained within the established safety margins,thereby confirming the reliability of the analytical predictions.展开更多
During flight operations,quadrotor UAVs are susceptible to interference from environmental factors such as wind gusts,battery depletion,and obstacles,which may compromise flight stability.This study proposes a fuzzy a...During flight operations,quadrotor UAVs are susceptible to interference from environmental factors such as wind gusts,battery depletion,and obstacles,which may compromise flight stability.This study proposes a fuzzy adaptive PID controller(Fuzzy PID)combining PID control with fuzzy logic to achieve self-adaptive adjustment of PID parameters in UAV flight control systems,thereby enhancing system robustness.A quadrotor UAV control model was developed in Simulink,and a Fuzzy PID control system was constructed by integrating fuzzy control logic for simulation and experimental validation.Test results demonstrate that UAVs governed by Fuzzy PID control exhibit faster regulation speed and improved stability when subjected to disturbances.展开更多
The“14th Five-Year Plan”and the Long-Range Objectives Through the Year 2035 propose to strengthen the construction of strategic emerging industrial clusters,promote the deep integration of the internet,big data,arti...The“14th Five-Year Plan”and the Long-Range Objectives Through the Year 2035 propose to strengthen the construction of strategic emerging industrial clusters,promote the deep integration of the internet,big data,artificial intelligence,blockchain technology,etc.with the real economy,facilitate the development of advanced manufacturing,and consider unmanned aerial vehicles(UAVs)as an important breakthrough,providing significant opportunities for the development of the UAV industry.Therefore,this article takes the current status of the UAV industry development as a starting point,analyzes the exploration and practice of the UAV development model based on the low-altitude economy,and discusses strategic suggestions to promote the development of UAVs empowered by the low-altitude economy.Through analysis,this article aims to provide theoretical references and practical guidance for promoting the sustainable development of the UAV industry under the wave of the low-altitude economy.展开更多
The“14th Five-Year Plan”and the Long-Range Objectives Through the Year 2035 propose to strengthen the construction of strategic emerging industrial clusters,promote the deep integration of the internet,big data,arti...The“14th Five-Year Plan”and the Long-Range Objectives Through the Year 2035 propose to strengthen the construction of strategic emerging industrial clusters,promote the deep integration of the internet,big data,artificial intelligence,blockchain technology,etc.with the real economy,facilitate the development of advanced manufacturing,and consider UAVs as an important breakthrough,providing significant opportunities for the development of the UAV industry.Therefore,this article takes the current status of the UAV industry development as a starting point,analyzes the exploration and practice of the UAV development model based on the low-altitude economy,and discusses strategic suggestions to promote the development of UAVs empowered by the low-altitude economy.Through analysis,this article aims to provide theoretical reference and practical guidance for promoting the sustainable development of the UAV industry under the wave of the low-altitude economy.展开更多
As battlefield scale enlarges,cross-platform collaborative combat provides an appealing paradigm for modern warfare.Complicated constraints and vast solution space pose great challenge for reasonable and efficient mis...As battlefield scale enlarges,cross-platform collaborative combat provides an appealing paradigm for modern warfare.Complicated constraints and vast solution space pose great challenge for reasonable and efficient mission planning,where path planning and target assignment are tightly coupled.In this paper,we focus on UAV mission planning under carrier delivery mode(e.g.,by aircraft carrier,ground vehicle,or transport aircraft) and design a three-layer hierarchical solution framework.In the first layer,we simultaneously determine delivery points and target set division by clustering.To address the safety concerns of radar risk and UAV endurance,an improved density peak clustering algorithm is developed by constraint fusio n.In the second layer,mission planning within each cluster is viewed as a coope rative multiple-task assignment problem.A hybrid heuristic algorithm that integrates a voting-based heuristic solution generation strategy(VHSG) and a stochastic variable neighborhood search(SVNS),called VHSG-SVNS,is proposed for rapid solution.Based on the results of the first two layers,the third layer transforms carrier path planning into a multiple-vehicle routing problem with time window.The cost between any two nodes is calculated by the A~* algorithm,and the genetic algorithm is then implemented to determine the global route.Finally,a practical mission scenario containing 200 targets is used to validate the effectiveness of the designed framework,where three layers cooperate well with each other to generate satisfactory combat scheduling.Comparisons are made in each layer to highlight optimum-seeking capability and efficiency of the proposed algorithms.Works done in this paper provide a simple but efficient solution framework for cross-platform cooperative mission planning problems,and can be potentially extended to other applications such as post-disaster search and rescue,forest surveillance and firefighting,logistics pick and delivery,etc.展开更多
