Abstract A Survey of Unmanned Aerial Vehicles (UAV) for Traffic Surveillance
- 格式:pdf
- 大小:921.66 KB
- 文档页数:29
关于无人机的英语作文Unmanned Aerial Vehicles: The Future of Aviation。
Introduction。
In recent years, the rapid advancements in technology have led to the emergence of a new and revolutionary form of aviation – unmanned aerial vehicles (UAVs), commonly known as drones. These sophisticated machines have captured the imagination of the public and transformed various industries, from military operations to commercial applications. As the world continues to embrace this innovative technology, it is crucial to understand the profound impact that UAVs have had and will continue to have on our society.The Rise of Unmanned Aerial Vehicles。
Unmanned aerial vehicles have their origins in military applications, where they were initially used forreconnaissance and surveillance purposes. However, as the technology has evolved, the use of UAVs has expanded significantly, with a wide range of civilian and commercial applications emerging.One of the key drivers behind the growth of UAVs istheir ability to perform tasks that are often too dangerous, expensive, or impractical for manned aircraft. For example, UAVs can be used to survey remote or inaccessible areas, monitor environmental changes, and assist in search and rescue operations. Additionally, the cost-effectiveness of UAVs, compared to traditional manned aircraft, has madethem an attractive option for many industries.The Military Applications of UAVs。
无人机航测技术在地质灾害应急测绘中的研究与应用——以6.16太原山体滑坡应急测绘为例刘建勋(山西豪正森资源环境规划设计有限公司,山西 太原 030000)摘 要:本文介绍了无人机航测技术的特点,从应急预案、航摄作业和数据处理三个方面对应急测绘的方法进行了阐述,说明了地质灾害应急测绘的需求。
结合太原山体滑坡的应急测绘案例,反映了无人机航测技术在地质灾害应急测绘中的应用,了解了其及时、安全和有效的特点,具有一定的推广价值。
关键词:无人机;航测;地质灾害;应急测绘中图分类号:P694 文献标识码:A 文章编号:1002-5065(2020)19-0125-2Research and application of uav aerial survey technology in geological disaster emergency mapping -A case study of emergency mapping of the Landslide in Taiyuan on 6.16LIU Jian-xun(Shanxi Haozhengsen Resource environment Planning and Design Co.LTD, Taiyuan 030000,China)Abstract: This paper introduces the characteristics of unmanned aerial vehicle(UAV)aerial surveying technology,expounds the methods of emergency surveying and mapping from the three aspects of emergency plan,aerial photography operation and data processing, and explains the needs of emergency surveying and mapping for geological disasters.Combined with the emergency surveying and mapping case of taiyuan landslide,this paper reflects the application of uav aerial surveying and mapping technology in the emergency surveying and mapping of geological disasters,and understands its characteristics of timeliness,safety and effectiveness,which has certain popularization value.Keywords: UAV;Aerial;Geological hazards;The emergency of surveying and mapping常见的地质灾害类型有山体滑坡、崩塌、泥石流等,均是地质环境异常变化和地质活动产生的灾害。
考研英语无人机作文英文回答:In the realm of modern technology, the advent of unmanned aerial vehicles (UAVs), commonly known as drones, has revolutionized various societal sectors. Drones have become ubiquitous in applications ranging from military reconnaissance to commercial photography and delivery. However, the proliferation of drones has also raised concerns regarding their potential misuse and the need for ethical governance.When considering the use of drones, it is crucial to weigh both their benefits and potential drawbacks. On the one hand, drones offer numerous advantages. They can provide an aerial perspective, facilitating tasks such as search and rescue operations during natural disasters or monitoring crops in agriculture. Drones also offer efficiency and cost-effectiveness in industries such as delivery and surveillance.On the other hand, drones can pose risks to privacy and safety. The unauthorized use of drones for surveillance can infringe upon individuals' rights and freedoms. Furthermore, drones can be equipped with weapons or used to deliverillicit goods, raising concerns about their misuse for malicious purposes.To address these concerns, it is essential to establish clear regulations and guidelines for the use of drones. Governments should play a central role in defining the permissible uses of drones, setting standards for their operation, and enforcing penalties for violations. Additionally, manufacturers must prioritize the development of robust safety features and technologies to minimize the risks associated with drone operation.Ethical considerations must also guide the use of drones. Operators should adhere to principles of privacyand respect for others. They should obtain informed consent before conducting surveillance and avoid using drones in ways that could endanger public safety or cause harm.International cooperation is crucial in addressing the challenges posed by drones. Shared standards and regulations can help prevent the misuse of drones across borders and ensure responsible use on a global scale. By working together, nations can establish a framework that balances the benefits of drones with the need to protect privacy, safety, and ethical values.中文回答:无人机作为现代科技的产物,已在军事侦察、商业摄影、运输等多个方面得到广泛应用,为社会各领域带来了巨大变革。
2020年9月Chinese Journal of Intelligent Science and Technology September 2020 第2卷第3期智能科学与技术学报V ol.2No.3 基于无人机的边缘智能计算研究综述董超1,沈赟1,屈毓锛2(1. 南京航空航天大学电子信息工程学院,江苏南京 211106;2. 上海交通大学计算机科学与工程系,上海 200240)摘 要:边缘智能计算是指将用户节点产生的计算密集型任务卸载到计算能力更强的边缘服务器上进行处理,而基于无人机的边缘智能计算是指在此基础上结合智能无人机平台,利用该平台机动性强、易于部署的优点,更加快速灵活地为地面用户设备提供边缘计算服务。
此外,无人机也可作为用户节点,将其计算密集型任务卸载到地面边缘服务器上来执行。
针对无人机作为用户节点和边缘服务器两种不同场景,根据最小化能耗、最小化时延和最大化效用等不同的优化目标对当前基于无人机的边缘智能计算研究进行了分类和总结,并对下一步的研究方向进行了思考与展望。
关键词:边缘智能计算;无人机;任务卸载中图分类号:TP391文献标识码:Adoi: 10.11959/j.issn.2096−6652.202025A survey of UAV-based edge intelligent computingDONG Chao1, SHEN Yun1, QU Yuben21. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China2. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaAbstract: Edge intelligent computing refers to the offloading of computationally intensive tasks generated by user nodes to edge servers with stronger computing capabilities for processing. Unmanned aerial vehicle (UA V)-based edge intelli-gent computing combines intelligent drone platforms on this basis and utilizes them with the advantages of strong mobil-ity and easy deployment, it can provide edge computing services for ground user equipment more quickly and flexibly. At the same time, drones can also be used as user nodes to off load their computationally intensive tasks to the ground edge server for execution. Aiming at two different scenarios of UA V as a user node or an edge server, the current research on edge intelligent computing based on UA V is classified and summarized according to different optimization goals such as minimizing energy consumption, minimizing delay and maximizing utility, and the next research direction is considered and prospected.Key words: edge intelligent computing, unmanned aerial vehicle, task off loading1引言无人机在拥有部署容易、灵活性强和应用范围广等优点的同时也面临着一些挑战,比如其十分有限的电池容量和相对较弱的计算能力。
中国无人机英语作文1Drones, also known as unmanned aerial vehicles (UA Vs), have become increasingly popular in recent years. They are aircraft without a human pilot on board and are controlled remotely or autonomously. Drones have a wide range of uses, including aerial photography, surveillance, and logistics delivery.In China, drones have made remarkable progress in both technology and application. Chinese drones are renowned for their advanced technology and high quality. For example, DJI, a leading Chinese drone manufacturer, has gained worldwide recognition for its innovative products. DJI drones are widely used by photographers, videographers, and professionals in various fields.One of the major advantages of Chinese drones is their technological innovation. Chinese companies have been investing heavily in research and development to improve the performance and functionality of drones. They have developed advanced features such as long flight times, high-resolution cameras, and stable flight control systems. These technological advancements have made Chinese drones more competitive in the global market.In addition to technological innovation, Chinese drones also enjoy alarge market share. Chinese drone manufacturers have been able to produce high-quality drones at competitive prices, which has led to a significant increase in demand. Chinese drones are exported to many countries around the world and are widely used in various industries.The success of Chinese drones can be attributed to several factors. Firstly, China has a strong manufacturing base and a large pool of talented engineers and technicians. This has enabled Chinese companies to produce high-quality drones at a lower cost. Secondly, the Chinese government has been supportive of the development of the drone industry by providing policies and financial support. Thirdly, Chinese companies have been proactive in marketing their products and building brand awareness.In conclusion, Chinese drones have made significant progress in technology and market share. They are playing an important role in various fields and are expected to continue to grow in popularity in the future. With their advanced technology and high quality, Chinese drones are set to lead the global drone market.2China's drones have made remarkable contributions to various fields, especially in agriculture. In the vast rural areas of China, drones are playing an increasingly important role.Drones are widely used in agricultural production. For example, they can be used to spray pesticides. Compared with traditional methods, dronespraying is more efficient and accurate. It can cover a large area in a short time, reducing labor intensity and improving work efficiency. Moreover, drones can also monitor the growth of crops. By taking high-resolution images, farmers can observe the growth status of crops in real time, detect diseases and pests in time, and take corresponding measures.The application of drones in agriculture is a significant manifestation of China's agricultural modernization. China has been continuously increasing investment in drone research and development. Through the efforts of scientific researchers and enterprises, a series of advanced drone technologies have been developed. These technologies not only meet the needs of domestic agricultural production but also are exported to many countries and regions, winning wide acclaim.In conclusion, China's drones have brought new opportunities and possibilities to agricultural modernization. With the continuous progress of technology, drones will play an even more important role in promoting agricultural development and rural revitalization.3China's drone industry has witnessed a remarkable journey from its humble beginnings to achieving a world-leading position. In the early days, China's drone technology was in its infancy. However, with the determination and efforts of researchers and engineers, significant progress has been made.The development of China's drone industry can be attributed to several key factors. Firstly, continuous technological breakthroughs have played a crucial role. Innovations in areas such as flight control systems, battery technology, and camera capabilities have enhanced the performance and functionality of drones. For example, the improvement in flight stability and endurance has made drones more reliable for various applications.Secondly, policy support has been instrumental. The government has recognized the potential of the drone industry and has implemented policies to encourage research and development, as well as promoting the commercialization of drones. This has provided a favorable environment for the growth of the industry.Over the years, Chinese drone manufacturers have made great strides in the global market. They have not only offered high-quality products at competitive prices but also provided excellent after-sales service. Brands like DJI have become household names worldwide, dominating the consumer drone market.In addition to consumer drones, China has also made significant progress in the field of industrial drones. These drones are being used in various sectors such as agriculture, surveying, and disaster relief. For instance, in agriculture, drones are used for crop spraying and monitoring, improving efficiency and reducing labor costs.The success of China's drone industry is a testament to the country's innovation and manufacturing capabilities. It has not only brought convenience and efficiency to people's lives but also made important contributions to various industries. As technology continues to advance, it is expected that China's drone industry will continue to thrive and lead the way in the global market.4Drones have become increasingly popular in China in recent years, especially in the field of entertainment. One of