基于LabVIEW Vision的无人机自主着降系统设计与实现

四旋翼无人机具有低成本、垂直起降、机动性好等优点,在民用领域诸如航拍、植保、物流、电力巡线等场合得到了广泛的关注和应用;随着四旋翼无人机应用的普及,一些新的问题也随之出现,其中一个厄待解决的问题是如何提高无人机的降落精度和可靠性,特别在超视距应用场景下,这一需求尤为突出;通过采用机器视觉技术,利用LabVIEW Vision编制视觉识别软件,可以控制四旋翼无人机实现高精度的自主降落。     

舰载无人机自主着舰回收制导与控制研究进展

舰载无人机正成为未来海战的重要组成部分，制导与控制是舰载无人机自主着舰/回收的关键技术.本文综述了舰载无人机自主着舰/回收制导与控制技术.概述了舰载无人机的发展历史，简单描述了舰载无人机跑道拦阻着舰、撞网回收、伞降回收、绳钩回收、天钩回收、过失速着舰、智能飞落着舰、风向筒回收、秋千式吊架回收等典型着舰回收方式.在深入分析无人机自主着舰/回收制导与控制关键问题的基础上，重点概述了无人机着舰/回收经典制导与现代制导技术，以及着舰/回收经典控制、现代控制、非线性与自适应控制、智能控制等飞行控制技术的研究现状.最后，对无人机自主着舰/回收制导与控制技术的发展状况进行总结，并对未来研究重点进行展望.     

Robotic technologies for solar‐powered UAVs: Fully autonomous updraft‐aware aerial sensing for multiday search‐and‐rescue missions

Large‐scale aerial sensing missions can greatly benefit from the perpetual endurance capability provided by high‐performance low‐altitude solar‐powered unmanned aerial vehicles (UAVs). However, today these UAVs suffer from small payload capacity, low energetic margins, and high operational complexity. To tackle these problems, this paper presents four individual technical contributions and integrates them into an existing solar‐powered UAV system: First, a lightweight and power‐efficient day/night‐capable sensing system is discussed. Second, means to optimize the UAV platform to the specific payload and to thereby achieve sufficient energetic margins for day/night flight with payload are presented. Third, existing autonomous launch and landing functionality is extended for solar‐powered UAVs. Fourth, as a main contribution an extended Kalman filter (EKF)‐based autonomous thermal updraft tracking framework is developed. Its novelty is that it allows the end‐to‐end integration of the thermal‐induced roll moment into the estimation process. It is assessed against unscented Kalman filter and particle filter methods in simulation and implemented on the aircraft's low‐power autopilot. The complete system is verified during a 26 h search‐and‐rescue aerial sensing mock‐up mission that represents the first‐ever fully autonomous perpetual endurance flight of a small solar‐powered UAV with a day/night‐capable sensing payload. It also represents the first time that solar‐electric propulsion and autonomous thermal updraft tracking are combined in flight. In contrast to previous work that has focused on the energetic feasibility of perpetual flight, the individual technical contributions of this paper are considered core functionality to guarantee ease‐of‐use, effectivity, and reliability in future multiday aerial sensing operations with small solar‐powered UAVs.     

Fuzzy Logic Based Approach to Design of Autonomous Landing System for Unmanned Aerial Vehicles

This paper is concerned with autonomous flight of UAVs and proposes a fuzzy logic based autonomous flight and landing system controller. Besides three fuzzy logic controllers which are developed for autonomous navigation for UAVs in a previous work as fuzzy logic based autonomous mission control blocks, three more fuzzy logic modules are developed under the main landing system for the control of the horizontal and the vertical positions of the aircraft against the runway under a TACAN (Tactical Air Navigation) approach. The performance of the fuzzy logic based controllers is evaluated using the standard configuration of MATLAB and the Aerosim Aeronautical Simulation Block Set which provides a complete set of tools for rapid development of 6 degree-of-freedom nonlinear generic manned/unmanned aerial vehicle models. Additionally, FlightGear Flight Simulator and GMS aircraft instruments are deployed in order to get visual outputs that aid the designer in evaluating the performance and the potential of the controllers. The simulated test flights on an Aerosonde indicate the capability of the approach in achieving the desired performance despite the simple design procedure.     

