针对智慧云仓货物信息量大、易出现账物不符等库存管理问题,迫切需要将无人机(unmanned aerial vehicle, UAV)和工业物联网(industrial Internet of things, IIoT)集成起来,为仓储精细化管理提供解决方案。首先,分析盘库作业数据采集与...针对智慧云仓货物信息量大、易出现账物不符等库存管理问题,迫切需要将无人机(unmanned aerial vehicle, UAV)和工业物联网(industrial Internet of things, IIoT)集成起来,为仓储精细化管理提供解决方案。首先,分析盘库作业数据采集与信息交互运行机制,以危险避障和数据采集为约束函数,考虑了UAV在加速、减速、匀速、转角等飞行条件下的能耗差异,并以能耗最低和时间最短为目标函数构造UAV盘库作业数学模型;然后,设计了差分迁移-分段变异生物地理学优化(differential migration-piecewise mutation-biogeography-based optimization, DPBBO)算法对上述模型进行优化解算;最后,进行了仿真实验验证。结果表明:DPBBO算法对解决该盘库作业问题的效果较优,可以提升库存抽检任务的时效性和库存管理的准确性。展开更多
Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metavers...Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.展开更多
Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unman...Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle(UAV)imagery.Addressing these limitations,we propose a hybrid transformer-based detector,H-DETR,and enhance it for dense small objects,leading to an accurate and efficient model.Firstly,we introduce a hybrid transformer encoder,which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently.Furthermore,we propose two novel strategies to enhance detection performance without incurring additional inference computation.Query filter is designed to cope with the dense clustering inherent in drone-captured images by counteracting similar queries with a training-aware non-maximum suppression.Adversarial denoising learning is a novel enhancement method inspired by adversarial learning,which improves the detection of numerous small targets by counteracting the effects of artificial spatial and semantic noise.Extensive experiments on the VisDrone and UAVDT datasets substantiate the effectiveness of our approach,achieving a significant improvement in accuracy with a reduction in computational complexity.Our method achieves 31.9%and 21.1%AP on the VisDrone and UAVDT datasets,respectively,and has a faster inference speed,making it a competitive model in UAV image object detection.展开更多
Timely acquisition of rescue target information is critical for emergency response after a flood disaster.Unmanned Aerial Vehicles(UAVs)equipped with remote sensing capabilities offer distinct advantages,including hig...Timely acquisition of rescue target information is critical for emergency response after a flood disaster.Unmanned Aerial Vehicles(UAVs)equipped with remote sensing capabilities offer distinct advantages,including high-resolution imagery and exceptional mobility,making them well suited for monitoring flood extent and identifying rescue targets during floods.However,there are some challenges in interpreting rescue information in real time from flood images captured by UAVs,such as the complexity of the scenarios of UAV images,the lack of flood rescue target detection datasets and the limited real-time processing capabilities of the airborne on-board platform.Thus,we propose a real-time rescue target detection method for UAVs that is capable of efficiently delineating flood extent and identifying rescue targets(i.e.,pedestrians and vehicles trapped by floods).The proposed method achieves real-time rescue information extraction for UAV platforms by lightweight processing and fusion of flood extent extraction model and target detection model.The flood inundation range is extracted by the proposed method in real time and detects targets such as people and vehicles to be rescued based on this layer.Our experimental results demonstrate that the Intersection over Union(IoU)for flood water extraction reaches an impressive 80%,and the IoU for real-time flood water extraction stands at a commendable 76.4%.The information on flood stricken targets extracted by this method in real time can be used for flood emergency rescue.展开更多
Improvement of integrated battlefield situational awareness in complex environments involving dynamic factors such as restricted communications and electromagnetic interference(EMI)has become a contentious research pr...Improvement of integrated battlefield situational awareness in complex environments involving dynamic factors such as restricted communications and electromagnetic interference(EMI)has become a contentious research problem.In certain mission environments,due to the impact of many interference sources on real-time communication or mission requirements such as the need to implement communication regulations,the mission stages are represented as a dynamic combination of several communication-available and communication-unavailable stages.Furthermore,the data interaction between unmanned aerial vehicles(UAVs)can only be performed in specific communication-available stages.Traditional cooperative search algorithms