基于DPBBO算法的智慧云仓UAV盘库作业优化

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。

通信约束下UAV集群协同拦截任务分配算法

针对多无人机协同拦截多机动目标的任务分配问题,同时考虑到真实战场环境中存在的通信约束以及探测范围约束条件,本文提出了分步一致性拍卖算法(SCBAA)。首先,对真实战场环境中存在的通信约束以及探测范围约束等问题进行了描述分析,构建了多无人机协同拦截任务分配模型,设计了综合效能函数以及相应约束条件。其次,为解决多无人机协同打击单一目标的不平衡任务分配以及冲突消解问题,将原任务分配过程分为主要任务分配以及次要任务分配两部分,通过多次拍卖以及冲突消解实现多无人机对单一目标的任务分配。仿真结果表明,该算法可有效解决通信约束条件下的分布式多无人机协同拦截问题,并适应动态环境中任务分配对实时性的要求。

UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

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.

对抗条件下基于SAC-Lagrangian的UAV智能规划

无人机因其低成本、可消耗、分布部署、敏捷灵活的优势,在多个民用领域大放异彩。但受其智能化程度限制,如何在复杂对抗条件下自主安全完成任务仍存在巨大挑战。针对目前无人机任务规划存在的智能性和安全性问题,提出一种基于安全强化学习算法的无人机智能规划方法(SAC-Lagrangian)。考虑了雷达威胁、禁飞区安全约束和地导对抗条件,将任务规划问题建模为约束马尔可夫决策过程(CMDP),通过拉格朗日乘子法变为对偶问题,采用最大熵柔性行动者-评论家(SAC)算法近似求解最优策略,保证了智能体在遵守安全约束条件下最大化期望回报。仿真结果表明,与其他基线算法相比,所提方法能在保证任务性能的同时确保安全性,适应动态变化的场景,任务完成率达到96%,因此,具有高效、鲁棒和安全的优势。

RIS辅助的UAV与用户协同缓存策略

研究了智能反射面(RIS)和缓存辅助的无人机(UAV)中继通信系统方案,通过在UAV与用户之间搭建RIS反射信号,改善信道环境;在UAV与用户设备上部署缓存设备,预先存储热点内容,减轻无线回程链路的压力;以最大化用户服务成功概率为优化目标,建立缓存容量受限约束下的UAV与用户协同缓存放置策略优化模型,针对该非线性连续非凸约束优化问题,提出基于鲸鱼优化算法(WOA)的求解方法.仿真实验结果表明,使用RIS可以有效降低UAV通信中断概率,基于WOA的UAV与用户协同缓存最优放置策略优于现有其他两种缓存策略,能有效提高缓存命中概率,从而提高用户服务成功概率.

IRS Assisted UAV Communications against Proactive Eavesdropping in Mobile Edge Computing Networks

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.

基于UAV勘察与层次分析法的安阳许家沟露天矿山地质风险评价

矿产资源是社会经济发展的重要基础,在社会发展与生态建设统筹的背景下,矿山地质风险防治与生态环境修复成为矿业研究的焦点。矿山地质风险的分类和量化是矿山地质风险防治和修复的基础,有利于因地制宜制定治理措施。本文基于UAV勘察和层次分析法对安阳许家沟露天矿山群地质风险进行了评价,根据勘察结果总结出边坡崩塌、矿坑坍塌和水土流失3个主要地质风险,各风险层选取4个因素,共12个评价指标,并利用ArcGIS栅格计算模块对选取的12个评价因素进行叠加分析。研究结果显示:高度风险区、显著风险区、一般风险区和稍有风险区的面积分别为13500 m^(2)、39000 m^(2)、26750 m^(2)、45750 m^(2),分别占研究区总面积的10.8%、31.2%、21.4%、36.6%;综合矿山地质风险排序为河西胜利II区<豫隆I区<豫安III区;从豫隆I区到豫安III区,随着研究子区面积的减少,边坡崩塌风险贡献率逐渐降低,矿坑坍塌风险和水土流失风险逐渐升高。研究结果表明,边坡崩塌评分与矿山地质评分呈现双峰态势,而矿坑坍塌风险和水土流失风险呈单峰态势。本文提出危岩清除、矿渣回填、边坡修整、客土恢复林地和耕地、养护治理等治理措施,从而降低矿山地质风险,修复矿山生态。

Detection and analysis of landslide geomorphology based on UAV vertical photogrammetry

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.

Elliptical encirclement control capable of reinforcing performances for UAVs around a dynamic target

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.

Multi-UAV cooperative maneuver decision-making for pursuitevasion using improved MADRL

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.