For autonomous Unmanned Aerial Vehicles(UAVs)flying in real-world scenarios,time for path planning is always limited,which is a challenge known as the anytime problem.Anytime planners address this by finding a collisi...For autonomous Unmanned Aerial Vehicles(UAVs)flying in real-world scenarios,time for path planning is always limited,which is a challenge known as the anytime problem.Anytime planners address this by finding a collision-free path quickly and then improving it until time runs out,making UAVs more adaptable to different mission scenarios.However,current anytime algorithms based on A^(*)have insufficient control over the suboptimality bounds of paths and tend to lose their anytime properties in environments with large concave obstacles.This paper proposes a novel anytime path planning algorithm,Anytime Radiation A^(*)(ARa A^(*)),which can generate a series of suboptimal paths with improved bounds through decreasing search step sizes and can generate the optimal path when time is sufficient.The ARa A^(*)features two main innovations:an adaptive variable-step-size mechanism and elliptic constraints based on waypoints.The former helps achieve fast path searching in various environments.The latter allows ARa A^(*)to control the suboptimality bounds of paths and further enhance search efficiency.Simulation experiments show that the ARa A^(*)outperforms Anytime Repairing A^(*)(ARA^(*))and Anytime D^(*)(AD^(*))in controlling suboptimality bounds and planning time,especially in environments with large concave obstacles.Final flight experiments demonstrate that the paths planned by ARa A^(*)can ensure the safe flight of quadrotors.展开更多
To address the confrontation decision-making issues in multi-round air combat,a dynamic game decision method is proposed based on decision tree for the confrontation of unmanned aerial vehicle(UAV)air combat.Based on ...To address the confrontation decision-making issues in multi-round air combat,a dynamic game decision method is proposed based on decision tree for the confrontation of unmanned aerial vehicle(UAV)air combat.Based on game the-ory and the confrontation characteristics of air combat,a dynamic game process is constructed including the strategy sets,the situation information,and the maneuver decisions for both sides of air combat.By analyzing the UAV’s flight dyna-mics and the both sides’information,a payment matrix is estab-lished through the situation advantage function,performance advantage function,and profit function.Furthermore,the dynamic game decision problem is solved based on the linear induction method to obtain the Nash equilibrium solution,where the decision tree method is introduced to obtain the optimal maneuver decision,thereby improving the situation advantage in the next round of confrontation.According to the analysis,the simulation results for the confrontation scenarios of multi-round air combat are presented to verify the effectiveness and advan-tages of the proposed method.展开更多
As important infrastructure for airborne communication platforms,unmanned aerial vehicles(UAVs)are expected to become a key part of 6G wireless networks.Thus,modeling low-and medium-altitude propagation channels has a...As important infrastructure for airborne communication platforms,unmanned aerial vehicles(UAVs)are expected to become a key part of 6G wireless networks.Thus,modeling low-and medium-altitude propagation channels has attracted much attention.Air-to-ground(A2G)propagation channel models vary in different scenarios,requiring accurate models for designing and evaluating UAV communication links.Unlike terrestrial models,A2G channel models lack detailed investigation.Therefore,this paper provides an overview of existing A2G channel measurement campaigns,different types of A2G channel models for various environments,and future research directions for UAV airland channel modeling.This study focuses on the potential of millimeter-wave technology for UAV A2G channel modeling and highlights nonsuburban scenarios requiring consideration in future modeling efforts.展开更多
Path planning is crucial for autonomous flight of fixed-wing Unmanned Aerial Vehicles(UAVs).However,due to the high-speed flight and complex control of fixed-wing UAVs,ensuring the feasibility and safety of planned pa...Path planning is crucial for autonomous flight of fixed-wing Unmanned Aerial Vehicles(UAVs).However,due to the high-speed flight and complex control of fixed-wing UAVs,ensuring the feasibility and safety of planned paths in complex environments is challenging.This paper proposes a feasible path planning algorithm named Closed-loop Radial Ray A^(*)(CL-RaA^(*)).The core components of the CL-RaA^(*)include an adaptive variable-step-size path search and a just-in-time expansion primitive.The former enables fast path search in complex environments,while the latter ensures the feasibility of the generated paths.By integrating these two components and conducting safety checks on the trajectories to be expanded,the CL-RaA^(*)can rapidly generate safe and feasible paths that satisfy the differential constraints that comprehensively consider the dynamics and control characteristics of six-degree-of-freedom fixed-wing UAVs.The final performance tests and simulation validations demonstrate that the CL-RaA^(*)can generate safe and feasible paths in various environments.Compared to feasible path planning algorithms that use the rapidlyexploring random trees,the CL-RaA^(*)not only ensures deterministic planning results in the same scenarios but also generates smoother feasible paths for fixed-wing UAVs more efficiently.In environments with dense grid obstacles,the feasible paths generated by the CL-RaA^(*)are more conducive to UAV tracking compared to those planned using Dubins curves.展开更多
基金Supported by Huizhou Municipal Stable Grain and Oil Production Award and Subsidy Project"2025 Single-spray Multi-Promotion Project of Silicon Fertilizer on Rice Crops Using UAVs of Huicheng District".
