“无人机”项目管理中的问题研究

针对"无人机"项目管理中知识管理的若干问题,结合理论实践,本文首先对知识管理特性做了简要介绍,然后深入分析了在"无人机"项目管理应用知识管理的作用,最后提出目前我国"无人机"项目管理中知识管理中存在问题和解决对策。得出通过应用知识管理,可有效提升"无人机"项目管理水平和质量的结论,希望通过本文阐述,可以对相关工作人员提供一些帮助。

人工智能背景下“无人机测绘技术”专业课程改革探索

近年来,随着人工智能的蓬勃发展,测绘工程行业也向智能化发展,“无人机测绘技术”作为测绘工程专业的一门新兴课程,具有专业性强、知识范围广泛、内容多、理论抽象化等特点。因此,传统的测绘专业课程模式已经不能满足人才培养的需要。从实际出发,分析了目前“无人机测绘技术”专业课程存在的问题,形成了人工智能背景下“无人机测绘技术”课程改革规划,从而为该课程构建课堂提供新思路。

“无人机”项目管理中知识管理的若干问题研究

分析了“无人机”项目的特殊性，提出了该项目管理过程中应用知识管理的重要性；探讨了当前“无人机”项目知识管理中存在的若干问题，并提出了通过以无人机领域本体为核心的知识发现体系，建立知识网络，人性化知识管理策略和成立知识创新小组这四种方法来克服这些问题，从而更好地发挥知识管理在“无人机”项目管理中的作用。

“三教”改革背景下“无人机摄影测量技术”课程的改革与思考

中国特色高水平高职学校和专业建设计划即“双高计划”,其中的“高”应该落实在人才培养规格高和院校办学质量高2个层面。关于教师、教材和教法的“三教”改革是“双高”计划实施的载体,目标是对接技术进步所带来的现代化经济发展对技术技能人才的新需求,解决的核心问题是赋予学生可持续发展的职业能力,促使学生适应技术不断革新的就业变迁。以“无人机摄影测量技术”课程尝试开展“三教”改革为抓手,以模块化课程建设为纽带,促进课程团队成员内驱发展,建设活页式教材,开发课证融通的在线开放课,以项目化教学为核心,突出学生学习主体地位。总结改革经验,以供讨论。

“无人机倾斜摄影测量+1”测量方法在房地一体项目中的应用探讨

2020年底,广西农村房地一体项目启动,至今完成了所有区县的项目外业作业,作业过程中发现存在以下问题:广西山区多、地形变化大,部分地区交通设施不完善,居民点分布零散,房屋构建情况复杂等。文章结合广西农村房地一体项目的特点与难点,总结广西地区农村房地一体项目普遍的外业测量方法,得出“无人机倾斜摄影测量+1”的测量方式具有较强的推广价值,可在未来广西地籍测量、不动产登记、自然资源登记等项目的外业工作中广泛应用。

Research on fast detection method of infrared small targets under resourceconstrained conditions

Infrared small target detection is a common task in infrared image processing.Under limited computa⁃tional resources.Traditional methods for infrared small target detection face a trade-off between the detection rate and the accuracy.A fast infrared small target detection method tailored for resource-constrained conditions is pro⁃posed for the YOLOv5s model.This method introduces an additional small target detection head and replaces the original Intersection over Union(IoU)metric with Normalized Wasserstein Distance(NWD),while considering both the detection accuracy and the detection speed of infrared small targets.Experimental results demonstrate that the proposed algorithm achieves a maximum effective detection speed of 95 FPS on a 15 W TPU,while reach⁃ing a maximum effective detection accuracy of 91.9 AP@0.5,effectively improving the efficiency of infrared small target detection under resource-constrained conditions.

Improved YOLOv8-Based Target Detection Algorithm for UAV Aerial Image

In response to the challenge of low detection accuracy and susceptibility to missed and false detections of small targets in unmanned aerial vehicles(UAVs)aerial images,an improved UAV image target detection algorithm based on YOLOv8 was proposed in this study.To begin with,the CoordAtt attention mechanism was employed to enhance the feature extraction capability of the backbone network,thereby reducing interference from backgrounds.Additionally,the BiFPN feature fusion network with an added small object detection layer was used to enhance the model's ability to perceive for small objects.Furthermore,a multi-level fusion module was designed and proposed to effectively integrate shallow and deep information.The use of an enhanced MPDIoU loss function further improved detection performance.The experimental results based on the publicly available VisDrone2019 dataset showed that the improved model outperformed the YOLOv8 baseline model,mAP@0.5 improved by 20%,and the improved method improved the detection accuracy of the model for small targets.