Vulnerability assessment of UAV engine to laser based on improved shotline method

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.

Distributed Multicircular Circumnavigation Control for UAVs with Desired Angular Spacing

This paper addresses a multicircular circumnavigation control for UAVs with desired angular spacing around a nonstationary target.By defining a coordinated error relative to neighboring angular spacing,under the premise that target information is perfectly accessible by all nodes,a centralized circular enclosing control strategy is derived for multiple UAVs connected by an undirected graph to allow for formation behaviors concerning the moving target.Besides,to avoid the requirement of target’s states being accessible for each UAV,fixed-time distributed observers are introduced to acquire the state estimates in a fixed-time sense,and the upper boundary of settling time can be determined offline irrespective of initial properties,greatly releasing the burdensome communication traffic.Then,with the aid of fixed-time distributed observers,a distributed circular circumnavigation controller is derived to force all UAVs to collaboratively evolve along the preset circles while keeping a desired angular spacing.It is inferred from Lyapunov stability that all errors are demonstrated to be convergent.Simulations are offered to verify the utility of proposed protocol.

基于多密度流聚类的UAV-NOMA用户分簇与功率分配算法

针对无人机(Unmanned Aerial Vehicle,UAV)辅助非正交多址(Non-Orthogonal Multiple Access,NOMA)下行通信系统,提出了最大化和速率的用户动态分簇与功率分配方案.考虑用户服务质量与UAV位置约束,建立了和速率最大化的优化问题.由于目标函数的非凸性,将原问题解耦为三个子问题,分别优化UAV位置部署与用户连接、用户动态分簇、功率分配以提高系统性能.首先,基于K-means算法设计了UAV位置部署与用户连接方案,以减小路损为目的确定UAV最佳部署位置,同时选择其服务的最优用户群;其次,改进多密度流聚类(Multi-Density Stream Clustering, MDSC)算法,提出了单UAV下用户静态与动态分簇方案,静态分簇方案可自适应平衡簇数与簇用户数,并获得较大的簇内用户信道增益差异,动态分簇方案则针对用户移动属性,制定了即时更新策略;最后,使用分式规划(Fractional Programming,FP)二次变换的方法,引入辅助变量将原非凸问题变换为凸问题,交替优化辅助变量与功率分配因子,获得原非凸问题的次优解.仿真结果表明,与其他算法相比,本文分簇方案能获得更大的簇内信道差异与更小的簇内用户数标准差,同时用户系统性能也获得了显著提升.

UAV倾斜摄影在革命旧址保护中的应用

针对革命旧址保护工作的严峻性,以及在获取革命旧址测区数据中传统测绘方法工作效率低、难以全面获取数据和无法获得高精度三维立体数据等问题,基于UAV倾斜摄影测量技术,以红军长征旧址为研究对象,通过外业数据采集,利用SIFT算法提取匹配影像特征点,再利用SfM-MVS算法构建三维立体场景,采集三维立体场景中的检查点数据。结果表明,三维立体场景满足大比例尺数字地形图成图规范要求;分析线元素精度可知,三维立体场景数据测量结果可达厘米级,满足革命旧址保护中对数据精度的要求;在革命旧址保护工作中还可提供多种基础数据,为今后革命旧址保护工作提供了一种高效获取高精度数据的参考方法。

A Planning Method for Operational Test of UAV Swarm Based on Mission Reliability

The unmanned aerial vehicle(UAV)swarm plays an increasingly important role in the modern battlefield,and the UAV swarm operational test is a vital means to validate the combat effectiveness of the UAV swarm.Due to the high cost and long duration of operational tests,it is essential to plan the test in advance.To solve the problem of planning UAV swarm operational test,this study considers the multi-stage feature of a UAV swarm mission,composed of launch,flight and combat stages,and proposes a method to find test plans that can maximize mission reliability.Therefore,a multi-stage mission reliability model for a UAV swarm is proposed to ensure successful implementation of the mission.A multi-objective integer optimization method that considers both mission reliability and cost is then formulated to obtain the optimal test plans.This study first constructs a mission reliability model for the UAV swarm in the combat stage.Then,the launch stage and flight stage are integrated to develop a complete PMS(Phased Mission Systems)reliability model.Finally,the Binary Decision Diagrams(BDD)and Multi Objective Quantum Particle Swarm Optimization(MOQPSO)methods are proposed to solve the model.The optimal plans considering both reliability and cost are obtained.The proposed model supports the planning of UAV swarm operational tests and represents a meaningful exploration of UAV swarm test planning.

