Design of Adaptive Fuzzy PID Altitude Control System for Unmanned Aerial Vehicle

Based on Matlab/Simulink and Fuzzy Logic toolboxes, the altitude control system is designed and simulated. The validity of conventional PID control method and adaptive fuzzy PID control method is compared. It can be drawn out that the adaptive fuzzy PID control method is superior to the conventional PID in rising time and overshoot etc. The effectiveness of a fuzzy PID controller shows potential application in the future, especially in the presence of model uncertainty or changing dynamics and time-varying parameters.

High Spatial Resolution Mapping of Dykes Using Unmanned Aerial Vehicle(UAV) Photogrammetry: New Insights On Emplacement Processes

Remote sensing has played a pivotal role in our understanding of the geometry of dykes and dyke swarms on Earth,Venus and Mars(West and Ernst,1991;Mege and Masson,1995;Ernst et al.,2005).Since the 1970’s

Review of Effective Vegetation Mapping Using the UAV (Unmanned Aerial Vehicle) Method

We tried more precise mapping of vegetation using UAV?(unmanned aerial vehicle), as a new method of creating vegetation maps, and we?objected to be clearly the efficient mapping of vegetation using the UAV method by comparing vegetation maps created by analysing aerial photographs taken by a UAV and an aircraft (manned flight). The aerial photography using UAV was conducted in the Niida River estuary (the secondary river flowing into Minamisoma City in Fukushima Prefecture, Japan). The photography period was in August 2013. We analysed the aerial photographs using ArcGis 9 (Esri Japan Corporation, Tokyo, Japan). The aerial photographs of the main plant communities (Phragmites australis,?Typha domingensis, and?Miscanthus sacchariflorus) taken by the UAV could clearly discriminate each plant community at the 1/50 scale. Moreover, it could clearly discriminate the shape of a plant at the 1/10 scale. We compared the vegetation maps by analysing the aerial photos taken by a UAV (2013 shooting) and an aircraft (2011 shooting). As a result, the vegetation map created by the UAV method could clearly discriminate community distributions. We conclude that vegetation surveys using UAV are possible and are capable of a highly precise community division in places where field reconnaissance is difficult. The UAV method is effective and will contribute to the improvement of research methods in the future;this method may reduce research costs associated with a reduction in field survey days and man-power.

Submarine Hunter: Efficient and Secure Multi-Type Unmanned Vehicles

Utilizing artificial intelligence(AI)to protect smart coastal cities has become a novel vision for scientific and industrial institutions.One of these AI technologies is using efficient and secure multi-environment Unmanned Vehicles(UVs)for anti-submarine attacks.This study’s contribution is the early detection of a submarine assault employing hybrid environment UVs that are controlled using swarm optimization and secure the information in between UVs using a decentralized cybersecurity strategy.The Dragonfly Algorithm is used for the orientation and clustering of the UVs in the optimization approach,and the Re-fragmentation strategy is used in the Network layer of the TCP/IP protocol as a cybersecurity solution.The research’s noteworthy findings demonstrate UVs’logistical capability to promptly detect the target and address the problem while securely keeping the drone’s geographical information.The results suggest that detecting the submarine early increases the likelihood of averting a collision.The dragonfly strategy of sensing the position of the submersible and aggregating around it demonstrates the reliability of swarm intelligence in increasing access efficiency.Securing communication between Unmanned Aerial Vehicles(UAVs)improves the level of secrecy necessary for the task.The swarm navigation is based on a peer-to-peer system,which allows each UAV to access information from its peers.This,in turn,helps the UAVs to determine the best route to take and to avoid collisions with other UAVs.The dragonfly strategy also increases the speed of the mission by minimizing the time spent finding the target.