Timely autonomous identification of UAV safe landing zones

For many applications such as environmental monitoring in the aftermath of a natural disaster and mountain search-and-rescue, swarms of autonomous Unmanned Aerial Vehicles (UAVs) have the potential to provide a highly versatile and often relatively inexpensive sensing platform. Their ability to operate as an ‘eye-in-the-sky’, processing and relaying real-time colour imagery and other sensor readings facilitate the removal of humans from situations which may be considered dull, dangerous or dirty. However, as with manned aircraft they are likely to encounter errors, the most serious of which may require the UAV to land as quickly and safely as possible. Within this paper we therefore present novel work on autonomously identifying Safe Landing Zones (SLZs) which can be utilised upon occurrence of a safety critical event. Safe Landing Zones are detected and subsequently assigned a safety score either solely using multichannel aerial imagery or, whenever practicable by fusing knowledge in the form of Ordnance Survey (OS) map data with such imagery. Given the real-time nature of the problem we subsequently model two SLZ detection options one of which utilises knowledge enabling the UAV to choose an optimal, viable solution. Results are presented based on colour aerial imagery captured during manned flight demonstrating practical potential in the methods discussed.     

Unmanned aerial vehicles using machine learning for autonomous flight; state-of-the-art

In recent years, since researchers began to study on Unmanned Aerial Vehicles (UAVs), UAVs have been integrated into today's everyday life, including civilian area and military area. Many researchers have tried to make use of UAVs as an ideal platform for inspection, delivery, surveillance, and so on. In particular, machine learning has been applied to UAVs for autonomous flight that enables UAVs do designated task more efficiently. In this paper, we review the history and the classification of machine learning, and discuss the state-of-the-art machine learning that has been applied to UAVs for autonomous flight. We provide control strategies including parameter tuning, adaptive control for uncertain environment, and real-time path planning, and object recognition that have been described in the literature.     

Integral Backstepping Control of an Unconventional Dual-Fan Unmanned Aerial Vehicle

In this paper, a dynamic model of vertical take-off and landing (VTOL) aerial vehicles, having lateral and longitudinal rotor tilting mechanism, is first developed using a Newton–Euler formulation. Then an integral backstepping (IB) control technique is proposed to improve the pitch, yaw, and roll stability of the vehicle. Such control mechanisms enables the UAV to perform complex tasks that no other Unmanned Aerial Vehicles (UAVs) can execute such as hover pitched. This control tactic allows the vehicle under investigation, eVader, to use the full potential of its flying characteristics enabled by the novel dual-axis oblique active tilting (OAT) mechanism, which enables it to maneuver inside obstructed environments. The potential of the eVader as a small UAV and its model are verified and then used to for autonomous take-off and landing as well as stabilizing the vehicle’s attitude. Finally, diverse simulation scenarios on attitude and position control, stabilization and autonomous take off and landing are presented.     

Visual servoing for underactuated VTOL UAVs: a linear,homography‐based framework

The paper concerns the control of vertical take‐off and landing (VTOL) underactuated aerial vehicles (UAVs) in hover flight, on the basis of measurements provided by an onboard video camera. The objective is to stabilize the vehicle to the equilibrium pose associated with an image of a planar target, using a minimal sensor suite and poor knowledge of the environment. By using the homography matrix computed from the camera measurements of the target, stabilizing feedback laws are derived on the basis of the visual data and gyrometer measurements only. Explicit stability conditions on the control parameters are provided, showing that a proper tuning of the control parameters ensures a large robustness margin with only planar target and visibility assumptions, although the target size and orientation, the UAV position, linear velocity and orientation are unknown. Additional issues, such as the use of accelerometers to improve the UAV's positioning in the case of unmodeled dynamics (such as wind), are also considered. Copyright © 2013 John Wiley & Sons, Ltd.     

Output feedback observation and control for visual servoing of VTOL UAVs

This paper considers the question of stabilizing vertical take‐off and landing (VTOL) unmanned aerial vehicles (UAVs) using a minimal and an inexpensive sensor suite. The sensor suite considered consists of rate gyros and a single embedded video camera system. The approach taken is based on the principles of dynamic image‐based visual servo control and extends the authors' earlier work by removing dependence on additional sensors, such as accelerometers and magnetometers. The technique presented has potential for mini‐UAVs evolving in urban areas where global positioning system signals may not be available. The control algorithms and coupled state observers are presented in details. The main result of the paper is synthesized in a theorem along with a rigorous stability analysis of the closed‐loop system. An estimate of the basin of attraction for the closed‐loop system is provided. Simulation results are performed to illustrate the performance of the proposed control. Copyright © 2010 John Wiley & Sons, Ltd.     

Multiple eyes in the skies: architecture and perception issues in the COMETS unmanned air vehicles project

This paper describes the COMETS (Real-Time Coordination and Control of Multiple Heterogeneous Unmanned Aerial Vehicles) Project, which is aimed at designing and implementing a system for cooperative activities using heterogeneous UAVs. Heterogeneity is considered both in terms of aerial vehicles and onboard processing capabilities ranging from fully autonomous systems to conventional remotely piloted vehicles. COMETS also involves cooperative environmental perception including fire detection and monitoring as well as terrain mapping.     