文摘[Objectives]To investigate the effects of silicon fertilizer spraying on the growth,yield,quality,and overall benefits of rice cultivation.[Methods]A systematic experiment involving the single-spray multi-promotion technology of silicon fertilizer via unmanned aerial vehicles(UAVs)was conducted in three representative rice-growing areas:Ma'an Town,Shuikou Subdistrict,and Luzhou Town.[Results]The spraying of silicon fertilizer markedly enhanced the root development of rice,resulting in increased tiller number,plant height,stem thickness,panicle length,and 1000-grain weight,thereby effectively improving both yield and quality.This treatment exerted six primary beneficial effects:promoting robust and stable seedling growth,enhancing stress resistance,reducing reliance on chemical fertilizers,improving quality,increasing economic benefits,and significantly advancing ecological and social benefits.[Conclusions]The application of silicon fertilizer through spraying is an effective agronomic practice that simultaneously promotes increased rice yield,improved quality,enhanced efficiency,and the sustainable development of resources and the environment.
文摘To overcome the limitations of conventional approaches that adopt monolithic architectures and overlook critical dynamic interactions in evaluating combat effectiveness and subsystem contributions within amphibious operations,this paper proposes an integrated framework combining complex system network modeling with dynamic adversarial simulation for evaluating mission-critical system-of-systems(SoS).Specifically,the contribution rate of unmanned aerial vehicles(UAVs)to the amphibious joint landing SoS(AJLSoS)is quantified.Firstly,a standardized network topology model is developed using operation loop theory,systematically characterizing node functionalities and their interdependencies.Secondly,the ideal Lanchester equation is augmented according to the model’s static operational capability,and an amphibious operational simulation model is constructed based on the modified equation,enabling dynamic simulation of force attrition and engagement duration as key performance indicators of AJLSoS.To validate the theoretical framework,a battalion-level amphibious campaign scenario is developed to compute effectiveness metrics across multiple control scenarios and the contribution rate of UAVs to AJLSoS is analyzed.This study not only provides actionable insights for operational mission planning of UAVs in the context of amphibious operations but also demonstrates high adaptability to diverse operational contexts.
基金supported by National Natural Science Foundation of China(No.62072436 and No.62202449)National Key Research and Development Program of China(2021YFB2900102).
文摘Benefitting from UAVs’characteristics of flexible deployment and controllable movement in 3D space,odor source localization with multiple UAVs has been a hot research area in recent years.Considering the limited resources and insufficient battery capacities of UAVs,it is necessary to fast locate the odor source with low-complexity computation and minimal interaction under complicated environmental states.To this end,we propose a multi-UAV collaboration based odor source localization(MUC-OSL)method,where source estimation and UAV navigation are iteratively performed,aiming to accelerate the searching process and reduce the resource consumption of UAVs.Specifically,in the source estimation phase,we present a collaborative particle filter algorithm on the basis of UAVs’cognitive difference and collaborative information to improve source estimation accuracy.In the following navigation phase,an adaptive path planning algorithm is designed based on partially observable Markov decision process to distributedly determine the subsequent flying direction and moving steps of each UAV.The results of experiments conducted on two simulation platforms demonstrate that MUC-OSL outperforms existing efforts in terms of mean search time and success rate,and effectively reduces the resource consumption of UAVs.