Central Command Architecture for High-Order Autonomous Unmanned Aerial Systems

This paper is the first in a two-part series that introduces an easy-to-implement central command architecture for high-order autonomous unmanned aerial systems. This paper discusses the development and the second paper presents the flight test results. As shown in this paper, the central command architecture consists of a central command block, an autonomous planning block, and an autonomous flight controls block. The central command block includes a staging process that converts an objective into tasks independent of the vehicle (agent). The autonomous planning block contains a non-iterative sequence of algorithms that govern routing, vehicle assignment, and deconfliction. The autonomous flight controls block employs modern controls principles, dividing the control input into a guidance part and a regulation part. A novel feature of high-order central command, as this paper shows, is the elimination of operator-directed vehicle tasking and the manner in which deconfliction is treated. A detailed example illustrates different features of the architecture.

Low Altitude Satellite Constellation for Futuristic Aerial-Ground Communications

This paper discusses the significance and prospects of low altitude small satellite aerial vehicles to ensure smooth aerial-ground communications for next-generation broadband networks.To achieve the generic goals of fifthgeneration and beyondwireless networks,the existing aerial network architecture needs to be revisited.The detailed architecture of low altitude aerial networks and the challenges in resource management have been illustrated in this paper.Moreover,we have studied the coordination between promising communication technologies and low altitude aerial networks to provide robust network coverage.We talk about the techniques that can ensure userfriendly control and monitoring of the low altitude aerial networks to bring forth wireless broadband connectivity to a new dimension.In the end,we highlight the future research directions of aerial-ground communications in terms of access technologies,machine learning,compressed sensing,and quantum communications.

Altitude information fusion method and experiment for UAV

Altitude regulation is a fundamental problem in UAV(unmanned aerial vehicles) control to ensure hovering and autonomous navigation performance.However,data from altitude sensors may be unstable by interference.A digital-filter-based improved adaptive Kalman method is proposed to improve accuracy and reliability of the altitude measurement information.A unique sensor data fusion structure is designed to make different sensors switch automatically in different environment.Simulation and experimental results show that an improved Sage-Husa adaptive extended Kalman filter(SHAEKF) is adopted in altitude data fusion which means that altitude error is limited to 1.5m in high altitude and 1.2m near the ground.This method is proved feasible and effective through hovering flight test and three-dimensional track flight experiment.

A Grey Wolf Optimization-Based Tilt Tri-rotor UAV Altitude Control in Transition Mode

To solve the problem of altitude control of a tilt tri-rotor unmanned aerial vehicle(UAV)in the transition mode,this study presents a grey wolf optimization(GWO)based neural network adaptive control scheme for a tilt trirotor UAV in the transition mode.Firstly,the nonlinear model of the tilt tri-rotor UAV is established.Secondly,the tilt tri-rotor UAV altitude controller and attitude controller are designed by a neural network adaptive control method,and the GWO algorithm is adopted to optimize the parameters of the neural network and the controllers.Thirdly,two altitude control strategies are designed in the transition mode.Finally,comparative simulations are carried out to demonstrate the effectiveness and robustness of the proposed control scheme.

复杂城市低空无人机安全风险评估与三维路径规划

针对复杂城市环境内低空无人机飞行安全与效率亟待提升的问题,提出了复杂城市低空无人机安全风险评估与三维路径规划方法。首先,设计了无人机越界冲突率、缓冲空域占比指标,建立了无人机地理围栏安全缓冲间距优化模型,对最佳缓冲间距和栅格粒度进行了标定;然后,构建了由人口密度层、遮蔽层和障碍层构成的无人机风险地图,建立了弹道下降和失控滑行两种模式下的无人机对地风险评估模型,生成了精细化、组合化的城市低空概率风险地图;最后,综合利用地理围栏、概率风险地图和跳点搜索算法,对无人机三维路径进行了初始规划和优化重构。结果表明:弹道下降模式的伤亡风险是失控滑行下降模式的5~75倍;与A*算法相比,跳点搜索算法有效减少了飞行路径的转弯数量,缩短了求解时长,更适合规划无人机飞行路径;与不采用风险地图的方法相比,基于风险地图的无人机路径规划减少了50%的较高风险节点,相应的路径长度仅增加了7.2%和11.4%,整体路径节点的伤亡风险明显降低。研究成果可为复杂城市低空无人机飞行计划制定及安全运行监管提供理论依据和方法支撑。