cannot handle such situations well.To solve this problem,this study constructed a distributed model predictive control(DMPC)architecture for a collaborative control of UAVs and used the Voronoi diagram generation method to re-plan the search areas of all UAVs in real time to avoid repetition of search areas and UAV collisions while improving the search efficiency and safety factor.An attention mechanism ant-colony optimization(AACO)algorithm is proposed for UAV search-control decision planning.The search strategy is adaptively updated by introducing an attention mechanism for regular instruction information,a priori information,and emergent information of the mission to satisfy different search expectations to the maximum extent.Simulation results show that the proposed algorithm achieves better search performance than traditional algorithms in restricted communication constraint scenarios.展开更多
To improve the anti-jamming and interference mitigation ability of the UAV-aided communication systems, this paper investigates the channel selection optimization problem in face of both internal mutual interference a...To improve the anti-jamming and interference mitigation ability of the UAV-aided communication systems, this paper investigates the channel selection optimization problem in face of both internal mutual interference and external malicious jamming. A cooperative anti-jamming and interference mitigation method based on local altruistic is proposed to optimize UAVs’ channel selection. Specifically, a Stackelberg game is modeled to formulate the confrontation relationship between UAVs and the jammer. A local altruistic game is modeled with each UAV considering the utilities of both itself and other UAVs. A distributed cooperative anti-jamming and interference mitigation algorithm is proposed to obtain the Stackelberg equilibrium. Finally, the convergence of the proposed algorithm and the impact of the transmission power on the system loss value are analyzed, and the anti-jamming performance of the proposed algorithm can be improved by around 64% compared with the existing algorithms.展开更多
In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of ...In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of UAV,the transmitting beamforming of users,and the phase shift matrix of IRS.The original problem is strong non-convex and difficult to solve.We first propose two basic modes of the proactive eavesdropper,and obtain the closed-form solution for the boundary conditions of the two modes.Then we transform the original problem into an equivalent one and propose an alternating optimization(AO)based method to obtain a local optimal solution.The convergence of the algorithm is illustrated by numerical results.Further,we propose a zero forcing(ZF)based method as sub-optimal solution,and the simulation section shows that the proposed two schemes could obtain better performance compared with traditional schemes.展开更多
High-resolution landslide images are required for detailed geomorphological analysis in complex topographic environment with steep and vertical landslide distribution.This study proposed a vertical route planning meth...High-resolution landslide images are required for detailed geomorphological analysis in complex topographic environment with steep and vertical landslide distribution.This study proposed a vertical route planning method for unmanned aerial vehicles(UAVs),which could achieve rapid image collection based on strictly calculated route parameters.The effectiveness of this method was verified using a DJI Mavic 2 Pro,obtaining high-resolution landslide images within the Dongchuan debris flow gully,in the Xiaojiang River Basin,Dongchuan District,Yunnan,China.A three-dimensional(3D)model was constructed by the structure-from-motion and multi-view stereo(SfM-MVS).Micro-geomorphic features were analyzed through visual interpretation,geographic information system(GIS),spatial analysis,and mathematical statistics methods.The results demonstrated that the proposed method could obtain comprehensive vertical information on landslides while improving measurement accuracy.The 3D model was constructed using the vertically oriented flight route to achieve centimeter-level accuracy(horizontal accuracy better than 6 cm,elevation accuracy better than 3 cm,and relative accuracy better than 3.5 cm).The UAV technology could further help understand the micro internal spatial and structural characteristics of landslides,facilitating intuitive acquisition of surface details.The slope of landslide clusters ranged from 36°to 72°,with the majority of the slope facing east and southeast.Upper elevation levels were relatively consistent while middle to lower elevation levels gradually decreased from left to right with significant variations in lower elevation levels.During the rainy season,surface runoff was abundant,and steep topography exacerbated changes in surface features.This route method is suitable for unmanned aerial vehicle(UAV)landslide surveys in complex mountainous environments.The geomorphological analysis methods used will provide references for identifying and describing topographic features.展开更多