基金supported in part by the National Natural Science Foundation of China under Grants 62001225,62071236,62071234 and U22A2002in part by the Major Science and Technology plan of Hainan Province under Grant ZDKJ2021022+1 种基金in part by the Scientific Research Fund Project of Hainan University under Grant KYQD(ZR)-21008in part by the Key Technologies R&D Program of Jiangsu(Prospective and Key Technologies for Industry)under Grants BE2023022 and BE2023022-2.
文摘The Internet of Unmanned Aerial Vehicles(I-UAVs)is expected to execute latency-sensitive tasks,but limited by co-channel interference and malicious jamming.In the face of unknown prior environmental knowledge,defending against jamming and interference through spectrum allocation becomes challenging,especially when each UAV pair makes decisions independently.In this paper,we propose a cooperative multi-agent reinforcement learning(MARL)-based anti-jamming framework for I-UAVs,enabling UAV pairs to learn their own policies cooperatively.Specifically,we first model the problem as a modelfree multi-agent Markov decision process(MAMDP)to maximize the long-term expected system throughput.Then,for improving the exploration of the optimal policy,we resort to optimizing a MARL objective function with a mutual-information(MI)regularizer between states and actions,which can dynamically assign the probability for actions frequently used by the optimal policy.Next,through sharing their current channel selections and local learning experience(their soft Q-values),the UAV pairs can learn their own policies cooperatively relying on only preceding observed information and predicting others’actions.Our simulation results show that for both sweep jamming and Markov jamming patterns,the proposed scheme outperforms the benchmarkers in terms of throughput,convergence and stability for different numbers of jammers,channels and UAV pairs.
基金Natural Science Foundation of Hunan Province,China(No.2024JJ8335)Open Topic of Hunan Geospatial Information Engineering and Technology Research Center,China(No.HNGIET2023004).
文摘The estimation of orientation parameters and correction of lens distortion are crucial problems in the field of Unmanned Aerial Vehicles(UAVs)photogrammetry.In recent years,the utilization of UAVs for aerial photogrammetry has witnessed a surge in popularity.Typically,UAVs are equipped with low-cost non-metric cameras and a Position and Orientation System(POS).Unfortunately,the Interior Orientation Parameters(IOPs)of the non-metric cameras are not fixed.Whether the lens distortions are large or small,they effect the image coordinates accordingly.Additionally,Inertial Measurement Units(IMUs)often have observation errors.To address these challenges and improve parameter estimation for UAVs Light Detection and Ranging(LiDAR)and photogrammetry,this paper analyzes the accuracy of POS observations obtained from Global Navigation Satellite System Real Time Kinematic(GNSS-RTK)and IMU data.A method that incorporates additional known conditions for parameter estimation,a series of algorithms to simultaneously solve for IOPs,Exterior Orientation Parameters(EOPs),and camera lens distortion correction parameters are proposed.Extensive experiments demonstrate that the coordinates measured by GNSS-RTK can be directly used as linear EOPs;however,angular EOP measurements from IMUs exhibit relatively large errors compared to adjustment results and require correction during the adjustment process.The IOPs of non-metric cameras vary slightly between images but need to be treated as unknown parameters in high precision applications.Furthermore,it is found that the Ebner systematic error model is sensitive to the choice of the magnification parameter of the photographic baseline length in images,it should be set as less than or equal to one third of the photographic baseline to ensure stable solutions.
基金National Natural Science Foundation of China(Grant No.52472417)to provide fund for conducting experiments.
文摘Complex multi-area collaborative coverage path planning in dynamic environments poses a significant challenge for multi-fixed-wing UAVs(multi-UAV).This study establishes a comprehensive framework that incorporates UAV capabilities,terrain,complex areas,and mission dynamics.A novel dynamic collaborative path planning algorithm is introduced,designed to ensure complete coverage of designated areas.This algorithm meticulously optimizes the operation,entry,and transition paths for each UAV,while also establishing evaluation metrics to refine coverage sequences for each area.Additionally,a three-dimensional path is computed utilizing an altitude descent method,effectively integrating twodimensional coverage paths with altitude constraints.The efficacy of the proposed approach is validated through digital simulations and mixed-reality semi-physical experiments across a variety of dynamic scenarios,including both single-area and multi-area coverage by multi-UAV.Results show that the coverage paths generated by this method significantly reduce both computation time and path length,providing a reliable solution for dynamic multi-UAV mission planning in semi-physical environments.