低空空域无人机运行安全保障技术研究综述

低空空域飞行活动日趋频繁,无人机类型多样且性能差异较大,飞行过程中易与建筑物或其他航空器发生飞行冲突。为实现低空空域的规范化、安全化、精细化和智慧化管控,需不断提升保障无人机运行安全的技术手段,例如冲突风险探测、安全风险评估等。文章从无人机的轨迹预测与意图识别、低空空域飞行冲突探测、低空空域风险评估及预警告警等3方面进行了研究。首先,总结了在无人机轨迹预测和意图识别方面的研究成果和面临的问题挑战;其次,基于低空空域冲突探测形式,分析不同探测方法的优缺点;随后,围绕低空空域风险评估及预警告警技术,分析当前研究需要解决的核心问题;最后,对无人机冲突探测和风险评估技术等方面进行了展望。

基于自抗扰方法的无人机控制律设计

针对多平台、多任务的无人机飞行控制律的快速开发问题,提出一种基于自抗扰方法的无人机控制律架构,设计了可复用的扩展状态观测器及微分跟踪器,研究对比了其在3种具有较大差异的无人机平台中的应用,使7 000 kg的超音速UAV_A获得了更优的敏捷性结果,在60 kg的缩比UAV_B完成了5.8g的半滚倒转大过载机动飞行试验,基于10 kg的平直翼UAV_C实现了12架机紧编队的精确轨迹控制飞行试验。仿真及试飞结果表明:所提自抗扰控制结构响应快速、控制精度高、鲁棒性强,能够较好地适应多类无人机、面向多种任务场景的控制需求,且不需调参就能够获得较好的控制效果,为飞行控制算法设计提供了新的技术途径。

低空经济的技术创新与场景创新

低空经济作为我国重点发展的战略性新兴产业,是经济发展与技术突破的新引擎。低空经济具有空间多维性、产业整合性、技术经济性等特征。当前,我国低空经济的发展呈现出以无人机为代表的低空制造产业基础与低空经济应用场景不断融合的态势。未来,低空经济高质量发展须回应好“如何实现制造产业与新场景的互通互融,创造低空经济新业态”这一核心议题。为此,应处理好航空器的效率与安全、研制与运行、空域的开放与管制三大关系,构建低空航空产品创新与场景创新相融相促的低空经济理论模型,推动促进低空经济增长的理论创新,拓展低空经济产业发展新路径。

我国低空经济的技术经济范式分析与发展对策

低空经济作为我国战略性新兴产业,具有典型的新质生产力特征。从低空经济的发展看,其符合技术经济范式的一般特征。低空经济发展遵循包含驱动力、新结构与新形态、价值创造活动、价值创造结果四要素的范式。目前,我国低空经济发展总体处于起步阶段,政策风口效应明显,发展前景广阔。依照技术经济范式,从驱动力看,我国正在逐步构建低空技术牵引、低空基础设施保障、低空经济政策助推的驱动力体系;从新结构与新形态看,我国低空经济已经广泛融入农业生产、观光旅游、物流配送、环境监测、应急救援等领域,在信息维度、能量维度、情绪维度的价值不断显现,同时部分城市依托低空经济产业链走出低空经济特色发展之路;从价值创造活动看,我国在低空飞行器制造、低空服务、低空运营维保等领域涌现出一批代表性企业,低空经济的场景和规模不断扩充,价值创造活动前景广阔;从价值创造结果看,近年来我国低空经济规模保持较快增长,在直接带动经济增长的同时展现出降低成本、提高效率、促进就业等社会效益。总体而言,发展低空经济是一项系统工程。尽管我国通用航空发展为低空经济奠定了基础,但与发达国家相比,我国低空经济发展仍然存在诸多不足,顶层设计亟待加强,体制机制有待理顺,技术创新有待加强,场景拓展有待加快,综合成本有待下降,行业保障有待提升。因此,应制定战略规划,统筹低空经济发展,优化低空空域管理,加大财政金融支持,推动低空经济产业链协同发展,加快技术创新与人才队伍建设,加强低空经济基础设施和法规标准体系建设以及安全保障。