Most researches associated with target encircling control are focused on moving along a circular orbit under an ideal environment free from external disturbances.However,elliptical encirclement with a time-varying obs...Most researches associated with target encircling control are focused on moving along a circular orbit under an ideal environment free from external disturbances.However,elliptical encirclement with a time-varying observation radius,may permit a more flexible and high-efficacy enclosing solution,whilst the non-orthogonal property between axial and tangential speed components,non-ignorable environmental perturbations,and strict assignment requirements empower elliptical encircling control to be more challenging,and the relevant investigations are still open.Following this line,an appointed-time elliptical encircling control rule capable of reinforcing circumnavigation performances is developed to enable Unmanned Aerial Vehicles(UAVs)to move along a specified elliptical path within a predetermined reaching time.The remarkable merits of the designed strategy are that the relative distance controlling error can be guaranteed to evolve within specified regions with a designer-specified convergence behavior.Meanwhile,wind perturbations can be online counteracted based on an unknown system dynamics estimator(USDE)with only one regulating parameter and high computational efficiency.Lyapunov tool demonstrates that all involved error variables are ultimately limited,and simulations are implemented to confirm the usability of the suggested control algorithm.展开更多
Aiming at the problem of multi-UAV pursuit-evasion confrontation, a UAV cooperative maneuver method based on an improved multi-agent deep reinforcement learning(MADRL) is proposed. In this method, an improved Comm Net...Aiming at the problem of multi-UAV pursuit-evasion confrontation, a UAV cooperative maneuver method based on an improved multi-agent deep reinforcement learning(MADRL) is proposed. In this method, an improved Comm Net network based on a communication mechanism is introduced into a deep reinforcement learning algorithm to solve the multi-agent problem. A layer of gated recurrent unit(GRU) is added to the actor-network structure to remember historical environmental states. Subsequently,another GRU is designed as a communication channel in the Comm Net core network layer to refine communication information between UAVs. Finally, the simulation results of the algorithm in two sets of scenarios are given, and the results show that the method has good effectiveness and applicability.展开更多
Laser anti-drone technology is entering the sequence of actual combat,and it is necessary to consider the vulnerability of typical functional parts of UAVs.Since the concept of"vulnerability"was proposed,a v...Laser anti-drone technology is entering the sequence of actual combat,and it is necessary to consider the vulnerability of typical functional parts of UAVs.Since the concept of"vulnerability"was proposed,a variety of analysis programs for battlefield targets to traditional weapons have been developed,but a comprehensive assessment methodology for targets'vulnerability to laser is still missing.Based on the shotline method,this paper proposes a method that equates laser beam to shotline array,an efficient vulnerability analysis program of target to laser is established by this method,and the program includes the circuit board and the wire into the vulnerability analysis category,which improves the precision of the vulnerability analysis.Taking the UAV engine part as the target of vulnerability analysis,combine with the"life-death unit method"to calculate the laser penetration rate of various materials of the UAV,and the influence of laser weapon system parameters and striking orientation on the killing probability is quantified after introducing the penetration rate into the vulnerability analysis program.The quantitative analysis method proposed in this paper has certain general expansibility,which can provide a fresh idea for the vulnerability analysis of other targets to laser.展开更多
