Obstacle Avoidance for Unmanned Aerial Vehicles

This work is framed within the PITVANT project and aims to contribute to the development of obstacle avoidance techniques for unmanned aerial vehicles (UAVs). The paper describes the design, implementation and experimental evaluation of a potential field obstacle avoidance algorithm based on the fluid mechanics panel methods. Obstacles and the UAV goal position are modeled by harmonic functions thus avoiding the presence of local minima. Adaptations are made to apply the method to the automatic control of a fixed wing aircraft, relying only on a local map of the environment that is updated with information from sensors onboard the aircraft. Hardware-In-Loop simulations show the good performance of the proposed algorithm in the envisioned mission scenarios for the PITVANT vehicles.     

基于几何法的无人机航迹规划

针对无人机的三维航迹规划问题,提出了一种基于几何法的航迹规划算法.通过对无人机飞行航迹的分析,将无人机航迹看作是由一系列直线、圆弧和螺旋线的有序组成.研究了无人机最大过载系数、最大平飞速度、升限等性能与航迹可行性的关系,得出生成最优航迹的限定条件.讨论了几何法无人机航迹规划的步骤.按照几何原理对无人机航迹进行了规划,将无人战斗机的机动性能在航迹中充分体现出来.计算机仿真结果表明用几何法规划的航迹具有较好的适用性.     

UAV中图像处理系统的研究

对UAV中的图像处理系统进行了探讨,给出了一个切实可行的图像处理系统,包括系统结构、硬件组成、软件设计等。其中,软件设计利用了Intel公司的开放源代码图像处理工具包OpenCV,并且在目标检测上取得了较好的效果。     

基于定制内点法的多无人机协同轨迹规划

为提高多无人机(Unmanned aerial vehicles, UAV)协同轨迹规划(Cooperative trajectory planning, CTP)效率, 在解耦序列凸优化(Sequential convex programming, SCP)方法基础上, 提出一种高效求解凸优化子问题的定制内点法. 首先引入松弛变量, 构建子问题的等价描述形式, 并推导该形式下的子问题最优性条件. 然后在预测−校正原对偶内点法的框架下, 构建一套高效求解最优性条件方程组的计算流程以降低子问题计算复杂度, 并利用约束矩阵特征提出一种快速计算原对偶搜索方向的方法以提高规划效率. 仿真结果表明, 在解耦序列凸优化框架下, 定制内点法可将协同轨迹规划耗时降低一个数量级, 达到秒级.     

四旋翼无人机自适应导航控制

研究四旋翼(Quadrotor)无人机导航控制问题。针对传统的四旋翼无人机导航控制方法的目标定位误差和实时性差问题,提出了基于CLOS技术的导航控制方法。采用CLOS技术所开发的导航控制系统使得四旋翼无人机能够在移动停机坪完成自主导航和着陆的任务,并详细研究了导航控制系统的设计和仿真。仿真结果显示了所设计的导航控制系统的性能和有效性,可应用于四旋翼无人机的实时导航。     

Temporal Range Registration for Unmanned Ground and Aerial Vehicles

An iterative temporal registration algorithm is presented in this article for registering 3D range images obtained from unmanned ground and aerial vehicles traversing unstructured environments. We are primarily motivated by the development of 3D registration algorithms to overcome both the unavailability and unreliability of Global Positioning System (GPS) within required accuracy bounds for Unmanned Ground Vehicle (UGV) navigation. After suitable modifications to the well-known Iterative Closest Point (ICP) algorithm, the modified algorithm is shown to be robust to outliers and false matches during the registration of successive range images obtained from a scanning LAser Detection And Ranging (LADAR) rangefinder on the UGV. Towards registering LADAR images from the UGV with those from an Unmanned Aerial Vehicle (UAV) that flies over the terrain being traversed, we then propose a hybrid registration approach. In this approach to air to ground registration to estimate and update the position of the UGV, we register range data from two LADARs by combining a feature-based method with the aforementioned modified ICP algorithm. Registration of range data guarantees an estimate of the vehicle's position even when only one of the vehicles has GPS information. Temporal range registration enables position information to be continually maintained even when both vehicles can no longer maintain GPS contact. We present results of the registration algorithm in rugged terrain and urban environments using real field data acquired from two different LADARs on the UGV. ★Commercial equipment and materials are identified in this article in order to adequately specify certain procedures. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.     