崎岖地表上的旋翼无人机自主安全降落系统

针对旋翼无人机全自主作业的需求,构建了崎岖地表上的旋翼无人机自主安全降落系统．该系统通过机载实时运算自动分析落区地形,寻找可行落点并实施自动降落．系统以低成本的立体RGB-D相机作为深度传感设备,利用截断符号距离函数（TSDF）对着陆区地形进行实时3维建模,生成低噪的落区地形深度图像,并设计了一种适应起落机构形状的实时精细落点搜索方法,最后使用级联PID（比例－积分－微分）控制器控制无人机实施安全降落．系统基于大疆M100无人机平台实现,定制了仿真器进行算法调试,并最终在实际的崎岖地表上实现了自主安全降落．本文工作可为旋翼无人机紧急降落、物流运输或者灾后搜救提供有效安全的解决方案．     

The nested k‐means method: A new approach for detecting lost persons in aerial images acquired by unmanned aerial vehicles

A new method, named as the nested k‐means, for detecting a person captured in aerial images acquired by an unmanned aerial vehicle (UAV), is presented. The nested k‐means method is used in a newly built system that supports search and rescue (SAR) activities through processing of aerial photographs taken in visible light spectra (red‐green‐blue channels, RGB). First, the k‐means classification is utilized to identify clusters of colors in a three‐dimensional space (RGB). Second, the k‐means method is used to verify if the automatically selected class of colors is concurrently spatially clustered in a two‐dimensional space (easting‐northing, EN), and has human‐size area. The UAV images were acquired during the field campaign carried out in the Izerskie Mountains (SW Poland). The experiment aimed to observe several persons using an RGB camera, in spring and winter, during various periods of day, in uncovered terrain and sparse forest. It was found that the nested k‐means method has a considerable potential for detecting a person lost in the wilderness and allows to reduce area to be searched to 4.4 and 7.3% in spring and winter, respectively. In winter, land cover influences the performance of the nested k‐means method, with better skills in sparse forest than in the uncovered terrain. In spring, such a relationship does not hold. The nested k‐means method may provide the SAR teams with a tool for near real‐time detection of a person and, as a consequence, to reduce search area to approximately 0.5–7.3% of total terrain to be visited, depending on season and land cover.     

Guidance and nonlinear control system for autonomous flight of minirotorcraft unmanned aerial vehicles

Small unmanned aerial vehicles (UAVs) are becoming popular among researchers and vital platforms for several autonomous mission systems. In this paper, we present the design and development of a miniature autonomous rotorcraft weighing less than 700 g and capable of waypoint navigation, trajectory tracking, visual navigation, precise hovering, and automatic takeoff and landing. In an effort to make advanced autonomous behaviors available to mini‐ and microrotorcraft, an embedded and inexpensive autopilot was developed. To compensate for the weaknesses of the low‐cost equipment, we put our efforts into designing a reliable model‐based nonlinear controller that uses an inner‐loop outer‐loop control scheme. The developed flight controller considers the system's nonlinearities, guarantees the stability of the closed‐loop system, and results in a practical controller that is easy to implement and to tune. In addition to controller design and stability analysis, the paper provides information about the overall control architecture and the UAV system integration, including guidance laws, navigation algorithms, control system implementation, and autopilot hardware. The guidance, navigation, and control (GN&C) algorithms were implemented on a miniature quadrotor UAV that has undergone an extensive program of flight tests, resulting in various flight behaviors under autonomous control from takeoff to landing. Experimental results that demonstrate the operation of the GN&C algorithms and the capabilities of our autonomous micro air vehicle are presented. © 2009 Wiley Periodicals, Inc.     

Monocular Visual Mapping for Obstacle Avoidance on UAVs

An unmanned aerial vehicle requires adequate knowledge of its surroundings in order to operate in close proximity to obstacles. UAVs also have strict payload and power constraints which limit the number and variety of sensors available to gather this information. It is desirable, therefore, to enable a UAV to gather information about potential obstacles or interesting landmarks using common and lightweight sensor systems. This paper presents a method of fast terrain mapping with a monocular camera. Features are extracted from camera images and used to update a sequential extended Kalman filter. The features locations are parameterized in inverse depth to enable fast depth convergence. Converged features are added to a persistent terrain map which can be used for obstacle avoidance and additional vehicle guidance. Simulation results, results from recorded flight test data, and flight test results are presented to validate the algorithm.     