the most exciting applications of drones is in drone shows. These shows involve a large number of drones flying in formation to create beautiful patterns and images in the sky. They are often used in major events and festivals, bringing people a lot of fun and surprises.Drone shows are not only visually stunning but also highly creative. The drones can be programmed to perform complex maneuvers and display a wide variety of colors and shapes. This makes them a unique form of entertainment that can attract audiences of all ages. Moreover, drone shows are environmentally friendly as they do not produce any pollution or noise.In addition to drone shows, drones are also being used in other areas of entertainment. For example, some people use drones to take aerial photos and videos, which can capture beautiful landscapes and moments from a unique perspective. Drones can also be used in gaming and racing,adding a new dimension to these activities.Looking to the future, the development of drones in China is expected to continue. As technology advances, drones will become more intelligent and capable. They may be used in more fields such as delivery services, disaster relief, and environmental monitoring. In the entertainment field, we can expect to see more innovative uses of drones, such as interactive drone shows and virtual reality experiences.In conclusion, drones have brought a lot of fun and surprises to people's lives in China. Whether it's through drone shows or other applications, drones are changing the way we experience entertainment. With the continuous development of technology, we can look forward to even more exciting uses of drones in the future.5China's drones have made remarkable achievements and gained wide acclaim in the international market. There are several key factors contributing to their competitiveness.First and foremost, technological advantages play a crucial role. Chinese drone manufacturers have been constantly investing in research and development, resulting in advanced features such as high-resolution cameras, long flight times, and stable flight performance. These drones are equipped with intelligent flight control systems, enabling them to perform complex tasks with precision and ease. Moreover, the application of newmaterials and manufacturing processes has improved the durability and reliability of Chinese drones.In addition to technological advantages, price advantage is also an important factor. Chinese drones offer competitive prices without sacrificing quality. This makes them accessible to a wide range of customers, from professional photographers and filmmakers to hobbyists and businesses. The cost-effectiveness of Chinese drones has helped them gain a larger market share.Looking ahead, the future of China's drone industry is promising. With the continuous advancement of technology, we can expect even more innovative features and capabilities in Chinese drones. For example, the integration of artificial intelligence and 5G technology will further enhance their performance and expand their application scenarios. Additionally, as the demand for drones continues to grow in various fields such as agriculture, logistics, and public safety, Chinese drone manufacturers will have more opportunities to develop and expand their businesses.In conclusion, China's drones have strong competitiveness in the international market due to their technological advantages and price advantages. With continuous innovation and development, the future of China's drone industry is full of hope.。
《机电一体化技术》课程报告题目:机电一体化技术在四旋翼无人机中的应用院(系):机械与电子信息学院专业:机械设计制造及其自动化学生姓名:学号:指导老师:吴来杰目录摘要 (1)1引言……………………………………………………………………………2四旋翼无人机的组成…………………………………………………………2.1无人机系统的总体构成……………………………………………………2.2四旋翼无人机的遥控系统……………………………………………………2.3四旋翼无人机机载主控…………………………………………………2.4 四旋翼无人机机载传感器………………………………………………3 工作流程………………………………………………3.1控制信号转换过程……………………………………………………3.2控制程序执行流程……………………………………………………………4 硬件总体设计…………………………………………………………4.1 无人机在遥感航拍方面的应用……………………………………………5 结束语…………………………………………………………参考文献……………………………………………………………………………机电一体化技术在四旋翼无人机中的应用姓名(中国地质大学机械与电子信息学院机械设计制造及自动化武汉430074)【摘要】:无人驾驶飞机,简称“无人机”,是一种用电子设备控制的无人驾驶航空器。
与载人飞机相比,无人机具有体积小、方便灵活、成本低等特点。
无人机最早以“靶机”的身份出现在军事领域;随着技术的成熟,生产成本的降低,逐步进入民用领域。
四旋翼无人机是最常见的一种民用无人机,它的四个旋翼作为飞行的直接动力源,旋翼对称分布在机体的前后、左右四个方向,四个旋翼处于同一高度平面,且四个旋翼的结构和半径都相同,旋翼1和旋翼3逆时针旋转,旋翼2和旋翼4顺时针旋转,四个电机对称的安装在飞行器的支架端,支架中间空间安放飞行控制计算机和外部设备。
第41卷增刊2009年12月 南 京 航 空 航 天 大 学 学 报Journal of Nanjing U niversity of Aeronautics &Astronautics V ol.41N o.S D ec.2009FBBM 型无线电遥控指令编码器王少云 张昭纯 杨 兵(南京航空航天大学无人机研究院,南京,210016)摘要:介绍了一种用于一站多机无人机测控系统的编码器,叙述了该编码器的组成、工作原理及实现方法。
该编码器的硬件电路采用以89C52单片机为核心的单片机系统,辅以指令采集电路、状态显示电路和串口通信电路等,将地址码、飞机号、遥控指令、遥调指令等编成相应的帧格式,送到遥控发射机发射出去,从而实现同时对多架无人机的控制。
提出了快速直接计算法对三字节序列进行简单快捷的CRC 计算,并用PL /M -51语言编写了软件程序。
该编码器具有体积小、重量轻、性能优良、形式新颖、成本低廉和使用方便等特点。
关键词:无人机;遥控;编码器;校验码;循环冗余校验中图分类号:V 24;T N 92;T P36 文献标识码:A 文章编号:1005-2615(2009)增刊-0052-05 收稿日期:2009-06-05;修订日期:2009-08-10 作者简介:王少云,男,研究员,1962年2月生,E -mail :nhshao yun @nuaa .edu .cn 。
FBBM Encoder in Remote Control SystemW ang Shaoy un ,Zhang Zhaochun ,Yang B ing(R esear ch Inst itute o f U nmanned A ircr aft,Nanjing U niver sity o f A ero nautics &A st ro nautics,N anjing ,210016,China)Abstract :A n encoder applied to the telecontr ol and telemetr y system of unmanned aerial vehicle (UAV )of "o ne station contr ol several vehicles"is pr esented.T he co mposition,the w orking principle and the implem entation method o f the encoder are described.An 89C52sing le-chip microcontro ller is ado pted as the cor e sy stem of the hardw are circuit o f the enco der supplemented by the instruction acquisitio n,the status display and the serial co mmunicatio n cir cuits ,etc .Some param eters ,such as the address code ,the plane number,and remote co ntro l com mands,and remote regulating com mands are encoded into the appropriate frame format by the hardw are circuit of the encoder transm itted by the remo te contro l tr ans-mitter to achiev e the sim ultaneo us control of sever al U AVs .An quick and dir ect calculatio n method is pr opo sed to carry on the simple and quick CRC calculation of the three -byte sequence .T he softw are pro -gram is w ritten by the PL/M-51language.The encoder has the advantag es o f small size,light w eig ht,excellent perform ance,novel form ,and low cost.Key words :unm anned aerial v ehicle (U AV );remote contro l ;encoder ;checko ut code ;CRC 在无人机系统中,为了实现对无人机的控制,必须由地面操作者向无人机发送各种指令。
摘要无人机的任务分配是无人机研究的重点问题。
在实际飞行中风对无人机的控制方式产生重要影响,导致无人机的地速大小和方向发生改变,从而使无人机飞行相同航迹任务的飞行时间发生改变,因此,在考虑风的前提下研究以最小化无人机飞行时间为优化目标的无人机任务分配问题具有一定的实际意义。
首先,针对旋翼无人机在风场环境下的任务分配问题进行研究。
根据风速、无人机空速和地速之间的矢量关系,以无人机完成所有任务点访问并返回起点时的时间最小化为优化目标,构建了变速VRP模型(VS-VRP),并通过实验证明了风对旋翼无人机任务分配结果存在的影响。
然后,在上述问题的基础上考虑固定翼无人机存在动力学约束,将任意两个目标点间的欧式距离拓展为杜宾路径,进一步地对风影响下的固定翼无人机任务分配进行研究。
构建了杜宾路径变速VRP 模型(DP-VS-VRP)。
通过设计交叉算子和变异算子,采用遗传算法对该问题有风以及不同风场情形进行求解,实验结果表明,在考虑无人机动力学约束的情形下,风场存在对固定翼无人机任务分配同样存在影响,且该模型能够有效的给出无人机在固定风场环境下的任务分配方案。
关键词:风;无人机;杜宾路径;任务分配;航迹规划IIABSTRACTThe task assignment of UA V is a key issue for UA V research. Wind has a significant effect on the control of unmanned aerial vehicles (UA Vs), resulting in changes in their ground speed and direction, which has an important influence on the results of UA V task allocation. Therefore, the task assignment problem of UA V under the influence of wind, which minimizes the flight time of UA V as the optimization goal, has certain practical significance.First of all, the research on the task assignment of quad copter UA V in steady wind is studied. According to the vector relationship between wind speed, UA V airspeed, and UA V ground speed, a method is proposed to calculate the flight time of UA V between targets, the variable-speed VRP model (VS-VRP) is constructed based on minimizing the time when the UA V finish all mission and return to the start point. Experiments show that the results of UA V assignment are different under the presence and absence of wind. Then, on the basis of the above problems, considering the dynamic constraints of the fixed-wing UA V, the Euclidean distance between any two targets is expanded to Dubins path.Further research on task allocation under the influence of wind is carried out. The Dubins path variable speed VRP model (DP-VS-VRP) is constructed. By designing crossover and mutation operator, the genetic algorithm is used to solve the problem under windless and different wind conditions. The experimental results show that under the consideration of UA V constraint, the existence of wind has the same effect on the task allocation of UA Vs, and the model can solve the problem efficiently.Keywords:wind; unmanned aerial vehicles; Dubins path; task allocation; path planning目录第一章绪论 (1)1.1 研究背景 (1)1.2 研究意义 (2)1.3 研究现状与分析 (3)1.3.1 无人机任务分配问题 (3)1.3.2 风场对无人机飞行航迹的影响 (5)1.4 研究内容 (6)1.5 结构安排 (6)第二章基础理论知识 (8)2.1 风场模型 (8)2.2 无人机模型 (9)2.2.1 无人机定义 (9)2.2.2 无人机访问任务点规则 (10)2.3 遗传算法 (13)2.4 本章小结 (14)第三章风影响下的旋翼无人机任务分配问题建模与求解 (15)3.1 风影响下的旋翼无人机任务分配问题描述 (15)3.2 风影响下的旋翼无人机任务分配模型的构建 (17)3.3 风影响下的旋翼无人机任务分配算法设计 (18)3.3.1 染色体编码 (18)3.3.2 交叉算子 (18)3.3.3 变异算子 (19)3.3.4 种群更新 (21)3.4 实验与分析 (22)3.4.1 实验参数设置 (22)3.4.2 风对旋翼无人机任务分配结果的影响分析 (22)3.5 本章小结 (24)第四章风影响下的固定翼无人机任务分配问题建模与求解 (26)4.1 风影响下的固定翼无人机任务分配问题描述 (26)4.2 风影响下的固定翼无人机任务分配模型的构建 (28)4.3 风影响下的固定翼无人机任务分配模型求解算法 (28)4.3.1 染色体编码 (28)IV4.3.2 交叉算子 (29)4.3.3 变异算子 (30)4.3.4 迭代更新 (32)4.4 实验与分析 (33)4.4.1 风场对固定翼无人机任务分配的影响效果实验 (33)4.4.2 风影响下的多固定翼无人机最优任务分配实验 (35)4.4.3 算法参数敏感性分析 (38)4.6 本章小结 (42)第五章总结与展望 (43)5.1 研究总结 (43)5.2 研究展望 (44)参考文献 (45)攻读硕士学位期间学术活动及成果情况 (48)插图清单图 2.1 风向示意图 (8)图 2.2 速度矢量关系图 (9)图 2.3 点X(100,330)处地速矢量合成结果示意图 (10)图 2.4 LSL路径分解示意图 (11)图 2.5 杜宾路径 (12)图 2.6 无人机地速航向角示意图 (12)图 2.7 遗传算法流程图 (13)图 3.1 无风场景下旋翼无人机最优航迹图 (15)图 3.2 南风场景下旋翼无人机最优航迹图 (16)图 3.3 染色体A编码示意图 (18)图 3.4 染色体交叉算子 (19)图 3.5 染色体变异算子 (20)图 3.6 不同风场下旋翼无人机最优航迹图 (24)图 4.1 南风环境下旋翼无人机完成任务不返回的最优飞行路径 (26)图 4.2 南风环境下固定翼无人机完成任务不返回的最优飞行路径 (27)图 4.3 固定翼无人机染色体A编码示意图 (29)图 4.4 固定翼无人机两个染色体交叉过程示意图 (30)图 4.5 染色体固定翼无人机变异过程示意图 (32)图 4.6 无风环境下固定翼无人机访问3个目标点的最优航迹示意图 (33)图 4.7 四种风场下固定翼无人机U1访问3个目标点耗时最短的航迹示意图 (35)图 4.8 东风环境中不同风速下2架固定翼无人机访问3个目标点耗时最短的航迹示意图 (38)图 4.9 风速为5m/s的东风环境中,不同规模、交叉、变异概率下固定翼无人机最短航时 (39)图 4.10 不同种群规模下,交叉和变异概率对算法求解结果的影响 (41)图 4.11 两种种群规模下三种交叉和变异概率配置下算法求解过程 (42)VI插表清单表 3.1 第三章符号表示说明 (16)表 3.2 交叉算子伪代码 (19)表 3.3 任务点变异算子伪代码 (20)表 3.4 无人机变异算子伪代码 (20)表 3.5 基于遗传优化算法的伪代码 (21)表 3.6 旋翼无人机实验参数设置 (22)表 3.7 无风环境下旋翼无人机最优任务分配方案 (22)表 3.8 不同风场环境下旋翼无人机沿固定路径的飞行时间 (23)表 3.9 不同风场环境下四旋翼无人机最优任务分配方案 (23)表 4.1 第四章符号表示说明 (27)表 4.2 固定翼无人机交叉算子伪代码 (29)表 4.3 任务点变异算子伪代码 (30)表 4.4 地速航向角变异算子伪代码 (31)表 4.5 无人机变异算子伪代码 (31)表 4.6 基于遗传优化算法的伪代码 (32)表 4.7 不同风场环境下固定翼无人机沿固定路径的飞行时间 (34)表 4.8 不同风场环境下固定翼无人机按不同顺序访问目标点的飞行时间34 表 4.9 四种风场环境下固定翼无人机任务分配结果 (36)表 4.10 东风环境中不同风速情况下固定翼无人机的任务分配结果 (37)表 4.11 固定翼无人机实验参数设置 (39)第一章绪论第一章绪论随着计算机、飞行控制、自动化以及装备材料研发等技术的提升,现代飞行器技术也得到了飞速的发展,飞行器种类日渐丰富,应用领域日趋广泛。
分析无人机遥感技术存在的技术优势及不足-航天工程论文-工程论文——文章均为WORD文档,下载后可直接编辑使用亦可打印——航空毕业论文导师精推范文10篇之第十篇:分析无人机遥感技术存在的技术优势及不足摘要:测绘工程伴随科学技术的高速发展也获得了极大提升,特别是无人机遥感技术,近年来呈现出良好的发展态势,取得的成绩非常显着。
无人机遥感技术主要是通过安装通信定位技术的无人驾驶飞行器,来对空间信息开展相应的测量工作,获取精准的测量数据。
利用无人机遥感技术进行测量,不仅可以提升测量结果的精准性与有效性,同时还能降低资源消耗。
特别在地形测绘工作中,无人机航空测量技术发挥着无可替代的作用。
本文结合实践,对当前无人机遥感技术存在的技术优势进行分析,并探讨该项技术存在的不足,并就测绘工程测量中,无人机遥感技术的应用进行研究,希望为相关工作提供有效的参考作用,推动工程测量工作的持续发展。
关键词:无人机遥感技术; 测绘工程; 应用;Abstract:With the rapid development of science and technology, surveying and mapping engineering has also been greatly improved, especially UAV remote sensing technology, which has shown a good development trend in recent years, and achieved remarkable results. UAV remote sensing technology is mainly through the installation of communication and positioning technology of unmanned aerial vehicles, to carry out the corresponding measurement of space information, to obtain accurate measurement data. Using UAV Remote Sensing Technology for measurement can not only greatly improve the accuracy and effectiveness of measurement results, but also reduce resource consumption. Especially in topographic mapping, UAV aerial survey technology plays an irreplaceable role. Combining with practice, the paper analyzes the technical advantages and disadvantages of UAVremote sensing technology, and studies the application of UAV Remote Sensing Technology in surveying and mapping engineering, hoping to provide effective reference for related work and to promote sustainable development of engineering surveying.Keyword:unmanned aerial vehicle(UAV); geomatics engineering; application;随着经济社会的不断进步,社会建设步伐的逐渐加快,极大推动测绘工程测量工作的稳步发展,与此同时,对于测量结果的质量、测量工作的效率要求也越来越高。