In mobile edge computing,unmanned aerial vehicles(UAVs)equipped with computing servers have emerged as a promising solution due to their exceptional attributes of high mobility,flexibility,rapid deployment,and terrain...In mobile edge computing,unmanned aerial vehicles(UAVs)equipped with computing servers have emerged as a promising solution due to their exceptional attributes of high mobility,flexibility,rapid deployment,and terrain agnosticism.These attributes enable UAVs to reach designated areas,thereby addressing temporary computing swiftly in scenarios where ground-based servers are overloaded or unavailable.However,the inherent broadcast nature of line-of-sight transmission methods employed by UAVs renders them vulnerable to eavesdropping attacks.Meanwhile,there are often obstacles that affect flight safety in real UAV operation areas,and collisions between UAVs may also occur.To solve these problems,we propose an innovative A*SAC deep reinforcement learning algorithm,which seamlessly integrates the benefits of Soft Actor-Critic(SAC)and A*(A-Star)algorithms.This algorithm jointly optimizes the hovering position and task offloading proportion of the UAV through a task offloading function.Furthermore,our algorithm incorporates a path-planning function that identifies the most energy-efficient route for the UAV to reach its optimal hovering point.This approach not only reduces the flight energy consumption of the UAV but also lowers overall energy consumption,thereby optimizing system-level energy efficiency.Extensive simulation results demonstrate that,compared to other algorithms,our approach achieves superior system benefits.Specifically,it exhibits an average improvement of 13.18%in terms of different computing task sizes,25.61%higher on average in terms of the power of electromagnetic wave interference intrusion into UAVs emitted by different auxiliary UAVs,and 35.78%higher on average in terms of the maximum computing frequency of different auxiliary UAVs.As for path planning,the simulation results indicate that our algorithm is capable of determining the optimal collision-avoidance path for each auxiliary UAV,enabling them to safely reach their designated endpoints in diverse obstacle-ridden environments.展开更多
基金supported in part by NSFC (62102099, U22A2054, 62101594)in part by the Pearl River Talent Recruitment Program (2021QN02S643)+9 种基金Guangzhou Basic Research Program (2023A04J1699)in part by the National Research Foundation, SingaporeInfocomm Media Development Authority under its Future Communications Research Development ProgrammeDSO National Laboratories under the AI Singapore Programme under AISG Award No AISG2-RP-2020-019Energy Research Test-Bed and Industry Partnership Funding Initiative, Energy Grid (EG) 2.0 programmeDesCartes and the Campus for Research Excellence and Technological Enterprise (CREATE) programmeMOE Tier 1 under Grant RG87/22in part by the Singapore University of Technology and Design (SUTD) (SRG-ISTD-2021- 165)in part by the SUTD-ZJU IDEA Grant SUTD-ZJU (VP) 202102in part by the Ministry of Education, Singapore, through its SUTD Kickstarter Initiative (SKI 20210204)。
文摘Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.
基金This research was funded by the Natural Science Foundation of Hebei Province(F2021506004).
文摘Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle(UAV)imagery.Addressing these limitations,we propose a hybrid transformer-based detector,H-DETR,and enhance it for dense small objects,leading to an accurate and efficient model.Firstly,we introduce a hybrid transformer encoder,which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently.Furthermore,we propose two novel strategies to enhance detection performance without incurring additional inference computation.Query filter is designed to cope with the dense clustering inherent in drone-captured images by counteracting similar queries with a training-aware non-maximum suppression.Adversarial denoising learning is a novel enhancement method inspired by adversarial learning,which improves the detection of numerous small targets by counteracting the effects of artificial spatial and semantic noise.Extensive experiments on the VisDrone and UAVDT datasets substantiate the effectiveness of our approach,achieving a significant improvement in accuracy with a reduction in computational complexity.Our method achieves 31.9%and 21.1%AP on the VisDrone and UAVDT datasets,respectively,and has a faster inference speed,making it a competitive model in UAV image object detection.
基金National Natural Science Foundation of China(No.42271416)Guangxi Science and Technology Major Project(No.AA22068072)Shennongjia National Park Resources Comprehensive Investigation Research Project(No.SNJNP2023015).