基金supported by the National Key R&D Program of China(No.2022YFB3104502)the National Natural Science Foundation of China(No.62301251)+2 种基金the Natural Science Foundation of Jiangsu Province of China under Project(No.BK20220883)the open research fund of National Mobile Communications Research Laboratory,Southeast University,China(No.2024D04)the Young Elite Scientists Sponsorship Program by CAST(No.2023QNRC001).
文摘The environment of low-altitude urban airspace is complex and variable due to numerous obstacles,non-cooperative aircraft,and birds.Unmanned Aerial Vehicles(UAVs)leveraging environmental information to achieve three-dimension collision-free trajectory planning is the prerequisite to ensure airspace security.However,the timely information of surrounding situation is difficult to acquire by UAVs,which further brings security risks.As a mature technology leveraged in traditional civil aviation,the Automatic Dependent Surveillance-Broadcast(ADS-B)realizes continuous surveillance of the information of aircraft.Consequently,we leverage ADS-B for surveillance and information broadcasting,and divide the aerial airspace into multiple sub-airspaces to improve flight safety in UAV trajectory planning.In detail,we propose the secure Sub-airSpaces Planning(SSP)algorithm and Particle Swarm Optimization Rapidly-exploring Random Trees(PSO-RRT)algorithm for the UAV trajectory planning in law-altitude airspace.The performance of the proposed algorithm is verified by simulations and the results show that SSP reduces both the maximum number of UAVs in the sub-airspace and the length of the trajectory,and PSO-RRT reduces the cost of UAV trajectory in the sub-airspace.
基金supported by the National Natural Science Foundation of China(Nos.12272104,U22B2013).
文摘This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.
基金supported by the National Natural Science Foundation of China(Nos.62201454 and 62306235)the Xi’an Science and Technology Program of Xi’an Science and Technology Bureau(No.23SFSF0004)。
文摘The unmanned aerial vehicle(UAV)images captured under low-light conditions are often suffering from noise and uneven illumination.To address these issues,we propose a low-light image enhancement algorithm for UAV images,which is inspired by the Retinex theory and guided by a light weighted map.Firstly,we propose a new network for reflectance component processing to suppress the noise in images.Secondly,we construct an illumination enhancement module that uses a light weighted map to guide the enhancement process.Finally,the processed reflectance and illumination components are recombined to obtain the enhancement results.Experimental results show that our method can suppress the noise in images while enhancing image brightness,and prevent over enhancement in bright regions.Code and data are available at https://gitee.com/baixiaotong2/uav-images.git.
基金supported by the Istanbul Technical University Office of Scientific Research Projects(ITUBAPSIS),under grant MYL-2022-43776。
文摘The design of unmanned aerial vehicles(UAVs)revolves around the careful selection of materials that are both lightweight and robust.Carbon fiber-reinforced polymer(CFRP)emerged as an ideal option for wing construction,with its mechanical qualities thoroughly investigated.In this study,we developed and optimized a conceptual UAV wing to withstand structural loads by establishing progressive composite stacking sequences,and we conducted a series of experimental characterizations on the resulting material.In the optimization phase,the objective was defined as weight reduction,while the Hashin damage criterion was established as the constraint for the optimization process.The optimization algorithm adaptively monitors regional damage criterion values,implementing necessary adjustments to facilitate the mitigation process in a cost-effective manner.Optimization of the analytical model using Simulia Abaqus~(TM)and a Python-based user-defined sub-routine resulted in a 34.7%reduction in the wing's structural weight after 45 iterative rounds.Then,the custom-developed optimization algorithm was compared with a genetic algorithm optimization.This comparison has demonstrated that,although the genetic algorithm explores numerous possibilities through hybridization,the custom-developed algorithm is more result-oriented and achieves optimization in a reduced number of steps.To validate the structural analysis,test specimens were fabricated from the wing's most critically loaded segment,utilizing the identical stacking sequence employed in the optimization studies.Rigorous mechanical testing revealed unexpectedly high compressive strength,while tensile and bending strengths fell within expected ranges.All observed failure loads remained within the established safety margins,thereby confirming the reliability of the analytical predictions.