A Survey of Quadrotor Unmanned Aerial VehiclesShweta GupteElectrical and Computer Engineering University of North Carolina atCharlotte sgupte2@Paul Infant Teenu MohandasElectrical and Computer Engineering University of North Carolina atCharlottepmohanda@James M. ConradElectrical and Computer Engineering University of North Carolina atCharlottejmconrad@Abstract — In the past decade Unmanned Aerial Vehicles (UAVs) have become a topic of interest in many research organizations. UAVs are finding applications in various areas ranging from military applications to traffic surveillance. This paper is a survey for a certain kind of UAV called quadrotor or quadcopter. Researchers are frequently choosing quadrotors for their research because a quadrotor can accurately and efficiently perform tasks that would be of high risk for a human pilot to perform. This paper encompasses the dynamic models of a quadrotor and the different model-dependent and model-independent control techniques and their comparison. Recently, focus has shifted to designing autonomous quadrotors. A summary of the various localization and navigation techniques has been given. Lastly, the paper investigates the potential applications of quadrotors and their role in multi-agent systems.Keywords - quadrotor, review, autonomous control, vision systems, navigation.I. I NTRODUCTIONThe quadrotor or quadcopter is a unique type of Unmanned Aerial Vehicle (UAV) which has Vertical Take Off and Landing (VTOL) ability. The quadrotor has an advantage of maneuverability due to its inherent dynamic nature. It is an under-actuated system with four inputs (roll, pitch, yaw and throttle) and six outputs. The parameters that determine the characteristics of a flying machine are the flying principle and propulsion mode [1], Figure 1 shows classification of different kind of aircrafts based on these parameters.Fig.1. Aircraft classification depending on flying principle and propulsionmodeWith developments in fields like sensor fabrication and automation, designing UAVs with different characteristics is becoming easier. UAVs were initially considered only formilitary applications, but as the cost to manufacture these flying robots decrease, users are finding civilian applications like traffic surveillance. A major advantage that a UAV has over manned aerial vehicles is that its flight time is restricted only by the fuel/battery life, whereas in manned vehicles the human component like fatigue has to be considered. They are also useful for missions and tasks which are beyond the limitations of human endurance.II. MECHANISM OF FLYINGThe major advantage of a quadrotor over a traditional helicopter is the fixed rotor propulsion mode. The roll, pitch and yaw of a quadrotor changes depending on the throttle in each rotor. The four rotors are aligned such that, the rotors on opposite ends rotate in the same direction and the other two in the opposite direction.In order for the quadrotor to move about the roll axis, the throttles of the other side rotors (right or left) are increased, while reducing the same side rotor throttles. For movement about pitch axis, the front or back rotors are increased or reduced in the same way as for roll. In case of movement about the yaw axis, the counter-clockwise rotating rotors throttles are increased for rotation of the vehicle in counter-clockwise direction and the same holds good for clockwiserotation as well.Fig.2. X-configurationMotorizedNon-motorized Non-motorized Motorized Heavier than airLighter than air AircraftBalloon Blimp GliderPlaneRotorcraftVTOL QuadrotorFig 3 +- ConfigurationThe vehicle has two different configuration in which it can be flown, the ‘X’ configuration (Fig. 2) and the ‘+’ configuration (Fig. 3). An X-configuration quadrotor is considered to be more stable compared to + configuration, which is a more acrobatic configuration. Figure 4 shows a typical quadrotor with the inertial measurement unit (IMU) sensors, the Electronic Speed Controllers (ESCs) and the microcontroller at the center and the four rotors at each end.The PING ultrasonic sensors were project specific.Fig. 4 Typical QuadrotorIII. CONTROL SYSTEMS AND DYNAMIC MODELSWith the increase in attention UAVs and quadrotors have received in the last decade, the algorithms laid out to control them have also increased substantially in number and complexity. Various control structures ranging from basic PID controllers [15, 27, 62] to more complex systems using backstepping or neural networks [16, 26, 46] have proved to be efficient. There has been considerable innovation in the sensors that are used to control the quadrotor as well. Modern MEMS technology makes it easier to add more sensors on a small quadrotor as the space and weight constraints can be stringent. Sensors including basic IMUs, GPS modules and cameras have all been used in quadrotors.The general method to design a control system is to calculate the dynamic model of the system. The dynamic model of a system is a mathematical equation that comprises of all the forces that can act on the system at a given time. Many researchers have also tried comparing different control techniques [12, 14, 16, 67] and in most cases a quadrotor proved to be a dynamic vehicle with major challenges because of its under-actuated nature. Algorithms like PID, backstepping and feedback linearization have mainly been applied and proved to work well with a quadrotor. A typical quadrotor will have parts like ESCs, four rotors, an IMU and a microcontroller running a control algorithm. Parts like vision based system and GPS are optional. As explained in Section I, the quadrotor is controlled using the values of its throttle, roll, pitch and yaw. The control algorithm reads the IMU sensor values and modifies these values in the opposite direction to stabilize the system. While most research done till date is on mini-quadrotors, i.e. quadrotors that are small in size typically between 1 to 2kg, there have been a few papers that have tried researching on large scale quadrotors of up to 4kg [56]. The major difficulty that larger quadrotors face is that their weight causes the dynamics of the machine to change completely.There have also been control methodologies which have found their motivation in nature [68]. Quadrotor control is based upon interactions of animals and insects in nature. The control algorithm is developed in a way that it can be used in a multi-agent environment, where each quadrotor can predict the location of the other. Control systems are also designed specifically for multi-agent systems [64, 68] where the quadrotors work along with each other to perform a specific task.Another major difficulty that is faced in control of a quadrotor is forces of nature. Factors like wind and terrain play a very important factor when flying in an open space. Control systems [53] for these conditions are also performed where wind parameters are estimated and the different degrees of freedom are control based on these parametersIV. SENSORSThis section describes the major sensors found on aquadrotor and the uses of each of them. The most basic and necessary sensor on any quadrotor is the IMU sensor [15, 16, 21, 26, 35, 72]. The IMU consists of an accelerometer and a gyroscope. Both these sensors are used in order to maintain the orientation of the quadrotor with respect to Earth. The accelerometer measures the orientation of the quadrotor with respect to earth.The reason why we need the gyroscope as well is because the accelerometer cannot sense rotation and only gives readingsassuming the platform is stationary. The gyroscope has the ability to measure the rate of rotation around an axis, for example, if the quadrotor moves in its pitch axis the gyroscope value for the pitch axis will be a non-zero value until it stops the movement in the pitch axis.Ultrasonic range sensors like the Ping sensor is another sensor that can be commonly found on a quadrotor; the major use of this sensor is for low level altitude control and obstacle avoidance. The sensor is generally used for quadrotors which need to hover at a certain height or need to fly in an indoor environment where it has to detect obstacles. The sensor need not necessarily be an ultrasonic sensor - it can also be an infrared or a Laser range finder which can measure distance [31, 35, 61].Even though the IMU sensors help in calculating orientation of the vehicle, as time goes on very slight errors in the measurement of the acceleration can compound and result in significant errors in position and velocity. Therefore, some additional sensors can be added for increased stability and autonomy. The next most common sensor is a barometer for measuring altitude, and a magnetometer for measuring direction. Sensors from a Nintendo Wii Motion Plus and Nunchuk can also be used.Another important sensor that can commonly be found on quadrotors is a GPS [35, 67]. The purpose of GPS is tracking and localization. In an outdoor environment it is important to realize the location of the quadrotor both for safety and because location gives other information, especially when the objective is to survey a certain region.Apart from the sensors mentioned above, a camera is another highly used sensor on a quadrotor [1]. Apart from providing video feedback a camera can also be used for image recognition and processing as well as obstacle avoidance. A large number of researchers use it for various different applications on a quadrotor.V.VISION SYSTEMSDue to miniaturization of quadrotors use of on board LIDARs becomes impractical. For accurate position estimation and mapping researching are turning to vision sensor systems.The vision systems implemented in UAVs cover areas such as object detection and object tracking, position estimation, navigation, obstacle detection, autonomous landing, and stable hovering among others. [8, 41, 49, 50, 58] Visual servoing, also known as Vision-Based Robot Control, is a technique which uses feedback information extracted from a vision sensor to control the motion of a robot. Visual servo algorithms have been extensively developed for aerial robots in the last decade. [11, 18, 19, 22, 23, 25] Visual servo techniques can be broadly classified into Image Based (IBVS) and Position Based (PBVS). PBVS is a model-based technique in which the pose of the object of interest is estimated with respect to the camera and then a command is issued to the robot controller, which in turn controls the robot. In this case the image features are extracted and are used to estimate 3D information. Hence it is servoing in 3D [7, 9, 54, 57, 58, 60, 72]. In IBVS the control law is based on the error between current and desired features on the image plane, and does not involve any estimate of the position of the target [11, 20, 33, 69].Various types of input systems have been used for controlling the quadrotor in specific ways. Two major types of such systems are those employing on-board camera systems and those employing off-board camera systems. Monocular systems (single camera) use a single downward facing onboard camera as in [13]. Two camera systems have been employed for obtaining more accurate tracking results [5, 6] - they use one onboard camera and one pan tilt camera on the ground for obtaining better altitude measurements. Ground based external camera systems are used in tracking the quadrotor within specific field of flight [4]. A novel method of relative navigation using moiré patterns is presented in [63]. More recent research is seen to be concentrated towards using stereo cameras and 3D tracking. A system using a real time trinocular vision system to obtain 3D data is described in [48]. This data is then validated using onboard sensors.For tracking the quadrotor from off board cameras most algorithms use blob detection. Four to five colored blobs are marked on the quadrotor and are used to calculate orientation and position [4, 5]. Other papers [22] have used direct feature detection from images of the quadrotor. One research group [44] uses the CAD model of the quadrotor for feature matching. Techniques like optical flow calculation are then applied to the extracted features for position estimation [3, 44].To improve the resolution and control performance the data from camera can be integrated with or validated against other onboard sensors like IMU (multi sensory control) [48, 71]VI.NAVIGATIONA.Localization,Mapping and PlanningFor an autonomous robot the ability to move about in its environment and reach its desired goal location using the best feasible path is called navigation. Since quadrotors are often chosen for their mobility and maneuverability it is essential that the quadrotor incorporates a good navigation system. If the user provides a map of the environment it is called map-based navigation. If the UAV is expected to navigate in an unknown environment, the quadrotor needs to build a map and then calculate its own location within the map. This is called localization. This process needs to be done constantly as the robot moves through the map. Hence it is known as simultaneous localization and mapping (SLAM) [13, 45].B.AlgorithmsDue to the limited