文摘Timely acquisition of rescue target information is critical for emergency response after a flood disaster.Unmanned Aerial Vehicles(UAVs)equipped with remote sensing capabilities offer distinct advantages,including high-resolution imagery and exceptional mobility,making them well suited for monitoring flood extent and identifying rescue targets during floods.However,there are some challenges in interpreting rescue information in real time from flood images captured by UAVs,such as the complexity of the scenarios of UAV images,the lack of flood rescue target detection datasets and the limited real-time processing capabilities of the airborne on-board platform.Thus,we propose a real-time rescue target detection method for UAVs that is capable of efficiently delineating flood extent and identifying rescue targets(i.e.,pedestrians and vehicles trapped by floods).The proposed method achieves real-time rescue information extraction for UAV platforms by lightweight processing and fusion of flood extent extraction model and target detection model.The flood inundation range is extracted by the proposed method in real time and detects targets such as people and vehicles to be rescued based on this layer.Our experimental results demonstrate that the Intersection over Union(IoU)for flood water extraction reaches an impressive 80%,and the IoU for real-time flood water extraction stands at a commendable 76.4%.The information on flood stricken targets extracted by this method in real time can be used for flood emergency rescue.
基金the support of the National Natural Science Foundation of China(Grant No.62076204)the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University(Grant No.CX2020019)in part by the China Postdoctoral Science Foundation(Grants No.2021M700337)。
文摘Improvement of integrated battlefield situational awareness in complex environments involving dynamic factors such as restricted communications and electromagnetic interference(EMI)has become a contentious research problem.In certain mission environments,due to the impact of many interference sources on real-time communication or mission requirements such as the need to implement communication regulations,the mission stages are represented as a dynamic combination of several communication-available and communication-unavailable stages.Furthermore,the data interaction between unmanned aerial vehicles(UAVs)can only be performed in specific communication-available stages.Traditional cooperative search algorithms cannot handle such situations well.To solve this problem,this study constructed a distributed model predictive control(DMPC)architecture for a collaborative control of UAVs and used the Voronoi diagram generation method to re-plan the search areas of all UAVs in real time to avoid repetition of search areas and UAV collisions while improving the search efficiency and safety factor.An attention mechanism ant-colony optimization(AACO)algorithm is proposed for UAV search-control decision planning.The search strategy is adaptively updated by introducing an attention mechanism for regular instruction information,a priori information,and emergent information of the mission to satisfy different search expectations to the maximum extent.Simulation results show that the proposed algorithm achieves better search performance than traditional algorithms in restricted communication constraint scenarios.
基金supported in part by the National Natural Science Foundation of China (No.62271253,61901523,62001381)Fundamental Research Funds for the Central Universities (No.NS2023018)+2 种基金the National Aerospace Science Foundation of China under Grant 2023Z021052002the open research fund of National Mobile Communications Research Laboratory,Southeast University (No.2023D09)Postgraduate Research & Practice Innovation Program of NUAA (No.xcxjh20220402)。
文摘To improve the anti-jamming and interference mitigation ability of the UAV-aided communication systems, this paper investigates the channel selection optimization problem in face of both internal mutual interference and external malicious jamming. A cooperative anti-jamming and interference mitigation method based on local altruistic is proposed to optimize UAVs’ channel selection. Specifically, a Stackelberg game is modeled to formulate the confrontation relationship between UAVs and the jammer. A local altruistic game is modeled with each UAV considering the utilities of both itself and other UAVs. A distributed cooperative anti-jamming and interference mitigation algorithm is proposed to obtain the Stackelberg equilibrium. Finally, the convergence of the proposed algorithm and the impact of the transmission power on the system loss value are analyzed, and the anti-jamming performance of the proposed algorithm can be improved by around 64% compared with the existing algorithms.
基金This work was supported by the Key Scientific and Technological Project of Henan Province(Grant Number 222102210212)Doctoral Research Start Project of Henan Institute of Technology(Grant Number KQ2005)Key Research Projects of Colleges and Universities in Henan Province(Grant Number 23B510006).