Hardware-in-the-Loop Verification for Mobile Manipulating Unmanned Aerial Vehicles

A hardware-in-the-loop test rig is presented to bridge the gap between basic aerial manipulation research and the ability of flying robots to perform tasks such as bridge repair, agriculture care, container inspection and other applications requiring interaction with the environment. Unmanned aerial vehicles have speed and mobility advantages over ground vehicles and can operate in 3-dimensional workspaces. In particular, the usefulness of these capabilities is highlighted in areas where ground robots cannot reach or terrains they are unable to navigate. However, most UAVs operating in near-earth or indoor environments still do not have the payload capabilities to support multi-degree of freedom manipulators. We present a rotorcraft emulation environment using a 7 degree of freedom manipulator. Since UAVs require significant setup time and to avoid potential crashes, our test and evaluation environment provides repeatable experiments and captures reactionary forces experienced during ground interaction. Recent experiments in insertion tests are presented. The lessons learned from these experiments will be ported onto an actual air vehicle with manipulator.     

基于XML的无人机任务链模型

对无人机系统地面任务控制站与飞行器通信协议中的任务链进行了研究。根据功能将任务链分为航路计划、传感器计划、通信计划和控制权限交接计划4个部分，建立了能够支持复杂任务的任务链模型。采用XML语言对任务链进行表达，增强了任务链的标准性和通用性。最后实现了任务控制站通过任务链控制多架无人机协同执行任务的仿真。     

低光照下的无人机异物检测与定位

为解决无人机在低光照环境下的巡检过程中, 不能对场景中的异物进行识别与定位, 导致后续智能算法无法获得环境语义信息的问题. 本文提出一种将ORB-SLAM2算法与适用于低光照目标检测改进的YOLOv5模型进行信息融合的方法. 首先, 通过RGB-D相机自采集低光照数据集进行深度学习训练及融合算法验证. 然后, 结合关键帧信息、目标检测模块的输出结果以及相机的固有信息完成目标像素坐标提取. 最后, 通过关键帧信息和像素坐标完成目标物体相对世界坐标系的位置解算. 本文实现了低光照环境下目标物体较为准确的识别和目标物体在世界坐标系中分米级的定位, 为低光照环境下无人机智能巡检提供了一种有效的解决方案.     

基于双目技术的无人机自主三维定位方法研究

无人机具有体积小、造价低、机动性强、隐蔽性好等优点,在军事侦查、地理勘测、目标跟踪等领域应用广泛。由于无法采用造价昂贵、体积庞大的精密惯导设备,当应用场景为弱或无GPS环境时,无人机的准确位置信息将难以获取,并且在实际飞行过程中还面临着GPS信号不稳或易遭欺骗等挑战。针对以上问题,提出了基于双目技术的无人机自主三维定位方法。介绍了双目立体视觉技术的光学物理原理,以及双目摄像头与目标的距离判别算法;设计了无人机通过双目技术进行三维定位的方法;详细说明了通过RSS定位方法计算出无人机的精确三维位置信息的运算步骤及矩阵计算式;通过MATLAB仿真实验检验了该基于双目技术的无人机自主三维定位方法的有效性和可实现性。结果表明,该无人机自主三维定位方法具有定位精度高、普适性强等特点。     

Rice Bacterial Infection Detection Using Ensemble Technique on Unmanned Aerial Vehicles Images