on-board capability of quadrotors, the navigation algorithms are run on an off-board remote computer. These algorithms take date from the primary sensors and estimate position, calculate path and send control commands to the quadrotor [24, 34]. The main difference fornavigation algorithms for quadrotors is that all mapping and path planning have to be done in a 3D co-ordinate system (since altitude also must be taken into consideration).Quadrotors that use vision sensors as their primary sensors use VSLAM or Visual SLAM [10].The control algorithms are often implemented in integrated loop with highest level algorithm running in the innermost most. For safe testing a basic obstacle avoidance algorithmruns as the outer loop [3].C.TestbedsFor extensive testing of the various control algorithms that are being developed efforts are being taken to develop a robust testbed. The Stanford Testbed of Autonomous Rotorcraft for Multi Agent Control or STARMAC is one example of an outdoor testbed. It comprises a set of autonomous quadrotor helicopters that can follow prescribed waypoint trajectories [36, 66]. Real-time Indoor Autonomous Vehicle Test environment or RAVEN is another testbed designed to explore long duration autonomous air operation using multiple UAVs [39, 65].Some similar developments are studied in [40, 51, 67].VII.APPLICATIONSDue to some unique abilities of the quadrotor such as high maneuverability, small size, and easy control quadrotors are finding many applications. The most significant of them would have to be search-and-rescue and emergency response. Other major applications of quadrotors are in homeland security, military surveillance, and search and destroy. Miniaturization of quadrotors has enabled them to be used for border patrol and surveillance. Quadrotors also have potential applications in other areas like earth sciences where they can be used to study climate change, glacier dynamics, and volcanic activity or for atmospheric sampling. A detailedanalysis of possible application of quadrotors ahs been provided in [59].For applications like search and rescue quadrotors are used as multi-agent systems [27, 37, 47].VIII.CONCLUSIONIn this paper, the basics of a quadrotor UAV are reviewed and the various elements that concern the quadrotor UAV including different sensors, applications and their advantages are surveyed. It starts at the basic control structure and describes advanced applications that a quadrotor can be put to as well. The field of UAVs and specifically quadrotors has more areas to develop and improve. These areas have lead to major developments in automation and robotics.The improvement in other technologies has given further leads in improving the design and computing power that can be associated with a quadrotor. Technologies like IC fabrication, chemical materials and programming are not the only fields that affect UAVs, various other fields add up to the improvement and hence the research in this field is never ending.Further work on quadrotors coupled with fields like power systems, path planning and SLAM can result in a great number of applications in everyday life. Research in areas specific to the flight of a UAV is also important. For example, Figure 5 is a photograph taken during testing of a project that dealt with landing of a quadrotor on an oscillating surface [73]. Similarly research can also be done on other aspects like take-off and hover stabilization that would aid in finding new practical applications for quadrotors.Fig. 5 Landing on oscillating surfaceREFERENCES[1]Markus Achtelik, Abraham Bachrach, Ruijie He, Samuel Prentice andNicholas Roy, “Autonomous Navigation and Exploration of aQuadrotor Helicopter in GPS-denied Indoor Environments”,/2009SymposiumPapers/2009MIT.pdf[2]Pratik Agarwal, Tom Brady, “SLAM Strategy for an AutonomousQuadrotor”, University of Michigan[3]Spencer Ahrens, Daniel Levine, Gregory Andrews, and Jonathan P.How, “Vision-Based Guidance And Control Of A Hovering Vehicle InUnknown, GPS Denied Environments”, Proceedings Of IEEEInternational Conference On Robotics And Automation, pp.- 2643 -2648 , May2009[4] E. Altug, J Ostrowski, R Mahony, “Control Of A Quadrotor HelicopterUsing Visual Feedback”, vol.1, pp-72 – 77, Proceedings of IEEEInternational Conference on Intelligent Robots and Systems, May 2002 [5] E. Altug, James Ostrowski, Camillo Taylor, “Quadrotor Control UsingDual Camera Visual Feedback”, pp.- 4294 – 4299, vol.3, Proceedingsof IEEE International Conference on Robotics and Automation, Sep2003[6]Erdinc Altug and Camillo Taylor, “Vision-Based Pose Estimation AndControl Of A Model Helicopter”, Proceedings of the IEEEInternational Conference on Mechatronics, pp.- 316 - 321 , December2004[7] E. Altug, J. P. Ostrowski, and C. J. Taylor, “Control of a quadrotorhelicopter using dual camera visual feedback,” Int. J. Robot. Res., vol.24, no. 5, pp. 329–341, 2005.[8]Omead Amidi, Takeo Kanade, and K. Fujita, “A visual odometer forautonomous helicopter flight”, In Proceedings of the FifthInternational Conference on Intelligent Autonomous Systems (IAS-5),June 1998.[9]O. Amidi, T. Kanade, and K. Fujita, “A visual odometer forautonomous helicopter flight,” J. Robot. Auton. Syst., vol. 28, pp. 185–193, Aug.1999.[10]Jorge Artieda, Jose M. Sebastian, Pascual Campoy, Juan F. Correa,Ivan F. Mondragon, Carol Martinez, Miguel Olivares, “Visual 3-DSLAM from UAV”, Intelligent Robot Systems, January 2009[11]J.R. Azinheira, P.Rives, J.R.H. Carvalho, G.F. Silveira, E.C. de Paiva,and S.S. Bueno, "Visual servo control for the hovering of an outdoorrobotic airship", IEEE International Conference on Robotics andAutomation, vol 3, pp. 2787-2792, May 2002[12] A. Benallegue, A. Mokhtari and L. Fridman, “Feedback Linearizationand High Order Sliding Mode Observer For A Quadrotor UAV”, pp365-372, Alghero, Proceedings of IEEE International Workshop onVariable Structure Systems, June 2006.[13]Micheal Blosch, Stephen Weiss, Davide Scaramuzza And RolandSiegwart, “Vision Based MAV Navigation In Unknown AndUnstructured Environments”, Proceedings of IEEE InternationalConference on Robotics and Automation, pp.- 21 – 28,May 2010 [14]S. Bouabdallah, A. Noth, R. Siegwart, “PID VS LQ ControlTechniques Applied to an Indoor Micro Quadrotor”, pp.2451-2456,Sendal, Proceedings of IEEE International Conference on IntelligentRobots and Systems, October 2004.[15]S. Bouabdallah, P. Murrieri and R. Siegwart, “Design and Control ofan Indoor Micro Quadrotor”, pp 4393-4398, New Orleans, Proceedingsof IEEE International Conference on Robotics and Automation, April2004.[16]S. Bouabdallah and R. Siegwart, “Backstepping and Sliding-modeTechniques Applied to an Indoor Micro Quadrotor”, pp 2247-2252,Barcelona, Proceedings of IEEE International Conference on Roboticsand Automation, April 2005.[17]Y. Bouktir, M Haddad, T Chettibi, “Trajectory planning for a quadrotorhelicopter” 16th Mediterranean Conference on Control and Automation,pp 1258 - 1263, June 2008[18]O. Bourquardez and F. Chaumette, "Visual servoing of an airplane forauto-landing", Proceedings of IEEE/RSJ International Conference onIntelligent Robot Systems, pp. 1314-1319, October 2007[19]O. Bourquardez, R. Mahony, N.Guenard, F.Chaumette, T.Hamel andL.Eck, “Kinematic visual servo control of a quadrotor aerial vehicle”,http://www.irisa.fr/lagadic/publi/year/2007-fra.html, July 2007[20]Odile Bourquardez, Robert Mahony, Nicolas Guenard, FrancoisChaumette, Tarek Hamel, and Laurent Eck, “Image-based Visual ServoControl of the Translation Kinematics of a Quadrotor Aerial Vehicle”,IEEE Transactions on Robotics, Vol. 25, No. 3, June 2009[21]P. Castillo, A. Dzul and R. Lozano, “Real-Time Stabilization andTracking of a Four-Rotor Mini Rotorcraft”, Vol. 12, No. 4, pp 510-516,IEEE Transactions on Control Systems Technology, July 2004.[22]Zehra Ceren, Erdinc Altug “Vision-based servo control of a quadrotorair vehicle”, IEEE International Symposium on ComputationalIntelligence in Robotics and Automation (CIRA), pp.- 84 - 89 ,December 2009[23] F. Chaumette and S.Hutchinson, "Visual servo control, Part 1: Basicapproaches," IEEE Robotics and Automation Magazine, vol 13, no. 4,pp. 82-90, December 2006.[24]Joseph Conroy, Gregory Gremillion, Badri Ranganathan, J. SeanHumbert, “Implementation of wide-field integration of optic flow forautonomous quadrotor navigation”, August 2009[25]P.Corke and S.A. Hutchinson, "A new partitioned approach to imagebased visual servo control," IEEE Transactions on Robotics andAutomation, vol 17, no. 4, pp. 507-515, August 2001.[26]J. Dunfied, M. Tarbouchi and G. Labonte, “Neural Network BasedControl of a Four Rotor Helicopter”, pp 1543-1548, Proceedings ofIEEE International Conference on Industrial Technology, December2004.[27] B. Erginer and E. Altuğ, “Modeling and PD Control of a QuadrotorVTOL Vehicle”, pp 894-899, Istanbul, Proceedings of IEEE IntelligentVehicle Symposium, June 2007.[28]J Gancet, G Hattenberger, R Alami, S Lacroix, “Task planning andcontrol for a multi-UAV system: architecture and algorithms” France International Conference on Intelligent Robots and Systems,pp.- 1017 - 1022 Aug. 2005[29]Grant R. Gerhart, Douglas W. Gage, Charles M. Shoemaker, “On-board SLAM for indoor UAV using a laser range finder “, UnmannedSystems Technology XII, April 2010[30]S. Grzonka, G.Grisetti and W. Burgard, “Towards a Navigation Systemfor Autonomous Indoor Flying”, pp 2878-2883, Kobe, Proceedings ofIEEE International Conference on Robotics and Automation, May2009.[31]S Grzonka, G Grisetti, W Burgard, “Towards a navigation system forautonomous indoor flying”, IEEE International Conference onRobotics and Automation, pp.- 2878 – 2883, May 2009[32]S Grzonka, G Grisetti, W Burgard, “A Fully Autonomous IndoorQuadrotor ” IEEE Transactions on Robotics, Vol: PP, Issue:99, pp. - 1– 11, Aug 2011[33]T. Hamel and R. Mahony, “Visual servoing of an under actuateddynamic rigid-body system: An image based approach,” IEEE Trans.Robot. Autom., vol. 18, no. 2, pp. 187–198, Apr. 2002.[34]Ruijie He; Prentice, S.; Roy, N.; “Planning in information space for aquadrotor helicopter in a GPS-denied environment ” IEEEInternational Conference on Robotics and Automation, pp.- 1814 -1820 , May 2008[35]G. Hoffmann, D. G. Rajnarayan, S. L. Waslander, D. Dostal, J. S. Jangand C. J. Tomlin, “The Stanford Testbed of Autonomous Rotorcraft forMulti-Agent Control (STARMAC)”, Vol. 2, pp12.E.4 – 1-10,Proceedings of IEEE Digital Avonics Systems Conference, October2004.[36]G. Hoffmann, D.G. Rajnarayan, S.L. Waslander, D. Dostal, J.S. Jang,C.J. Tomlin, “The Stanford Testbed Of Autonomous Rotorcraft ForMulti Agent Control (STARMAC)”, Digital Avionics SystemsConference, 2004 12.E.4 - 121-10 Vol.2, February 2005[37]G.M. Hoffmann, S.L.Waslander, C.J. Tomlin, “Mutual InformationMethods with Particle Filters for Mobile Sensor Network Control”,45th IEEE Conference on Decision and Control, pp.- 1019 – 1024,Dec. 2006[38]Gabriel M. Hoffmann and Steven L. Waslander, “DistributedCooperative Search using Information-Theoretic Costs for ParticleFilters with Quadrotor Applications”, AIAA Guidance, Navigation, andControl Conference and Exhibit, August 2006[39]J. How, B. Bethke, A. Frank, D. Dale, and J. Vian, “Real-time IndoorAutonomous Vehicle Test environment” 2009 IEEE InternationalConference on Robotics and Automation AIAA Guidance, Navigation,and Control Conference and Exhibit, August 2006.[40]J. How, B. Bethke, A Frank, D Dale, J Vian, “Real-Time IndoorAutonomous Vehicle Test Environment”, Control Systems, IEEE, pp.-51 – 64, April 2008[41]S. Hrabar, G.S. Sukhatme, P. Corke, K. Usher, and J. Roberts,“Combined optic-flow and stereo-based navigation of urban canyonsfor a UAV”, IEEE/ RSJ International Conference on Intelligent Robotsand Systems, pp.- 3309–3316, Aug. 2005.[42] F. Kendoul, K. Nonami, “A visual navigation system for autonomousflight of micro air vehicles”, pp- 3888 – 3893, Proceedings ofInternational Conference on Intelligent Robots and Systems, Oct 2009 [43]Laszlo Kisa, Zoltan Prohaszka, Gergely Regula, “Calibration AndTesting Issues Of The Vision, Inertial Measurement And ControlSystem Of An Autonomous Indoor Quadrotor Helicopter”,International Workshop on Robotics in Alpe-Adria-Danube Region,Italy, 2008[44]Sebastian Klose, Jian Wang, Micheal Achtelik, Giorgio Panin, FlotianHolzapfel, Alois Knoll, “Marker less, Vision-Assisted Flight ControlOf A Quadrotor”, Proceedings of IEEE International Conference onRobotics and Automation, October 2010[45]Gim Hee Lee, Friedrich Fraundorfer, and Marc Pollefeys, “RE-SLAM:RANSAC Sampling For Visual FastSLAM”, proceedings of IEEEInternational Conference On Intelligent Robots And Systems, pp.- 1655- 1660, September 2011[46]T. Madani and A. Benallegue, “Backstepping Control for a QuadrotorHelicopter”, pp 3255-3260, Proceedings of IEEE InternationalConference on Intelligent Robotics and Systems, Beijing 2006.