A Dual-Cluster-Head Based Medium Access Control for Large-Scale UAV Ad-Hoc Networks

Unmanned Aerial Vehicle(UAV)ad hoc network has achieved significant growth for its flexibility,extensibility,and high deployability in recent years.The application of clustering scheme for UAV ad hoc network is imperative to enhance the performance of throughput and energy efficiency.In conventional clustering scheme,a single cluster head(CH)is always assigned in each cluster.However,this method has some weaknesses such as overload and premature death of CH when the number of UAVs increased.In order to solve this problem,we propose a dual-cluster-head based medium access control(DCHMAC)scheme for large-scale UAV networks.In DCHMAC,two CHs are elected to manage resource allocation and data forwarding cooperatively.Specifically,two CHs work on different channels.One of CH is used for intra-cluster communication and the other one is for inter-cluster communication.A Markov chain model is developed to analyse the throughput of the network.Simulation result shows that compared with FM-MAC(flying ad hoc networks multi-channel MAC,FM-MAC),DCHMAC improves the throughput by approximately 20%~50%and prolongs the network lifetime by approximately 40%.

Enhancing Dense Small Object Detection in UAV Images Based on Hybrid Transformer

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.

Sensing-Assisted Accurate and Fast Beam Management for Cellular-Connected mmWave UAV Network

Beam management,including initial access(IA)and beam tracking,is essential to the millimeter-wave Unmanned Aerial Vehicle(UAV)network.However,the conventional communicationonly and feedback-based schemes suffer a high delay and low accuracy of beam alignment,since they only enable the receiver to passively“hear”the information of the transmitter from the radio domain.This paper presents a novel sensing-assisted beam management approach,the first solution that fully utilizes the information from the visual domain to improve communication performance.We employ both integrated sensing and communication and computer vision techniques and design an extended Kalman filtering method for beam tracking and prediction.Besides,we also propose a novel dual identity association solution to distinguish multiple UAVs in dynamic environments.Real-world experiments and numerical results show that the proposed solution outperforms the conventional methods in IA delay,association accuracy,tracking error,and communication performance.

Multi-UAVs Collaborative Path Planning in the Cramped Environment

Due to its flexibility and complementarity, the multiUAVs system is well adapted to complex and cramped workspaces, with great application potential in the search and rescue(SAR) and indoor goods delivery fields. However, safe and effective path planning of multiple unmanned aerial vehicles(UAVs)in the cramped environment is always challenging: conflicts with each other are frequent because of high-density flight paths, collision probability increases because of space constraints, and the search space increases significantly, including time scale, 3D scale and model scale. Thus, this paper proposes a hierarchical collaborative planning framework with a conflict avoidance module at the high level and a path generation module at the low level. The enhanced conflict-base search(ECBS) in our framework is improved to handle the conflicts in the global path planning and avoid the occurrence of local deadlock. And both the collision and kinematic models of UAVs are considered to improve path smoothness and flight safety. Moreover, we specifically designed and published the cramped environment test set containing various unique obstacles to evaluating our framework performance thoroughly. Experiments are carried out relying on Rviz, with multiple flight missions: random, opposite, and staggered, which showed that the proposed method can generate smooth cooperative paths without conflict for at least 60 UAVs in a few minutes.The benchmark and source code are released in https://github.com/inin-xingtian/multi-UAVs-path-planner.

Panoptic UAV:Panoptic Segmentation of UAV Images for Marine Environment Monitoring

UAV marine monitoring plays an essential role in marine environmental protection because of its flexibility and convenience,low cost and convenient maintenance.In marine environmental monitoring,the similarity between objects such as oil spill and sea surface,Spartina alterniflora and algae is high,and the effect of the general segmentation algorithm is poor,which brings new challenges to the segmentation of UAV marine images.Panoramic segmentation can do object detection and semantic segmentation at the same time,which can well solve the polymorphism problem of objects in UAV ocean images.Currently,there are few studies on UAV marine image recognition with panoptic segmentation.In addition,there are no publicly available panoptic segmentation datasets for UAV images.In this work,we collect and annotate UAV images to form a panoptic segmentation UAV dataset named UAV-OUC-SEG and propose a panoptic segmentation method named PanopticUAV.First,to deal with the large intraclass variability in scale,deformable convolution and CBAM attention mechanism are employed in the backbone to obtain more accurate features.Second,due to the complexity and diversity of marine images,boundary masks by the Laplacian operator equation from the ground truth are merged into feature maps to improve boundary segmentation precision.Experiments demonstrate the advantages of PanopticUAV beyond the most other advanced approaches on the UAV-OUC-SEG dataset.