基金The 2023 Scientific and Technological Project in Henan Province of China(232102220098)。
文摘During flight operations,quadrotor UAVs are susceptible to interference from environmental factors such as wind gusts,battery depletion,and obstacles,which may compromise flight stability.This study proposes a fuzzy adaptive PID controller(Fuzzy PID)combining PID control with fuzzy logic to achieve self-adaptive adjustment of PID parameters in UAV flight control systems,thereby enhancing system robustness.A quadrotor UAV control model was developed in Simulink,and a Fuzzy PID control system was constructed by integrating fuzzy control logic for simulation and experimental validation.Test results demonstrate that UAVs governed by Fuzzy PID control exhibit faster regulation speed and improved stability when subjected to disturbances.
文摘The“14th Five-Year Plan”and the Long-Range Objectives Through the Year 2035 propose to strengthen the construction of strategic emerging industrial clusters,promote the deep integration of the internet,big data,artificial intelligence,blockchain technology,etc.with the real economy,facilitate the development of advanced manufacturing,and consider unmanned aerial vehicles(UAVs)as an important breakthrough,providing significant opportunities for the development of the UAV industry.Therefore,this article takes the current status of the UAV industry development as a starting point,analyzes the exploration and practice of the UAV development model based on the low-altitude economy,and discusses strategic suggestions to promote the development of UAVs empowered by the low-altitude economy.Through analysis,this article aims to provide theoretical references and practical guidance for promoting the sustainable development of the UAV industry under the wave of the low-altitude economy.
文摘The“14th Five-Year Plan”and the Long-Range Objectives Through the Year 2035 propose to strengthen the construction of strategic emerging industrial clusters,promote the deep integration of the internet,big data,artificial intelligence,blockchain technology,etc.with the real economy,facilitate the development of advanced manufacturing,and consider UAVs as an important breakthrough,providing significant opportunities for the development of the UAV industry.Therefore,this article takes the current status of the UAV industry development as a starting point,analyzes the exploration and practice of the UAV development model based on the low-altitude economy,and discusses strategic suggestions to promote the development of UAVs empowered by the low-altitude economy.Through analysis,this article aims to provide theoretical reference and practical guidance for promoting the sustainable development of the UAV industry under the wave of the low-altitude economy.
文摘As battlefield scale enlarges,cross-platform collaborative combat provides an appealing paradigm for modern warfare.Complicated constraints and vast solution space pose great challenge for reasonable and efficient mission planning,where path planning and target assignment are tightly coupled.In this paper,we focus on UAV mission planning under carrier delivery mode(e.g.,by aircraft carrier,ground vehicle,or transport aircraft) and design a three-layer hierarchical solution framework.In the first layer,we simultaneously determine delivery points and target set division by clustering.To address the safety concerns of radar risk and UAV endurance,an improved density peak clustering algorithm is developed by constraint fusio n.In the second layer,mission planning within each cluster is viewed as a coope rative multiple-task assignment problem.A hybrid heuristic algorithm that integrates a voting-based heuristic solution generation strategy(VHSG) and a stochastic variable neighborhood search(SVNS),called VHSG-SVNS,is proposed for rapid solution.Based on the results of the first two layers,the third layer transforms carrier path planning into a multiple-vehicle routing problem with time window.The cost between any two nodes is calculated by the A~* algorithm,and the genetic algorithm is then implemented to determine the global route.Finally,a practical mission scenario containing 200 targets is used to validate the effectiveness of the designed framework,where three layers cooperate well with each other to generate satisfactory combat scheduling.Comparisons are made in each layer to highlight optimum-seeking capability and efficiency of the proposed algorithms.Works done in this paper provide a simple but efficient solution framework for cross-platform cooperative mission planning problems,and can be potentially extended to other applications such as post-disaster search and rescue,forest surveillance and firefighting,logistics pick and delivery,etc.