[47] D.H.A. Maithripala, and S Jayasuriya, “Feasibility considerations information control: Phantom track generation through multi-UAVcollaboration", 47th IEEE Conference on Decision and Control, pp.-3959 – 3964, Dec. 2008[48]Carol Martinez, Pascual Campoy, Ivan Mondragon, and Miguel A.Olivares-Mendez, “Trinocular Ground System To Control UAVs”,proceedings of IEEE conference on Intelligent Robots and Systems,pp.- 3361 – 3367, October 2009[49]T.G. McGee, R. Sengupta, and K. Hedrick, “Obstacle detection forsmall autonomous aircraft using sky segmentation”, Proceedings of the2005 IEEE International Conference on Robotics and Automation, pp.-4679–4684, April 2005.[50]Luis Mejias, Srikanth Saripalli, Gauvav Sukhatme, and PascualCampoy, “Detection and tracking of external features in a urbanenvironment using an autonomous helicopter”, Proceedings of IEEEInternational Conference on Robotics and Automation, pages 3983–3988, May 2005.[51]N. Michael, J. Fink, V. Kumar, “Experimental Testbed for LargeMultirobot Teams” IEEE Robotics & Automation Magazine, pp.- 53 –61, March 2008[52]Armen A. Mkrtchyan, Richard R. Schlitz, and WIllaim H. Semke,“Vision-based Autopilot Implementation using Quadrotor Helicopter”,AIAA Aerospace Conference, April 2009[53] A. Mokhtari and A. Benallegue, “Dynamic feedback Controller ofEuler Angles and Wind parameters estimation for a QuadrotorUnmanned Aerial Vehicle”, pp 2359-2366, Proceedings of IEEEInternational Conference on Robotics and Automation, April 2004. [54]K. Nordberg, P. Doherty, G. Farneback, P.-E. Forssen, G. Granlund, A.Moe, and J. Wiklund, “Vision for a UAV helicopter,” presented at theIEEE/RSJ Int. Conf. Intell. Robots Syst., Workshop Aerial Robotics,Oct. 2002[55]Tin Thet Nwe, Than Htike, Khine Myint Mon, Dr.Zaw Min Naing andDr.Yin Mon Myint, “Application of an Inertial Navigation System tothe Quad-rotor UAV using MEMS Sensors”, World Academy ofScience, Engineering and Technology 42 2008.[56]P. Ponds and R. Mahony, “Design Principles of Large Quadrotors forPractical Applications”, pp 3265-3270, Kobe, Proceedings of IEEEInternational Conference on Robotics and Automation, May 2009. [57]H. Romero, R. Benosman, and R. Lozano, “Stabilization and locationof a four rotor helicopter applying vision,” in Proc. Amer. ControlConf., pp. 3930–3936, June 2006[58]S. Saripalli, J. F. Montgomery, and G. S. Sukhatme, “Visually-guidedlanding of an unmanned aerial vehicle,” IEEE Trans. Robot. Autom.,vol. 19, no. 3, pp. 371–381, Jun. 2003.[59]Zak Sarris, “Survey of UAV Applications In Civil Markets”, STNATLAS-3Sigma AE and Technical University of Crete, June 2001. [60]O. Shakernia, Y. Ma, T. Koo, and S. Sastry, “Landing an unmanned airvehicle: Vision based motion estimation and nonlinear control,” AsianJ.Control, vol. 1, no. 3, pp. 128–145, Sep. 1999.[61] D.H. Shim and S. Sastry, “An Evasive Maneuvering Algorithm forUAVs in See-and-Avoid Situations”, pp 3886-3891, New York City,Proceedings of IEEE American Control Conference, July 2007 [62] A.Tayebi and S. McGilvary, “Attitude Stabilization of a VTOLQuadrotor Aircraft”, Vol. 14, No.3 pp 562-571, Proceedings of IEEETransactions on Control Systems Technology, May 2006.[63]Glenn P. Tournier, Mario Valenti, Jonathan P. How and Eric Feron,“Estimation and Control of a Quadrotor Vehicle Using MonocularVision and Moire Patterns”, AIAA Guidance, Navigation and ControlConference and Exhibition, August 2006[64] A. Tsourdos, S. Jeyaraman, M. Shanmugavel, B.A. White and R.Żbikowski, “A Formal Model Approach for the Analysis andValidation of the Cooperative Path Planning of a UAV Team”, pp 69-73, Proceedings of IEEE seminar on Autonomous Agents in Control,May 2005[65]M. Valenti, B. Bethke, G. Fiore, and J. P. How, “Indoor Multi-VehicleAutonomous Vehicle Test Environment”, IEEE Control SystemsMagazine, April 2008.[66]M. Valenti, B. Bethke, D. Dale, A. Frank, J. McGrew, S. Ahrens, J.P.How, J. Vian, “The MIT Indoor Multi-Vehicle Flight Testbed”, IEEEInternational Conference on Robotics and Automation, pp.- 2758 –2759, May 2007[67]S. L. Waslander, G. M. Hoffmann, J. S. Jang and C. J. Tomlin,” Multi-Agent Quadrotor Testbed Control Design: Integral Sliding Mode vs.Reinforcement Learning”, pp 3712-3717, IEEE Proceedings ofInternational Conference on Intelligent Robotics and Systems, August2005.[68]Zhibin Xue; Jianchao Zeng, "Formation Control NumericalSimulations of Geometric Patterns for Unmanned AutonomousVehicles with Swarm Dynamical Methodologies”, ICMTMA '09.International Conference on Measuring Technology and MechatronicsAutomation, 2009, vol.1, no., pp.477-482, 11-12 April 2009[69]H. Zhang and J. P. Ostrowski, “Visual servoing with dynamics: Controlof an unmanned blimp,” in Proc. IEEE Int. Conf. Robot. Autom., ICRA,vol. 1999, pp. 618–623, May 1999[70]T. Zhang, W. Li, M. Achtelik, K. Kühnlenz and M. Buss, “Multi-Sensory Motion Estimation and Control of a Mini-Quadrotor in an Air-Ground Multi-Robot System”, pp 45-50, Guilin, Proceedings of IEEEInternational Conference on Robotics and Biomimetics, December2009.[71]Tianguang Zhang, Ye Kang, Markus Achtelik, Kolja Kuhnlenz AndMartin Buss, “Autonomous Hovering Of A Vision/Imu GuidedQuadrotor”, Proceedings of IEEE International Conference onMechatronics and Automation, pp.- 2870 - 2875, August 2009[72] A. D. Wu, E. N. Johnson, and A. A. Proctor, “Vision-aided inertialnavigation for flight control,” presented at the AIAA Guid., Navigat.,Control Conf. Exhib., San Francisco, CA, Aug. 2005.[73]P. Mohan Das, S. Swami and J M. Conrad, “Landing of a QuadrotorUAV on an Oscillating Surface”, University of North Carolina atCharlotte, Jan 2012.。
软件工程 SOFTWARE ENGINEERING 第24卷第6期2021年6月V ol.24 No.6Jun. 2021文章编号:2096-1472(2021)-06-57-05DOI:10.19644/ki.issn2096-1472.2021.06.014基于半实物仿真平台的通用无人机系统模拟器设计倪怡涛1,李俊杰2,李晓明1(1.浙江理工大学机械与自动控制学院,浙江 杭州 310018;2.北京航天测控技术有限公司,北京 100043)****************;********************;****************.cn摘 要:无人机本身是一个复杂的机电系统,是机械、电子、通讯、控制、信息等技术的高度融合体,如何模拟无人机单元是这些系统开发的一个重要课题。
本文提出了基于通用半实物仿真平台的无人机模拟方案,与传统的数学模型或数据驱动模型方案相比,该模拟方案更接近真实系统,更容易模拟各种故障,同时也更容易与真实系统进行替换,方便系统整体的开发、测试与运行。
提出的模拟器方案基于单元模拟,通过对无人机系统内各个单元的数据交互和通讯协议进行抽象,以真实的接口实现单元之间的数据交互,将无人机飞行模型仿真单元嵌入系统中,实现最大程度接近真机的模拟。
测试表明,该模拟方案不但可以用于总体系统的开发和调试,而且对无人机自身的研制、测试等起到了关键作用。
关键词:无人机系统;组件;仿真;通讯中图分类号:TP311.5 文献标识码:ADesign of Universal UA V System Simulator based onSemi-physical Simulation PlatformNI Yitao 1, LI Junjie 2, LI Xiaoming 1(1.Faculty of Mechanical Engineering and Automation , Zhejiang Sci -Tech University , Hangzhou 310018, China ;2.Beijing Aerospace T&C Technology Co . LTD ., Beijing 100043, China )****************;********************;****************.cnAbstract: Unmanned Aerial Vehicle (UA V), a complex electromechanical system, is a high integration of machinery, electronics, communication, control, information and other technologies. How to simulate the UA V unit is an important topic in developing these systems. This paper proposes a UA V simulation solution based on semi-physical simulation platform. Compared with traditional mathematical model or data-driven model program, the simulation solution is closer to the real system, easier to simulate various faults, and easier to match the real system. The system facilitates the overall development, testing and operation of the system. The unit-based simulator solution proposed realizes data interaction between units by abstracting data interaction and communication protocol of each unit in the UA V system with a real interface. UA V flight model simulation unit is embedded in the system to realize the simulation as close as possible to the real machine. Tests show that the simulation solution proposed can not only be used for development and debugging of the overall system, but also play a key role in the development and testing of the UA V itself.Keywords: UA V system; component; simulation; communication1 引言(Introduction)无人机已广泛应用于航空、航天、测控和勘探等领域,其开发测试技术和流程越来越精细和复杂,无人机系统的仿真模拟技术应运而生。
史上最牛无人机作文英语Title: The Most Impressive Drone in History。
In the realm of unmanned aerial vehicles (UAVs), there emerges a pinnacle of innovation and engineering that has left an indelible mark on history—the MQ-9 Reaper. Fromits inception, this formidable drone has redefined the capabilities of unmanned aircraft, showcasing unprecedented endurance, versatility, and lethality.First and foremost, the MQ-9 Reaper stands out for its exceptional endurance, a quality that sets it apart fromits predecessors and contemporaries alike. Equipped with a powerful turboprop engine, this drone boasts an impressive endurance of over 27 hours, enabling it to conduct extended surveillance and strike missions with unparalleled persistence. This remarkable feat of engineering not only enhances its operational effectiveness but also underscores its significance in modern warfare and intelligence gathering.Moreover, the MQ-9 Reaper embodies unparalleled versatility, capable of executing a wide range of missions with precision and efficiency. Whether tasked with reconnaissance, surveillance, target acquisition, or strike operations, this drone excels across various operational environments, from combat zones to border security and counterterrorism efforts. Its modular design allows for the integration of advanced sensors, communication systems, and weapons payloads, ensuring adaptability to evolving mission requirements and operational scenarios.Furthermore, what truly distinguishes the MQ-9 Reaper is its lethal firepower, making it a formidable asset on the battlefield. Armed with a diverse array of munitions, including precision-guided missiles and bombs, this drone delivers pinpoint accuracy and devastating effects against enemy targets, effectively neutralizing threats while minimizing collateral damage. Its ability to loiter over hostile territory for extended durations, coupled with its rapid response capabilities, amplifies its effectiveness in engaging time-sensitive targets and providing close airsupport to ground forces.Beyond its military applications, the MQ-9 Reaper has also demonstrated its utility in various civilian and humanitarian endeavors. From disaster response and search-and-rescue operations to environmental monitoring and infrastructure inspection, this drone offers a cost-effective and non-intrusive solution for addressing a myriad of societal challenges. Its aerial surveillance capabilities, coupled with advanced sensor technologies, enable timely and accurate data collection, facilitating informed decision-making and resource allocation in times of crisis.In conclusion, the MQ-9 Reaper stands as a testament to the ingenuity and innovation of mankind, representing the epitome of unmanned aerial technology. With its unmatched endurance, versatility, and lethality, this drone has left an indelible mark on history, reshaping the landscape of modern warfare and revolutionizing the way we perceive aerial operations. As we continue to harness the potential of unmanned systems, the legacy of the MQ-9 Reaper willendure as a symbol of progress and advancement in the field of aviation and beyond.。