文摘In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of UAV,the transmitting beamforming of users,and the phase shift matrix of IRS.The original problem is strong non-convex and difficult to solve.We first propose two basic modes of the proactive eavesdropper,and obtain the closed-form solution for the boundary conditions of the two modes.Then we transform the original problem into an equivalent one and propose an alternating optimization(AO)based method to obtain a local optimal solution.The convergence of the algorithm is illustrated by numerical results.Further,we propose a zero forcing(ZF)based method as sub-optimal solution,and the simulation section shows that the proposed two schemes could obtain better performance compared with traditional schemes.
基金supported by the National Natural Science Foundation of China (Grant No. 62266026)
文摘High-resolution landslide images are required for detailed geomorphological analysis in complex topographic environment with steep and vertical landslide distribution.This study proposed a vertical route planning method for unmanned aerial vehicles(UAVs),which could achieve rapid image collection based on strictly calculated route parameters.The effectiveness of this method was verified using a DJI Mavic 2 Pro,obtaining high-resolution landslide images within the Dongchuan debris flow gully,in the Xiaojiang River Basin,Dongchuan District,Yunnan,China.A three-dimensional(3D)model was constructed by the structure-from-motion and multi-view stereo(SfM-MVS).Micro-geomorphic features were analyzed through visual interpretation,geographic information system(GIS),spatial analysis,and mathematical statistics methods.The results demonstrated that the proposed method could obtain comprehensive vertical information on landslides while improving measurement accuracy.The 3D model was constructed using the vertically oriented flight route to achieve centimeter-level accuracy(horizontal accuracy better than 6 cm,elevation accuracy better than 3 cm,and relative accuracy better than 3.5 cm).The UAV technology could further help understand the micro internal spatial and structural characteristics of landslides,facilitating intuitive acquisition of surface details.The slope of landslide clusters ranged from 36°to 72°,with the majority of the slope facing east and southeast.Upper elevation levels were relatively consistent while middle to lower elevation levels gradually decreased from left to right with significant variations in lower elevation levels.During the rainy season,surface runoff was abundant,and steep topography exacerbated changes in surface features.This route method is suitable for unmanned aerial vehicle(UAV)landslide surveys in complex mountainous environments.The geomorphological analysis methods used will provide references for identifying and describing topographic features.
基金National Natural Science Foundation of China(Grant Nos.61803348,62173312,51922009)Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement(Grant No.201905D121001).
文摘Most researches associated with target encircling control are focused on moving along a circular orbit under an ideal environment free from external disturbances.However,elliptical encirclement with a time-varying observation radius,may permit a more flexible and high-efficacy enclosing solution,whilst the non-orthogonal property between axial and tangential speed components,non-ignorable environmental perturbations,and strict assignment requirements empower elliptical encircling control to be more challenging,and the relevant investigations are still open.Following this line,an appointed-time elliptical encircling control rule capable of reinforcing circumnavigation performances is developed to enable Unmanned Aerial Vehicles(UAVs)to move along a specified elliptical path within a predetermined reaching time.The remarkable merits of the designed strategy are that the relative distance controlling error can be guaranteed to evolve within specified regions with a designer-specified convergence behavior.Meanwhile,wind perturbations can be online counteracted based on an unknown system dynamics estimator(USDE)with only one regulating parameter and high computational efficiency.Lyapunov tool demonstrates that all involved error variables are ultimately limited,and simulations are implemented to confirm the usability of the suggested control algorithm.