新冠肺炎疫情防控中“无人机+”引发的思考

无人机系统在新冠肺炎疫情防控中,在飞行宣传、防疫监控、交通疏导、病毒消杀、远程测温等工作中发挥突出作用,为疫情预防、延缓蔓延提供了技术保障;无人机系统在公安实践中能突破地理制约和信息阻碍,高效完成空中监控、现场取证、搜索救援等急、难、险、重任务。"无人机+"警务实践探索中,警用无人机系统尚有与公安业务系统应需深度融合、功能需多样化、通信传输数据需链化、有效载荷应警用化、监管应规范化等方面的提升空间。

“无人机+”警务模式的实践探索

无人机作为无人航空载具因其灵活性、实用性、智能化、方便操作等特点,成为各执法部门的重要装备,特别是在公安机关的实战应用中发挥着重要作用。云南省红河州金平县公安局在日常警务工作和特殊警务活动中,充分发挥无人机“查得准、盯得住、传得快、看得全、易操作”的特点和快捷高效地完成空中监控、现场取证、搜索救援等急、难、险、重任务的优势,突破地理制约和信息阻碍,实现可视化指挥、视频导侦、情报研判“三位一体”,提高打击犯罪、服务群众效率,提升警务实战制胜力和核心战斗力,向“科技强警”迈出了坚实的一步。

Monitoring coal fires in Datong coalfield using multi-source remote sensing data

Numerous coal fires burn underneath the Datong coalfield because of indiscriminate mining.Landsat TM/ETM,unmanned aerial vehicle（UAV）,and infrared thermal imager were employed to monitor underground coal fires in the Majiliang mining area.The thermal field distributions of this area in 2000,2002,2006,2007,and 2009 were obtained using Landsat TM/ETM.The changes in the distribution were then analyzed to approximate the locations of the coal fires.Through UAV imagery employed at a very high resolution（0.2 m）,the texture information,linear features,and brightness of the ground fissures in the coal fire area were determined.All these data were combined to build a knowledge model of determining fissures and were used to support underground coal fire detection.An infrared thermal imager was used to map the thermal field distribution of areas where coal fire is serious.Results were analyzed to identify the hot spot trend and the depth of the burning point.

AC-PSO ALGORITHM FOR UAV MISSION PLANNING

Choosing the best path during unmanned air vehicle （UAV） flying is the target of the UAV mission planning problem. Because of its nearly constant flight height, the UAV mission planning problem can be treated as a 2-D （horizontal） path arrangement problem. By modeling the antiaircraft threat, the UAV mission planning can be mapped to the traveling seaman problem （TSP）. A new algorithm is presented to solve the TSP. The algorithm combines the traditional ant colony system （ACS） with particle swarm optimization （PSO）, thus being called the AC-PSO algorithm. It uses one by one tour building strategy like ACS to determine that the target point can be chosen like PSO. Experiments show that AC-PSO synthesizes both ACS and PSO and obtains excellent solution of the UAV mission planning with a higher accuracy.

REAL-TIME TRACKING FOR FAST MOVING OBJECT ON COMPLEX BACKGROUND

A real-time tracking system for the fast moving object on the complex background is proposed.The Markov random filed（MRF）model based background subtraction algorithm is used to detect the changing pixels and track the moving object.The prior probability of the segmentation mask is modeled by using MRF,and the object tracking task is translated into the maximum a-posterior（MAP）problem.Experimental results show that the method is efficient at both offline and online moving objects on simple and complex background.

GUIDANCE AND CONTROL FOR AUTOMATIC LANDING OF UAV

A scheme of guidance and control is presented to meet the requirements for automatic landing of unmanned aerial vehicles (UAVs) based on the airborne digital flight control system and radio tracker on ground station. An automatic landing system is realized for an unmanned aerial vehicle. The results of real time simulation and flight test are given to illustrate the effectiveness and availability of the scheme. Results meet all the requirements for automatic landing of the unmanned aerial vehicle.

INTEGRATED DESIGN OF AN ELECTRIC POWERED UNMANNED AIR VEHICLE USING CONCURRENT SUBSPACE DESIGN

The concurrent subspace design (CSD) framework has been used to conduct a preliminary design optimization of an electric powered, unmanned air vehicle (EPUAV) operating at a low Reynolds number. A multidisciplinary system analysis that includes aerodynamics, weights, propulsion, performance and stability and control has been developed for this class of vehicles. The CSD framework employs artificial neural network based response surfaces to provide approximations to the design space. The EPUAV system includes 25 continuous and 4 discrete design variables. The CSD framework was able to identify feasible designs with significant weight reductions relative to any previously considered (i.e. initial database) designs. This was accomplished with a limited number of system analyses. The results also demonstrate the nature of this design framework adaptive to changes in design requirements.