采用局部-全局区域重检测机制的无人机长期跟踪算法

为解决基础跟踪器面对遮挡和移出视野等长期跟踪场景时易出现跟踪失败等问题,提出了一种基于局部-全局区域重检测的无人机长期跟踪算法。设计了基础滤波器,将高置信度样本与其结合,并融入自适应时空正则化,解决了滤波器退化问题,提高了模型鲁棒性以及复杂场景下的性能;优化了滤波器更新策略,通过评价跟踪结果进行自适应更新;设计快速尺度滤波器,解决了跟踪过程中的尺度变化问题;设计了局部-全局区域重检测机制,跟踪失败时启动重检测器恢复跟踪目标,先完成局部区域重检测,若恢复跟踪失败,再利用全局区域重检测器继续恢复目标跟踪状态。实验结果表明:所提算法在UAV20L数据集上的精确度和准确率分别可达0.724和0.621,与基于时空正则化相关滤波器的跟踪算法(STRCF)相比分别提升了25.9%和20.6%,与同类主流算法相比,跟踪效果得到提升,证明了算法的有效性。

无人机低空遥感结合YOLOv7快速评估水稻穗颈瘟抗性

为解决传统水稻稻瘟病抗性评估手段单一、效率低的问题,该研究提出一种无人机低空遥感技术结合YOLOv7模型的水稻穗颈瘟抗性鉴定方法。首先,将标注区域分割成小尺寸图像(≤1 240×1 240像素),将小尺寸图像进行旋转、缩放、平移、剪切和改变对比度处理。经数据清洗,去除分辨率过低的图像,扩充样本数量,以提高数据多样性。然后,将压缩注意力机制(squeeze-excitation attention)和可变形卷积(deformable convolution)引入YOLOv7模型,自适应调整感受野,以提升捕捉穗颈瘟病斑细粒度特征的能力。最后,构建穗颈瘟检测的YOLOv7_Neckblast模型。研究结果表明,YOLOv7_Neckblast能够有效检测穗颈瘟,计算出15个品种的穗颈瘟发病率和病害等级(1、3、5、7和9级的水稻品种分别有4、4、3、2和2个)。在交并比阈值为0.5时,YOLOv7_Neckblast平均精度均值相较于YOLOv7、FCOS和RetinaNet分别提升了4.0、6.4和5.8个百分点,召回率和F1值分别提高了至少4.0和4.0个百分点,且浮点计算数和参数量最低。与育种专家判定的实际抗性水平相比,YOLOv7_Neckblast模型对15个水稻品种的穗颈瘟抗性水平的平均评估准确率为86.67%,能较好地实现不同水稻品种穗颈瘟抗性的区分。无人机低空遥感融合人工智能技术可用于水稻黄熟期育种中穗颈瘟抗性的评估,也可为其他作物优势品种的选育提供参考。