基金supported in part by the National Key Laboratory of Air-based Information Perception and Fusion and the Aeronautical Science Foundation of China (Grant No. 20220001068001)National Natural Science Foundation of China (Grant No.61673327)+1 种基金Natural Science Basic Research Plan in Shaanxi Province,China (Grant No. 2023-JC-QN-0733)China IndustryUniversity-Research Innovation Foundation (Grant No. 2022IT188)。
文摘Aiming at the problem of multi-UAV pursuit-evasion confrontation, a UAV cooperative maneuver method based on an improved multi-agent deep reinforcement learning(MADRL) is proposed. In this method, an improved Comm Net network based on a communication mechanism is introduced into a deep reinforcement learning algorithm to solve the multi-agent problem. A layer of gated recurrent unit(GRU) is added to the actor-network structure to remember historical environmental states. Subsequently,another GRU is designed as a communication channel in the Comm Net core network layer to refine communication information between UAVs. Finally, the simulation results of the algorithm in two sets of scenarios are given, and the results show that the method has good effectiveness and applicability.
基金National Natural Science Foundation of China(Grant Nos.62005276,62175234)the Scientific and Technological Development Program of Jilin,China(Grant No.20230508111RC)to provide fund for this research。
文摘Laser anti-drone technology is entering the sequence of actual combat,and it is necessary to consider the vulnerability of typical functional parts of UAVs.Since the concept of"vulnerability"was proposed,a variety of analysis programs for battlefield targets to traditional weapons have been developed,but a comprehensive assessment methodology for targets'vulnerability to laser is still missing.Based on the shotline method,this paper proposes a method that equates laser beam to shotline array,an efficient vulnerability analysis program of target to laser is established by this method,and the program includes the circuit board and the wire into the vulnerability analysis category,which improves the precision of the vulnerability analysis.Taking the UAV engine part as the target of vulnerability analysis,combine with the"life-death unit method"to calculate the laser penetration rate of various materials of the UAV,and the influence of laser weapon system parameters and striking orientation on the killing probability is quantified after introducing the penetration rate into the vulnerability analysis program.The quantitative analysis method proposed in this paper has certain general expansibility,which can provide a fresh idea for the vulnerability analysis of other targets to laser.
基金supported by the Central University Basic Research Business Fee Fund Project(J2023-027)Open Fund of Key Laboratory of Flight Techniques and Flight Safety,CAAC(No.FZ2022KF06)China Postdoctoral Science Foundation(No.2022M722248).
文摘In mobile edge computing,unmanned aerial vehicles(UAVs)equipped with computing servers have emerged as a promising solution due to their exceptional attributes of high mobility,flexibility,rapid deployment,and terrain agnosticism.These attributes enable UAVs to reach designated areas,thereby addressing temporary computing swiftly in scenarios where ground-based servers are overloaded or unavailable.However,the inherent broadcast nature of line-of-sight transmission methods employed by UAVs renders them vulnerable to eavesdropping attacks.Meanwhile,there are often obstacles that affect flight safety in real UAV operation areas,and collisions between UAVs may also occur.To solve these problems,we propose an innovative A*SAC deep reinforcement learning algorithm,which seamlessly integrates the benefits of Soft Actor-Critic(SAC)and A*(A-Star)algorithms.This algorithm jointly optimizes the hovering position and task offloading proportion of the UAV through a task offloading function.Furthermore,our algorithm incorporates a path-planning function that identifies the most energy-efficient route for the UAV to reach its optimal hovering point.This approach not only reduces the flight energy consumption of the UAV but also lowers overall energy consumption,thereby optimizing system-level energy efficiency.Extensive simulation results demonstrate that,compared to other algorithms,our approach achieves superior system benefits.Specifically,it exhibits an average improvement of 13.18%in terms of different computing task sizes,25.61%higher on average in terms of the power of electromagnetic wave interference intrusion into UAVs emitted by different auxiliary UAVs,and 35.78%higher on average in terms of the maximum computing frequency of different auxiliary UAVs.As for path planning,the simulation results indicate that our algorithm is capable of determining the optimal collision-avoidance path for each auxiliary UAV,enabling them to safely reach their designated endpoints in diverse obstacle-ridden environments.