IMPROVED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR INTELLIGENTLY SETTING UAV ATTITUDE CONTROLLER PARAMETERS

An improved particle swarm optimization （PSO） algorithm is investigated in the optimization of the attitude controller parameters of unmanned aerial vehicle （UAV）. Considering the stagnation phenomenon in the later phase of the basic PSO algorithm caused by the diversity scarcity of particles, a modified PSO algorithm is presented. For the basic PSO algorithm, the velocity of each particle is adjusted according to the inertia motion, the swarm previous best position and its own previous best position. However, in the improved PSO algorithm, each particle only learns from another randomly selected particle with higher performance, besides keeping the inertia motion. The inertia weight of the improved PSO algorithm is a random number. The modification decreases the uncertain parameters of the algorithm, simplifies the learning mechanism of the particle, and enhances the diversity of the swarm. Furthermore, a UAV attitude control system is built, and the improved PSO algorithm is applied in the optimized tuning of four controller parameters. Simulation results show that the improved PSO algorithm has stronger global searching ability than the common PSO algorithms, and obtains better UAV attitude control parameters.

Effects of Supplementary Pollination by Single-rotor Agricultural Unmanned Aerial Vehicle in Hybrid Rice Seed Production

In 2012-2015, under the conditions of different natural wind speeds, the single-rotor agricultural unmanned aerial vehicle was used for the supplementary pollination during seed production of 10 hybrid combinations with big parental row ratios in the hybrid rice seed production bases of Hunan, Hainan and Guangdong Province, and the pollination effects were studied through the investigation of pollen density in the field, outcrossing seeding rate of female parent and seed production yield. The results showed that under the parental row ratio of 6：（40-60）, the seed setting rate and yields of the supplementary pollination by single-rotor agricultural UAV could reach and even higher than those of artificial pollination, indicating the single-rotor agricultural UAV could be used in supplementary pollination for hybdd rice seed production, which could promote the whole-process mechanization of seed production.

ADAPTIVE NEURAL NETWORK ATTITUDE CONTROL FOR UNMANNED HELICOPTER

Adaptive flight control technology, feedback linearization, model inversion theory are reviewed and the error dynamic characteristics are analyzed, and an adaptive on-line neural network attitude control system is presented. The model inversion is under the hover condition. And the adaptive control law based on the neural network is designed to guarantee the boundedness of tracking error and control signals. Simulation results demonstrate that the nonlinear neural network augmented model inversion can self-adapt to the uncertainty and modeling errors of unmanned helicopters. Results are compared while the parameters of PD controller and robustness items are changed.

Cooperative task allocation for heterogeneous multi-UAV using multi-objective optimization algorithm

The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV.Accordingly,several constraints should be satisfied to realize the efficient cooperation,such as special time-window,variant equipment,specified execution sequence.Hence,a proper task allocation in UAVs is the crucial point for the final success.The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics.To this end,a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints.In addition,four optimization objectives:completion time,target reward,UAV damage,and total range,are introduced to evaluate various allocation plans.Subsequently,to efficiently solve the multi-objective optimization problem,an improved multi-objective quantum-behaved particle swarm optimization(IMOQPSO)algorithm is proposed.During this algorithm,a modified solution evaluation method is designed to guide algorithmic evolution;both the convergence and distribution of particles are considered comprehensively;and boundary solutions which may produce some special allocation plans are preserved.Moreover,adaptive parameter control and mixed update mechanism are also introduced in this algorithm.Finally,both the proposed model and algorithm are verified by simulation experiments.

An adaptive backstepping sliding mode method for flight attitude of quadrotor UAVs

To overcome nonlinear and 6-DOF(degrees of freedom)under-actuated problems for the attitude and position of quadrotor UAVs,an adaptive backstepping sliding mode method for flight attitude of quadrotor UAVs is proposed,in which an adaptive law is designed to online estimate the parameter variations and the upper bound of external disturbances and the assessments is utilized to compensate the backstepping sliding mode control.In addition,the tracking error of the design method is shown to asymptotically converge to zero by using Lyapunov theory.Finally,based on the numerical simulation of quadrotor UAVs using the setting parameters,the results show that the proposed control approach can stabilize the attitude and has hover flight capabilities under the parameter perturbations and external disturbances.

Cultivated land information extraction in UAV imagery based on deep convolutional neural network and transfer learning

The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. As a new means of monitoring the ground in recent years, unmanned aerial vehicle （UAV） low-height remote sensing technique, which is flexible, efficient with low cost and with high resolution, is widely applied to investing various resources. Based on this, a novel extraction method for cultivated land information based on Deep Convolutional Neural Network and Transfer Learning （DTCLE） was proposed. First, linear features （roads and ridges etc.） were excluded based on Deep Convolutional Neural Network （DCNN）. Next, feature extraction method learned from DCNN was used to cultivated land information extraction by introducing transfer learning mechanism. Last, cultivated land information extraction results were completed by the DTCLE and eCognifion for cultivated land information extraction （ECLE）. The location of the Pengzhou County and Guanghan County, Sichuan Province were selected for the experimental purpose. The experimental results showed that the overall precision for the experimental image 1, 2 and 3 （of extracting cultivated land） with the DTCLE method was 91.7%, 88.1% and 88.2% respectively, and the overall precision of ECLE is 9o.7%, 90.5% and 87.0%, respectively. Accuracy of DTCLE was equivalent to that of ECLE, and also outperformed ECLE in terms of integrity and continuity.