基于地标的轻量化无人机精准降落算法

轻量化无人机的机载计算机通常计算性能较弱,很难满足无人机实现精准降落的需要。针对这一问题,提出了一种基于地标的轻量化精准降落算法,通过识别对比颜色和指定形状实现快速实时地检测着陆标识,图像处理流程简单快速且准确,通过相对位置计算在二维层面得到无人机对于降落地标的相对位置和方向,引导无人机精准降落,算法执行过程不需要考虑相机焦距。实际测试结果表明,在一些特定如无人机自主充电的应用场景,该算法过程简单,稳定性和降落精度相较于传统方法较高。     

UAV Task Assignment

Unmanned aerial vehicles (UAVs) are becoming vital warfare and homeland security platforms because they have the potential to significantly reduce cost and risk to human life while amplifying warfighter and first-responder capabilities. This article builds on the very active area of planning and control for autonomous multiagent systems. This work represents a step toward enabling robust decision making for distributed autonomous UAVs by improving the team's operational reliability and capabilities through better system self-awareness and adaptive mission planning. The health-aware task assignment algorithm developed in this article was demonstrated to be effective both in simulation and flight experiments.     

Recognition of a landing platform for unmanned aerial vehicles by using computer vision-based techniques

The use of Unmanned Aerial Vehicles (UAVs) is growing significantly for many and varied purposes. During the mission, an outdoor UAV is guided by following the planned path using GPS signals. However, the GPS capability may become defective or the environment may be GPS-denied, and an additional safety aid is therefore required for the automatic landing phase that is independent of GPS data. Most UAVs are equipped with machine vision systems which, together with onboard analysis, can be used for safe, automatic landing. This contributes greatly to the overall success of autonomous flight.This paper proposes an automatic expert system, based on image segmentation procedures, that assists safe landing through recognition and relative orientation of the UAV and platform. The proposed expert system exploits the human experience that has been incorporated into the machine vision system, which is mapped into the proposed image processing modules. The result is an improved reliability capability that could be incorporated into any UAV, and is especially robust for rotary wing UAVs. This is clearly a desirable fail-safe capability.     

Development and application of an autonomous unmanned aerial vehicle for precise aerobiological sampling above agricultural fields

Remote‐controlled (RC) unmanned aerial vehicles (UAVs) have been used to study the movement of agricultural threat agents (e.g., plant and animal pathogens, invasive weeds, and exotic insects) above crop fields, but these RC UAVs are operated entirely by a ground‐based pilot and often demonstrate large fluctuations in sampling height, sampling pattern, and sampling speed. In this paper, we describe the development and application of an autonomous UAV for precise aerobiological sampling tens to hundreds of meters above agricultural fields. We equipped a Senior Telemaster UAV with four aerobiological sampling devices and a MicroPilot‐based autonomous system, and we conducted 25 sampling flights for potential agricultural threat agents at Virginia Tech's Kentland Farm. To determine the most appropriate sampling path for aerobiological sampling above crop fields with an autonomous UAV, we explored five different sampling patterns, including multiple global positioning system (GPS) waypoints plotted over a variety of spatial scales. An orbital sampling pattern around a single GPS waypoint exhibited high positional accuracy and produced altitude standard deviations ranging from 1.6 to 2.8 m. Autonomous UAVs have the potential to extend the range of aerobiological sampling, improve positional accuracy of sampling paths, and enable coordinated flight with multiple UAVs sampling at different altitudes. © 2008 Wiley Periodicals, Inc.     

基于微型自动驾驶飞机的航拍航摄遥感系统

飞机是一种重要的遥感平台。我们发展了一套基于微型自动驾驶飞机的航拍航摄系统。该系统具有以下特点：（1）野外作业机动灵活,对场地和气象等条件要求不高;（2）费用较低,无人身安全之虑;（3）所获得的相片和图像清晰,分辨率较高,可达到分米的量级;（4）在航拍航摄时,同时有GPS信息（即经、纬度和高度）;（5）可进行定点定高拍摄,适合对无人区的遥感普查（例如：土地利用和气象及地质灾害等）。     

Homography-based depth recovery with descent images

In planetary landing exploration task, the images captured by the landing camera are nearly along optical axis which results in multi-resolution images of same terrain surface. Recovering the surface shape of landing terrain from descent imagery is of great value for lander to choose safe landing area. In this paper, a homography-based depth recovery method with descent images is addressed. At first, the parallax and scale change in descent images are analyzed. Second, the camera motion is optimized with SIFT features correspondence constraints. For dense depth recovery, a set of virtual parallel planes is assumed to slice the terrain and each plane induces a homography to warp back the second image to first image plane. Zero-normalized cross-correlation score is chosen to compute the correlation score and the correlation curve is smoothed by two Gaussian filters. The depth for each pixel is determined by the plane which has highest correlation value. At the end, some experiments are conducted, including different correlation computation, depth recovery with different terrain, and the error tests. The results show that the discussed method is feasible to recover the depth information overall.