中国航空史英文作文The History of Aviation in ChinaChina has a rich and storied history when it comes to aviation. From the early pioneers who experimented with kites and gliders to the modern-day technological advancements in the field, China has played a significant role in the development of air travel and aerospace engineering. In this essay, we will explore the key milestones and achievements that have shaped the history of aviation in China.One of the earliest recorded instances of aviation-related activities in China dates back to the 6th century BCE, when the Chinese philosopher Mozi experimented with kites and gliders. He is believed to have been the first person to successfully fly a kite and to have developed the principles of aerodynamics. This early work laid the foundation for future advancements in the field of aviation.In the late 19th century, the Chinese government began to recognize the importance of aviation technology. In 1909, the first Chinese-built aircraft, the Feng Ru, took to the skies. This was a significant achievement, as it demonstrated China's growing capabilities in thefield of aviation. The Feng Ru was designed and built by Chinese engineers and was a testament to the country's burgeoning technological prowess.As the 20th century progressed, China continued to make strides in the field of aviation. In the 1930s, the Chinese government established the first national airline, the China National Aviation Corporation (CNAC). This airline played a crucial role in connecting China's major cities and facilitating the movement of people and goods throughout the country.During the Second World War, China's aviation industry was put to the test. The country's air force played a vital role in the war effort, engaging in numerous battles and air raids against the Japanese forces. Despite the challenges posed by the war, China's aviation industry continued to grow and develop, with the country producing its own fighter planes and bombers.In the post-war era, China's aviation industry underwent a period of rapid expansion and modernization. The country's first domestically-produced commercial airliner, the Y-10, took to the skies in 1980. This was a significant milestone, as it demonstrated China's ability to design and manufacture its own aircraft.In the decades that followed, China's aviation industry continued togrow and evolve. The country has become a major player in the global aerospace market, producing a wide range of aircraft, from commercial airliners to military jets. China's aviation industry has also played a key role in the country's space program, with the development of advanced rocket and satellite technology.Today, China's aviation industry is at the forefront of technological innovation. The country is investing heavily in the development of new aircraft designs, including the C919 commercial airliner and the J-20 stealth fighter jet. China is also making significant strides in the field of unmanned aerial vehicles (UAVs), with the country becoming a global leader in the production and deployment of these advanced technologies.In conclusion, the history of aviation in China is a testament to the country's technological prowess and its commitment to innovation. From the early pioneers who experimented with kites and gliders to the modern-day aerospace engineers who are pushing the boundaries of what is possible, China has played a crucial role in the development of air travel and aerospace technology. As the country continues to invest in and develop its aviation industry, it is poised to play an even more significant role in shaping the future of global aviation.。
无人机的好处和潜在的风险英语作文英文回答:Drones, also known as unmanned aerial vehicles (UAVs), have become increasingly prevalent in recent years, offering a wide range of benefits in various domains. However, their proliferation also raises potential risks that need to be carefully considered.Benefits of Drones:Enhanced Aerial Surveillance: Drones provide a unique perspective from the sky, enabling real-time monitoring of remote areas, disaster zones, and critical infrastructure. This aerial surveillance aids in situational awareness, decision-making, and response efforts.Improved Remote Sensing: Equipped with advanced sensors, drones can collect high-resolution imagery and data, including aerial photography, thermal imaging, andmultispectral data. This information is valuable for mapping, environmental monitoring, search and rescue operations, and precision agriculture.Efficient Delivery and Transportation: Drones have the potential to revolutionize logistics by enabling rapid and cost-effective delivery of goods, particularly in remote or inaccessible areas. They can also transport medical supplies, humanitarian aid, and other essential items to disaster zones.Enhanced Safety and Security: Drones can be used for surveillance and monitoring in public spaces, border patrol, and other security applications. They provide a safer alternative to manned aircraft, reducing risks to humanlife and enabling more effective situational awareness.Scientific Research and Exploration: Drones have become invaluable tools for scientific research, allowing scientists to collect data in previously inaccessible or dangerous environments. They facilitate aerial surveys, wildlife tracking, and environmental monitoring,contributing to advancements in various disciplines.Potential Risks of Drones:Privacy Concerns: Drones equipped with cameras and sensors raise concerns about privacy violations. Their ability to capture aerial imagery and monitor individuals without their knowledge or consent can compromise personal privacy.Security Threats: Malicious use of drones poses security risks. They can be used for surveillance, illegal activities, or even as weapons, potentially causing harm or disruption.Air Traffic Safety: As the number of drones in the airspace increases, there is a potential risk of collisions with manned aircraft or other drones. This can lead to accidents and safety hazards, requiring effective airtraffic management systems to mitigate these risks.Noise Pollution: Drones generate noise duringoperation, which can be a nuisance in residential areas or near sensitive locations. Excessive noise levels can cause discomfort and disruption in urban environments.Regulatory Challenges: The rapid adoption of droneshas outpaced regulatory frameworks in many jurisdictions. This creates challenges in ensuring responsible and safe drone operations, preventing misuse, and protecting public interests.中文回答:无人机的优点:加强空中监视,无人机可以从空中提供独特的视角,实现对偏远地区、灾区和关键基础设施的实时监测。
无人机感受英文作文Title: The Revolutionary Impact of Unmanned Aerial Vehicles (UAVs)。
Unmanned Aerial Vehicles (UAVs), colloquially known as drones, have surged into prominence in recent years, marking a paradigm shift in various fields. Their applications span from military operations to civilian endeavors, revolutionizing industries and societal practices. In this essay, we delve into the multifaceted impact of UAVs, exploring their transformative influence across different domains.Firstly, UAVs have revolutionized military operations worldwide. With their ability to conduct reconnaissance, surveillance, and targeted strikes without risking human lives, drones have become indispensable assets for modern warfare. They provide real-time intelligence, enhancing situational awareness and operational effectiveness on the battlefield. Moreover, UAVs equipped with precision-guidedmunitions can accurately eliminate enemy targets while minimizing collateral damage, thus reducing the human and economic costs of armed conflicts.Beyond the military realm, UAVs have permeated various civilian sectors, reshaping industries and driving innovation. In agriculture, drones equipped with advanced imaging sensors enable precision farming techniques, facilitating crop monitoring, pest detection, and yield optimization. By gathering aerial data with unprecedented detail and efficiency, UAVs empower farmers to make informed decisions, leading to increased productivity and sustainability in food production.In the realm of infrastructure and construction, UAVs play a pivotal role in surveying and mapping terrain, inspecting infrastructure assets, and monitoring construction progress. Their ability to capture high-resolution aerial imagery expedites surveying processes and enhances the accuracy of topographic mapping. Additionally, drones equipped with LiDAR (Light Detection and Ranging) technology can generate detailed 3D models of structuresand landscapes, facilitating infrastructure planning and maintenance.The entertainment industry has also embraced UAV technology, leveraging drones for aerial cinematography and live event coverage. Equipped with high-definition cameras and stabilizing gimbal systems, drones capture breathtaking aerial footage that was previously inaccessible or prohibitively expensive to obtain. From capturing panoramic vistas in films to providing dynamic perspectives in sports broadcasts, UAVs enhance storytelling and audience engagement across various media platforms.Furthermore, UAVs have emerged as invaluable tools in disaster response and environmental conservation efforts.In emergency situations such as natural disasters or humanitarian crises, drones equipped with thermal imaging cameras and gas sensors aid in search and rescue operations, identifying survivors and assessing damage in hazardous environments. Moreover, UAVs enable environmentalscientists to monitor ecosystems, track wildlife populations, and combat poaching activities through aerialsurveillance and data analysis.Despite their myriad benefits, UAVs also raise ethical, legal, and privacy concerns that warrant careful consideration. Issues such as unauthorized surveillance, airspace regulation, and the potential for misuse in criminal activities underscore the need for robust regulatory frameworks and ethical guidelines governing UAV operations. Balancing innovation with accountability is essential to harnessing the full potential of UAV technology while mitigating its risks and safeguarding individual rights and societal values.In conclusion, Unmanned Aerial Vehicles represent a transformative force with profound implications formilitary operations, civilian industries, and societal practices. From enhancing military capabilities to revolutionizing agriculture, infrastructure, entertainment, and disaster response, UAVs have reshaped the landscape of human activities. As we navigate the opportunities and challenges posed by UAV technology, responsible stewardshipand ethical considerations must guide our use of drones to ensure a future where innovation serves the collective good.。
A Survey of Unmanned Aerial Vehicles (UAV) for Traffic SurveillanceAnuj PuriDepartment of Computer Science and EngineeringUniversity of South Florida4202 E Fowler Ave, Tampa, FL 33620AbstractThe United States Department of Transportation (DOT) has been interested for the past several years in obtaining data on traffic trends and to monitor and control traffic in real-time. Currently, there are several methods by which the DOT regulates and monitors road transport. Cameras mounted on towers, detectors embedded in pavements or pneumatic tubes, and unmanned aircraft have been proven to be expensive and time-consuming solution candidates. However, aerial monitoring has the potential to yield detailed information to help traffic planners, as well as commuters. Unmanned Aerial Vehicles (UAVs) may provide a “bird’s eye view” for traffic surveillance, road conditions and emergency response. The purpose of this technical report is to provide a survey of research related to the application of UAVs for traffic management.1. IntroductionThe increase in the number of vehicles on roadway networks has led transport management agencies to allow use of technology advances resulting in better decisions. The mission of roadway transportation agencies is to evolve from solely providing roadway infrastructure to focusing on the needs of the traveling public, management and operations, and improved performance of the surface transportation system. This requires collection of precise and accurate information about the state of the traffic and road conditions. It is also required to get timely information in case of emergencies (accidents, oil leaks, etc). In case of accidents, time of response is the most critical constraint in victim survivability.Traditional technology for traffic sensing, including inductive loop detectors and video cameras, are positioned at fixed locations in the transportation network. Data related to traffic flow is currently obtained from detectors embedded in pavements or pneumatic tubes stretched across roads. Such methods do not prove to be time-efficient or cost-effective. While these detectors do provide useful information and data about traffic flows at particular points, they generally do not provide useful data for traffic flows over space. It is not possible to move detectors; further, they cannot provide useful information such as vehicle trajectories, routing information, and paths through the network.Several on-going research projects have been working to come up with technologies that improve surveillance techniques for traffic management. Travel time estimation algorithms such as Extrapolation method and Platoon matching, have been developed based upon measurable point parameters such as volume, lane occupancy, or vehicle headways. Image matching algorithms are used to match vehicle images or signatures captured at two consecutive observation points.Aerial view provides better perspective with the ability to cover a large area and focus resources on the current problems. It has the advantage of being both mobile, and able to be present in both time and space. Satellites were initially considered for traffic surveillance purposes, but the transitory nature of satellite orbits makes it difficult to obtain the right imagery to address continuous problems such as traffic tracking [24]. Also, cloud cover doesn’t give good image quality on days with bad weather. Some private companies have been flying manned aircrafts for commercial usage and survey. But this approach does not prove to be cost-effective. Also, the manned aircraft can not be flown in bad weather, or regions which are potentially unsafe for the operators.UAVs may be employed for a wide range of transportation operations and planning