数字低空空域栅格化的表征度量与最优标定

低空飞行空间范围小、目标速度慢、环境要素杂,传统的经纬度表征方式无法满足智联环境下的低空精细管理要求,为此本文研究了数字低空空域栅格化的表征度量与最优标定问题。首先,从“点-线-面”视角构建了多维度低空空域结构要素量化表征规则,提出了低空空域多层级栅格量化表征方法;然后,通过判定不同空域栅格的“点-线-面”位置关系,提出了基于栅格交集矩阵的低空空域拓扑关系度量方法;最后,综合考虑低空无人机碰撞指数、低空栅格利用指数等优化目标,以及节点与栅格匹配约束、空间位置约束、无人机与无人机/障碍物安全约束等限制条件,建立了面向多维性能的低空空域栅格粒度最优标定模型。针对城市低空典型飞行任务场景对所提方法的有效性及优化效果进行了验证分析。实验结果表明,针对任意设定的低空空域和无人机飞行任务,在可接受的无人机碰撞指数和栅格利用指数下,所提方法可对数字低空栅格粒度进行最优配置,从根源上有效确保低空飞行活动的安全和高效。研究成果对于支撑数字低空空域精细化管理和异质飞行器融合运行具有一定的理论价值和应用意义。

基于低空视觉的直升机旋翼轴心轨迹测量法

为满足直升机飞行状态旋翼轴心轨迹测量需求,提出了一种基于低空双目视觉的轴心轨迹测量方法。首先,针对高速旋转体的小量测量,基于立体视觉测量原理设计构建了以无人机为低空移动平台的旋翼轴心轨迹视觉测量系统。其次,针对动-动测量环境条件,提出了地面标定与空中动态实时标定联合的测量系统标定方法。然后,基于空间几何关系,论述了旋翼轴心静态估计与动态轨迹测量方法;最后,进行了测量系统地面精度验证试验与直升机地面开车状态的旋翼轴心轨迹测量试验。试验结果表明,该方法准确可靠,点间距离精度优于1‰,可实现轴心轨迹的非接触精确测量,还可推广应用于其他动态小量测量任务。

基于空中可重构智能表面的低轨卫星高通量通信

卫星通信凭借其全球覆盖、抗干扰能力强和稳定的通信质量,在通信领域展现出广阔的应用前景。针对新一代通信技术对于高速率、低时延和覆盖增强的需求,提出了一种低轨卫星、无人机和可重构智能表面协作的空天地一体化网络框架。从高通量卫星通信的角度出发,制订了一个通信容量最大化方案。该方案基于最大化-最小化算法和深度确定性策略梯度算法联合优化反射相移和无人机轨迹。仿真结果表明,所提方案能够显著提升通信容量。

从飞行汽车看低空经济新业态

近年来,我国低空经济呈现蓬勃发展态势,相关政策密集出台,企业也作出了积极响应,行业发展确定性进一步增强,相关产业取得长足发展,特别是无人机领域处于世界先进水平,但我国低空经济整体水平与欧美发达国家相比仍有一定差距。通用航空、无人机、飞行汽车是低空经济的主要产业领域,特别是近年出现的飞行汽车被视为低空经济发展的新引擎。在飞行汽车研发方面,国内外均尚处于起步阶段,我国在若干局部技术上领先,但并未形成明显优势。未来,需推动飞行汽车品质和关键技术的不断突破,同时,也要加紧筹划和完善配套的法律法规、基础设施、行业标准、运行环境及监管政策以推动城市空中交通(UAM)体系的建设和低空经济的整体发展。

基于模糊贝叶斯网络的低空无人机运行风险评估

为解决目前城市低空环境下的无人机(UAV)运行安全风险问题,利用模糊贝叶斯网络(FBN)识别和评估低空无人机运行的关键风险因素。首先,在分析低空无人机运行流程基础上,从人-机-环-管角度分析低空无人机运行过程的风险因素。随后,利用GeNIe软件构建低空无人机运行风险评估贝叶斯网络(BN),利用专家先验知识和模糊集量化分析底事件的先验概率。最后,开展单因素分析、双因素分析以及敏感性分析,并以实例验证网络可行性。研究结果表明:低空无人机运行的关键风险因素为无人机电池故障、运行航线的环境障碍物以及无人机运行监管技术;FBN反向推理分析结果显示,环境相关风险(79%)、无人机设备风险(60%)是无人机运行过程主要风险因素。