基金the support of the National Natural Science Foundation of China(No.52272382)the Aeronautical Science Foundation of China(No.20200017051001)the Fundamental Research Funds for the Central Universities,China。
文摘For autonomous Unmanned Aerial Vehicles(UAVs)flying in real-world scenarios,time for path planning is always limited,which is a challenge known as the anytime problem.Anytime planners address this by finding a collision-free path quickly and then improving it until time runs out,making UAVs more adaptable to different mission scenarios.However,current anytime algorithms based on A^(*)have insufficient control over the suboptimality bounds of paths and tend to lose their anytime properties in environments with large concave obstacles.This paper proposes a novel anytime path planning algorithm,Anytime Radiation A^(*)(ARa A^(*)),which can generate a series of suboptimal paths with improved bounds through decreasing search step sizes and can generate the optimal path when time is sufficient.The ARa A^(*)features two main innovations:an adaptive variable-step-size mechanism and elliptic constraints based on waypoints.The former helps achieve fast path searching in various environments.The latter allows ARa A^(*)to control the suboptimality bounds of paths and further enhance search efficiency.Simulation experiments show that the ARa A^(*)outperforms Anytime Repairing A^(*)(ARA^(*))and Anytime D^(*)(AD^(*))in controlling suboptimality bounds and planning time,especially in environments with large concave obstacles.Final flight experiments demonstrate that the paths planned by ARa A^(*)can ensure the safe flight of quadrotors.
基金supported by the Major Projects for Science and Technology Innovation 2030(2018AAA0100805).
文摘To address the confrontation decision-making issues in multi-round air combat,a dynamic game decision method is proposed based on decision tree for the confrontation of unmanned aerial vehicle(UAV)air combat.Based on game the-ory and the confrontation characteristics of air combat,a dynamic game process is constructed including the strategy sets,the situation information,and the maneuver decisions for both sides of air combat.By analyzing the UAV’s flight dyna-mics and the both sides’information,a payment matrix is estab-lished through the situation advantage function,performance advantage function,and profit function.Furthermore,the dynamic game decision problem is solved based on the linear induction method to obtain the Nash equilibrium solution,where the decision tree method is introduced to obtain the optimal maneuver decision,thereby improving the situation advantage in the next round of confrontation.According to the analysis,the simulation results for the confrontation scenarios of multi-round air combat are presented to verify the effectiveness and advan-tages of the proposed method.
基金supported by the National Natural Science Foundation of China under Grant No.42176190Fundamental Research Funds for the Central Universities,CHD under Grant Nos.300102243401 and 300102244203Research Funds for the Interdisciplinary Projects,CHU under Grant Nos.300104240912 and 300104240922。
文摘As important infrastructure for airborne communication platforms,unmanned aerial vehicles(UAVs)are expected to become a key part of 6G wireless networks.Thus,modeling low-and medium-altitude propagation channels has attracted much attention.Air-to-ground(A2G)propagation channel models vary in different scenarios,requiring accurate models for designing and evaluating UAV communication links.Unlike terrestrial models,A2G channel models lack detailed investigation.Therefore,this paper provides an overview of existing A2G channel measurement campaigns,different types of A2G channel models for various environments,and future research directions for UAV airland channel modeling.This study focuses on the potential of millimeter-wave technology for UAV A2G channel modeling and highlights nonsuburban scenarios requiring consideration in future modeling efforts.
基金supported by the National Natural Science Foundation of China(No.52272382)the Fundamental Research Funds for the Central Universities,China。
文摘Path planning is crucial for autonomous flight of fixed-wing Unmanned Aerial Vehicles(UAVs).However,due to the high-speed flight and complex control of fixed-wing UAVs,ensuring the feasibility and safety of planned paths in complex environments is challenging.This paper proposes a feasible path planning algorithm named Closed-loop Radial Ray A^(*)(CL-RaA^(*)).The core components of the CL-RaA^(*)include an adaptive variable-step-size path search and a just-in-time expansion primitive.The former enables fast path search in complex environments,while the latter ensures the feasibility of the generated paths.By integrating these two components and conducting safety checks on the trajectories to be expanded,the CL-RaA^(*)can rapidly generate safe and feasible paths that satisfy the differential constraints that comprehensively consider the dynamics and control characteristics of six-degree-of-freedom fixed-wing UAVs.The final performance tests and simulation validations demonstrate that the CL-RaA^(*)can generate safe and feasible paths in various environments.Compared to feasible path planning algorithms that use the rapidlyexploring random trees,the CL-RaA^(*)not only ensures deterministic planning results in the same scenarios but also generates smoother feasible paths for fixed-wing UAVs more efficiently.In environments with dense grid obstacles,the feasible paths generated by the CL-RaA^(*)are more conducive to UAV tracking compared to those planned using Dubins curves.