applications: incident response, monitor freeway conditions, coordination among a network of traffic signals, traveler information, emergency vehicle guidance, track vehicle movements in an intersection, measurement of typical roadway usage, monitor parking lot utilization, estimate Origin-Destination (OD) flows [5]. The advantage of UAVs is that they can move at higher speeds than ground vehicles as they are not restricted to traveling on the road network. Unmanned vehicles have advantages over manned vehicles as most of the functions and operations can be implemented at a much lower cost, faster and safer. UAVs may potentially fly in conditions that are too dangerous for a manned aircraft, such as evacuation conditions, or very bad weather conditions. UAVs are programmed off-line and controlled in real-time to navigate and to collect transportation surveillance data. UAVs can view a whole set of network of roads at a time and inform the base station of emergency or accidental sites. It also permits timely view of disaster area to access severity of damage. The base station can then choose the best route and inform the police cars.UAVs are equipped with a variety of multiple and interchangeable imaging devices including day and night real-time video cameras to capture real-time video; sensors such as digital video, infrared cameras, multi-spectral and hyper-spectral sensors, thermal, synthetic aperture radar, moving target indicator radar, laser scanners, chemical, biological and radiological sensors, and road weather information systems (RWIS) to record necessary information, such as weather, fire and flood information; and communications hardware to relay data to the ground station [2], [5]. With advances in digital sensing platforms, image processing, and computational speed, there are significant opportunities to automate traffic data collection. Different UAVs have different data collection capabilities. Some of them have real-time data transfer capabilities to the ground station, while the others are capable of storing high quality video or images on-board.2. UAVs OverviewUAVs are semi-autonomous or fully autonomous aircrafts that can carry cameras, sensors, communication equipment or other payloads. UAVs have been a topic of research for military applications since 1950s. UAVs were used as prototypes in World War I and II. In the last decade, Defense Advanced Research Projects Agency (DARPA) initiated several projects to increase use of UAVs in military applications [1]. Lately, increasing interest has been found in diverse civilian, federal and commercial applications, such as traffic monitoring.UAVs are classified as either rotary-wing or fixed-wing. Fixed-wing vehicles are simple to control, have better endurance, and are well suited for wide-area surveillance and tracking applications. Fixed wing vehicles have another advantage that they can sense image at long distances. One disadvantage though is that it takes sufficient time to react as turning a fixed-wing vehicle takes time and space until the vehicle regains its course. The rotary-wing vehicles are also known as Vertical Takeoff and Landing (VTOL) vehicles. They have the advantage of minimum launching time, as well as they don’t need enough space for landing. They have high maneuverability and hovering. Rotary wing vehicles have short range radars and cameras to detect traffic movement. The drawback of such type of vehicles is that the rotary motion leads to vibration.Vehicle Endurance(hours)Payload Weight(kg)Altitude Capacity(ft)Aerosonde 40 1 20,000Altus2 24 150 65,000AV BlackWidow 5 0 1,000 AV Dragoneye 1 0.5 3,000AV Pointer 1.5 0.9 3,000AV Puma 4 0.9 3,000AV Raven 1.25 0.2 3,000BQM-34 1.25 214 60,000Chiron 8 318 19,000Darkstar 8 455 45,000Exdrone 2.5 11 10,000 Global Hawk 42 891 65,000Gnat 750 48 64 25,000Helios 17+ 97,000MLB Bat 6 1.8 9,000MLB Volcano 10 9 9,000Pathfinder 16 40 70,000Pioneer 5.5 34 12,000RMAX 1 28 500Predator 29 318 40,000+ Shadow 200 4 23 15,000Shadow 600 14 45 17,000Table 1: Capabilities and characteristics of UAV systems presented and discussed duringthe UAV 2003 workshop [28].UAVs have different payload weight carrying capability, their accommodation (volume, environment), their mission profile (altitude, range, duration), and their command, control and data acquisition capabilities vary significantly. A summary of the UAV capabilities and characteristics were presented in [28] as shown in the Table 1.The smallest vehicles are Micro UAVs (MAVs) like the AV Black Widow developed for military surveillance, law enforcement, and civilian rescue efforts. Their payloads are just a few grams with vehicle size of a few centimeters. Larger than MAVs are Small UAVs (SUAVs) like the MLB Bat. SUAVs are largely used for traffic surveillance oriented research as they are designed for small regional scales and carry a payload of a few kilograms. They are portable, flexible and autonomous in their applications. Medium altitude and medium endurance UAVs (MUAVs) are used for regional scale observations. They can be used for applications such as mapping and monitoring of fire hazards, weather phenomena etc. UAVs that operate in High Altitude with Long Endurance (HALE) range, like the Helios, are used for applications such as mapping, communication, and monitoring tasks of the earth surface and the atmosphere, as they can work at altitudes up to 100,000 feet.3. Barriers to UAV DeploymentUAVs fall under the direct jurisdiction and control of the Federal Aviation Administration (FAA). The FAA has not yet issued governing regulations concerning their use. The FAA requires that UAVs must have onboard “detect, see and avoid” (DSA) capabilities to prevent in-air collisions. In addition, the Federal Communications Commission (FCC) regulates all non-Federal areas of communications and radio/television transmission in the United States. Wireless transmissions to and from the UAVs must meet all applicable FCC rules [2]. A fail-safe option for the mission must automatically apply if the ground to UAV communication link is lost, to prevent hazards from a UAV crashing to the ground.Apart from getting clearance from the FAA and the FCC, some other key issues that need to be addressed for the successful deployment and acceptance of UAVs are:Physical LayerThe setup requires locations of ground base stations such as the microwave towers. There are issues such as bandwidth requirement, channel characteristics, transceiver design, range of aerial platform to ground base stations, power and fuel consumption.Communication Properties IssuesThe UAV and the base station must have the ability to transmit and receive video, data, and control signals in a reliable and failsafe way. Issues to be considered under this section are high-bandwidth requirements, asymmetric data communications, integrationwith ground sensors, potential real-time communications with an incident commander, and distributed data exchange.Communication Network Layer IssuesIssues such as network configuration and reconfiguration, fixed infrastructure versus ad hoc networks, adaptive quality-of-service, mobility management (location update and handoff), and ground station (tower) location and distribution need to be covered for proper communication between the ground base stations and the UAV.There are several more issues, such as spectrum allocation (unlicensed versus licensed), data security, and political and public acceptability, which need to be taken care off for the successful deployment of UAVs in civil airspace. Ground crew training and pilot certifications are required to fly the UAV. Also, various economics are involved such as system and lifecycle cost of hardware, software, data products, training and certification of ground crew, analysts etc. Yet, the most important issue remains the safety involved in flying the UAV in civil airspace; it should be a hazard to other aircraft, ground vehicles, people and facilities.Many agencies, industry, and universities along with the FAA have made efforts to develop alternative regulatory tools for UAV deployment. The DOD has developed and updated its 25 years strategic UAV technology deployment roadmap, which could benefit manufacturers of civilian and commercial UAVs [32]. The ACCESS 5 regulatory UAV road-mapping efforts are funded by NASA, DOD and industry (UNITE) with FAA participation. They focus on the high end UAVs used primarily by DOD. Several voluntary standards and professional associations (ASTM, RTCA, AIAA, ICAO) have formed UAV standard committees to develop appropriate UAV safe operability standards for the FAA.4. Existing Systems and Current Research WorkSeveral types of aerial surveys have been used or tested to measure data related to traffic management. The method of using fixed-wing aircraft to collect congestion and traffic information was being used as early as 1965 by a transportation consultant in Maryland. Researchers from the University of Karlsruhe in Germany examined the matching of vehicle images from aircraft in 1987. New methods of improving this technology are under development and research at various universities around the world. Researchers have tried experimenting on fixed wing aircraft, helicopter, observation balloons, and satellites.Fixed-wing or Rotary-wing vehicles are been used as experimental aircrafts at several Universities. Bridgewater State College, Geodata Systems, and the MLB Company developed small winged craft with live video feeds and high resolution still imagery, and examined the suitability of the data for various applications. Iowa State University investigated camera equipped helium balloons that could be launched at short notice frompickup trucks. This section covers some of the research work on-going at several universities such as University of Florida, Ohio State University, Linkoping University (LiU), Sweden, Georgia-Tech, Stanford, Carnegie Mellon University, etc.4.1 University of Florida – Airborne Traffic Surveillance Systems (ATSS)ATSS is a project initiated by the University of Florida (UFL). The ATSS research team includes the UFL research team, the Florida Department of Transportation (FDOT), Tallahassee Commercial Airport, and University of North Florida Road Weather Information System (RWIS) Research Team [1]. FDOT organized a proof-of-concept test to choose UFL as the primary contractor for conducting this project [4].The primary interest of this project to the FDOT is monitoring remote and rural areas of the state of Florida. The ATSS proof-of-concept project also aims at evaluating the feasibility of the wireless communication systems, as well as switching of the video. The project serves as a case-study for the use of UAVs in remote sensing and multimodal transportation.The SRA/Aerosonde was chosed as the UAV vendor. The Aerosonde UAV is a fixed-wing vehicle made in Australia and operated by Aerosonde Pty Ltd (AePL). It flies for over 32 hours, at an altitude of between 300 to 20000 feet above the ground, where it will be largely invisible during daylight hours. The Aerosonde employs a Sony XC555 video camera, which captures video of the traffic on the highway; and a pair of Vaisala RSS901 weather sondes to gather freeway surveillance and RWIS data for transmission to the FDOT microwave towers [4]. The data and video are transmitted using a 2.4 GHz wireless link.Figure 1: The Aerosonde UAVThe proof of concept test intended to show that the UAV can fly for a certain distance collecting traffic information and successfully transmit it to the base stations. A small segment of highway between two of FDOTs microwave towers, at Lake City and White Springs, was chosen. The UAV is expected to capture and transmit the video in real-time while it flies along the highway. The aim is to investigate the integration of ATSS into FDOT’s existing microwave network, Traffic Management Centers (TMCs) and the State Emergency Operations Center (SEOC).Figure 2: UAV captures video on highway [1].The base station consists of video encoders, which receives the video from the UAV, encodes it and transfers it to the FDOT network. Both towers would transmit different signals with different signal strengths. These signals and data are received by the SEOC. Based on the signal strength and the designated handoff algorithm, SEOC switches the video signals and shows the video of highway traffic received by the better signal.Figure 3: Video Encoding and Recording at the Microwave Tower [1].Figure 4: Video Decoding, Switching and Display at the SEOC [1].UFL has developed two software programs, SignalReader and VideoProcessor for efficient communication and processing of the video signals. SignalReader reads the signal strength received from the video receiver, uses an internal algorithm to parse the signals into the correct format, accurately decodes it, and transmits the signal strength value over the microwave IP network using TCP client sockets. VideoProcessor receives the video signals from the two microwave towers, encodes them in Windows Media format, and uses an embedded multimedia player to play the streaming video. It also switches the video signals based on a handoff algorithm built into the program. Simulated tests were performed in December 2003 and January 2004, using the communications equipment, FDOT’s microwave IP network and UFL-developed software, to demonstrate the feasibility of the project. Another simulated test was performed in April 2004 on the site, with the UFL research team testing the equipment and software at Lake City, White Springs and the SEOC. These tests demonstrated that the ATSS project is completely capable of supporting ground communication between the towers and the SEOC.4.2 WITAS Unmanned Aerial Vehicle ProjectThe Wallenberg Laboratory for Information Technology and Autonomous Systems (WITAS) is conducting a long-term basic research project on Unmanned Aerial Vehicles at the Linkoping University (LiU), Sweden [17]. The project is multi-disciplinary and in cooperation with a number of Universities in Europe, USA and South America. The goal of this project is to develop technologies and functionalities necessary for the successful deployment of a fully autonomous UAV operating over diverse geographical terrain containing road and traffic networks. It involves integration of autonomy with an active vision system consisting of digital video and IR cameras, and a ground control dialogue system.The UAV is intended to navigate autonomously at different altitudes, plan for mission goals such as locating, identifying, tracking and monitoring different vehicle types, and construct internal representations of its focus of attention for use in achieving its mission goals [17], [20]. The project also aims for identifying complex patterns of behavior such as vehicle overtaking, traversing of intersections, parking lot activities, etc. The main goals of this ongoing research project are:•Development of reliable software and hardware architectures with both deliberative and reactive components for autonomous control of UAV platforms;•Development of sensory platforms and sensory interpretation techniques with an emphasis on active vision systems to deal with real-time constraints in processing sensory data;•Development of efficient inferencing and algorithmic techniques to access geographic, spatial and temporal information of both a dynamic and static character associated with the operational environment;•Development of planning, prediction and chronicle recognition techniques to guide the UAV and predict and act upon behaviors of vehicles on ground; and •Development of simulation, specification and verification techniques and modeling tools specific to the complex environments and functionalities associated with the project.WITAS uses a generic UAV setup consisting of an air vehicle with a still or video camera, a tactical control station with one or more humans in the loop, and a data-link between the station and air vehicle used for downloading images and data and for uploading navigation and camera control commands. WITAS is currently collaborating with Scandicraft Systems, a university spin-off company that develops autonomous mini-helicopters [17]. The Apid Mk III has a payload of 20 kg including fuel. WITAS is also considering using Yamaha RMAX Aero Robots, which as a payload of around 30 kg which is helpful for an extra set of camera housing and on-board system.Figure 5: The Scandicraft Apid Mk III UAV. Picture taken from/Since80s/h_apid3.phpThe WITAS project is divided into four stages. The first stage involves collection of a library of video sequences of various vehicle scenarios and traffic patterns. In stage two, a mathematical model of the helicopter platform is derived, which is used as the basis forexperimentation and development of robust fuzzy controllers for the platform. The project is currently at the end of stage two and beginning of stage three. Stage three would include the basic development of the on-board system, which will be initially used from the ground to control the Scandicraft platform. The input to the ground system consists of helicopter state and sensor information in addition to analogue video received via a radio link. The output from the system and to the helicopter platform consists of flight control and camera control commands. The fourth and final stage will integrate the system developed in stage three and be placed on-board the platform where both semi- and fully autonomous experimentation will ensue.The project uses an Intelligent Vehicle Control Architecture (IVCA), which is a multi-layered hybrid deliberative/reactive software architecture. The architecture contains two main information repositories, the Knowledge Structure Repository (KSR) and the Geographic Data Repository (GDR). The deliberative and reactive layers of the architecture communicate directly with the core vision system. The vision system tries to determine the position, velocity, color and type of vehicle, or vehicles, in the foci of attention. This involves accurately determining the position of the UAV and camera angles, mapping positions in image coordinates to geographical coordinates, anchoring identified objects into qualitative descriptions of road segments, estimating absolute and relative motions of objects, and indexing or matching the view from the camera with the information in the GDR so as to derive additional information about a situation, or generate additional constraints to assist the operations carried out in the vision system.A model-based distributed simulation environment was developed, to support the design and evaluate the software architectures and helicopter controllers. A set of scenarios are devised to test various functionalities of the architecture. For the purpose of generating a realistic simulation environment, all the data is collected using manned helicopters, and the data is post processed off-line.Figure 6: Virtual Simulation: Traffic/Tunnel Scenario; Pseudo-Virtual Simulation overStockholm4.3 Ohio State UniversityThis research is an Ohio Department of Transportation research consortium led by Ohio State University (OSU) [5]. The project is pioneered by National Consortium on Remote Sensing in Transportation (NCRST). The UAV used for experimentation by OSU uses the BAT III technology provided by the MLB Company, carrying a payload of two video cameras, and can fly at an altitude of 500 ft with an air speed of 30 mph.Figure 7: MLB BAT 3 Technology. Image taken from/bat3.htmlThe BAT technology acquires information of the vehicle using videos and sensors such as GPS, and communicates with the base station on a 2.4 GHz data link.Figure 8: BAT Technology used in OSU research.The field experiments were conducted in July 2003 at Columbus, OH, on different freeway scenarios, collecting information on freeway conditions, intersection movements, network paths and parking lot monitoring. The UAV flew at an altitude of 500 ft and an air speed of 30 mph while transmitting the video images collected by its on-board camera to the ground station in real-time. The UAV flew over a freeway for thepurpose of observing flows, speeds, densities, off-ramp weaving, and vehicle trajectories. It also observed the flows, turning movements and queue lengths on intersections while gathering information on a network consisting of seven intersections [5]. Information collected by such flights can be useful in accessing and predicting network conditions which can be used by the state DOT to control real-time signal timing depending on the link speeds, link densities and queue lengths. The final scenario was that the UAV made a tour of surface parking lots to assess their utilization. The information gathered from such scenario can be helpful in space planning and distribution. It can also provide quasi real-time information to travelers.Figure 9: Views of the SR 315 freeway interchange with Lane Ave captured in real-time from a UAV. (Left): Wide angle view looking south while flying along the freeway (Right): telephoto view looking south while flying along the freeway.Figure 10: An example of circling a facility with the UAV, showing queue lengths andturning movements.Figure 11: An example of (Left): circling a network with the UAV, (Right): showsutilization of three parking lots.The team at the Ohio State University is now focusing on learning, discovering, and developing potential benefits of UAV applications to transportation surveillance; and quantifying the value of the potential benefits. The field experiment provides a strong indication that the application of the UAV technology to surface transportation surveillance seems viable and potentially viable. It was observed during these experiments that the UAV followed its pre-programmed flight plan covering the locations of interest accurately. Though, various refinements are yet to be made based on the current observations made through the experiments. It was observed that better resolution is required to identify distinguished characteristics in individual vehicles. Also, radio interference was observed beyond distance of 1 mile, corrupting the images. Such problems need to be addressed by utilizing a dedicated communication channel.4.4 Georgia Tech’s Traffic Surveillance DroneTraffic Surveillance Drone is a project funded by the Georgia Department of Transportation and the Federal Highway Administration’s Priority Technology Program. It is being developed at the Georgia Tech Research Institute’s (GTRI) Advanced Vehicle Development and Integration Laboratory. The focus of this research is on development of generic VTOL UAV test-bed that may be used to flight test other research projects such as advanced controllers, fault-tolerance algorithms and autonomous operation algorithms. This drone is being designed and tested with affordability and safety in mind, making it attractive to the law enforcement agencies, emergency search and rescue teams, and the highway departments.Figure 12: Traffic Surveillance Drone developed at Georgia Tech. Image taken from /RCM/RCM/DronePictures/GDOT_Drone.GIFA militarized version of the drone known as Dragon Stalker has capabilities that make it attractive in case of low intensity conflicts and urban warfare. Both the versions of the drone are VTOLs and hence they don’t need much space for takeoff and landing purposes.The drone will be able to relay live video and two-way audio from the site of traffic incidents, back into the state’s Advanced Traffic Management System (ATMS). The images would be relayed from 5 to 10 miles via a spread spectrum link. The initial effort involves the development and working of a prototype to demonstrate the capabilities of traffic data collection. It will be capable of 30 minutes of flight at a maximum speed of 30 knots. The projection is that the later versions of this VTOL vehicle will be fully autonomous. They are focusing in developing software-enabled control methods for complex dynamic systems with application focus on intelligent UAVs. Also, further plans consist of developing intelligent, agent-based mission planning algorithms in order to achieve dynamic performance and flight control command generation under various aircraft dynamics and environmental constraints.4.5 University of California, Berkeley: Ultimate Auto-PilotThe University of California at Berkeley is building intelligent guidance systems for UAVs, which may be used for monitoring traffic conditions, collecting data from environmental sensors, etc. The project is sponsored by the Office of Naval Research’s (ONR) Autonomous Intelligent Network and Systems (AINS) program.The first goal of this project is to develop strategies of path-planning for a UAV to track a ground vehicle. An algorithm based on waypoint strategy was created. The computer vision system detects natural features of the scene and tracks the roadway in order to determine relative yaw and lateral displacement between the aircraft and the road. The UAV will fly in a sinusoidal manner at a constant velocity while tracking the ground vehicle which has varying speed. If the ground vehicle is not moving, or its speed is under a selected threshold, the UAV starts to follow a circular path or rose curve trajectory. The effect of wind disturbances has been taken to offset the planned UAV trajectory. The path-planning algorithm has been developed, tested and debugged using the “controller development platform” [12].Figure 13: Experimental autonomous aircraft: Sig Rascal radio-controlled airplane。