Establishing a system for measuring plant health and bacterial infection is critical in agriculture. Previously, the farmers themselves, who observed them with their eyes and relied on their experience in analysis, which could have been incorrect. Plant inspection can determine which plants reflect the quantity of green light and near-infrared using infrared light, both visible and eye using a drone. The goal of this study was to create algorithms for assessing bacterial infections in rice using images from unmanned aerial vehicles (UAVs) with an ensemble classification technique. Convolution neural networks in unmanned aerial vehicles image were used. To convey this interest, the rice’s health and bacterial infection inside the photo were detected. The project entailed using pictures to identify bacterial illnesses in rice. The shape and distinct characteristics of each infection were observed. Rice symptoms were defined using machine learning and image processing techniques. Two steps of a convolution neural network based on an image from a UAV were used in this study to determine whether this area will be affected by bacteria. The proposed algorithms can be utilized to classify the types of rice deceases with an accuracy rate of 89.84 percent.     

无人飞行器视觉SLAM特征检测和匹配算法研究

近几年图像局部特征检测和描述在机器人视觉中得到了广泛的应用,鲁棒的、快速且高精度的视觉特征检测和描述算法对飞行器进行实时的位姿估计和地图构建具有决定性意义;针对四旋翼无人飞行器平台的RGB-D传感器同时定位与地图构建(SLAM),讨论FAST、STAR、SIFT和SURF等检测算法和ORB、FREAK和SURF等匹配描述符的性能,对不同的特征算法进行对比评估出最合适的特征检测方法和匹配描述符;最后,基于Eclipse与OpenCV平台进行了实验,实验结果表明FAST检测和FREAK描述符比其他方法更适用于四旋翼飞行器在板视觉SLAM,且能基本满足实时性。     

基于状态空间MPC的无人机无人车联合运动控制

针对四旋翼无人机无人车联合运动缺乏对系统成员姿态约束的问题,研究了一种基于模型预测控制(MPC)的分布式联合运动控制方法.基于虚拟结构法,使用虚拟领航者策略,以虚拟领航者提供参考轨迹及参考速度,分别在各无人器平台上转换成各自所需的预测时域信息,结合推导的各无人器的状态空间模型滚动优化实现预测控制.限定四旋翼高度方向运动状态与偏航角,构造以俯仰角、横滚角与重力加速度乘积为位置运动输入的状态空间模型,将无人机内环姿态控制约束加入位置运动,增强飞行稳定性.改良无人车状态空间模型,增加速度信息得到可提供位置速度追踪的增广状态空间模型,增强运动追踪能力.仿真表明在满足无人器姿态约束条件下,能够保证联合运动的位置速度精度.     

基于随机森林的无人机检测方法

随着低成本小型无人机的普及带来了一系列的严重问题并且难以监管。并且,由于环境物体的扰动、摄像机的抖动及采样噪声等因素导致现有方法在可见光图像下对无人机等小目标检测准确率低。针对上述问题,提出了一种基于随机森林的无人机检测方法。该方法采集可见光下的图像序列,使用混合高斯模型和聚类检测算法检测图像中的运动小目标,继而通过随机森林算法融合目标的多种特征进行目标的判别,最终得到检测目标。实验结果表明,该方法可有效地检测出无人机运动小目标并大幅提高检测的精确率。     

Autonomous Swarm of Low-Cost Commercial Unmanned Aerial Vehicles for Surveillance

Programming and Computer Software -     

A Flocking-Based on Demand Routing Protocol for Unmanned Aerial Vehicles

研究人员的某社区显示出到自治雄蜂或 UAV (无人的天线车辆) 的兴趣与无线通讯网络的来临增加了。这些网络允许 UAV 以一种特定的方式更高效地合作以便在特定的环境完成特定的任务。到那么,当保持经由收音机连接在它的组与另外的节点连接了时,每只雄蜂独立地遨游。这个连接能故意被维持一会儿抑制雄蜂的活动性。这将对涉及在来源和一个目的地之间的给定的传播的一条给定的路径的雄蜂合适。这限制能在传播过程的结束被移开,每只担心的雄蜂的活动性从其它变得再独立。在这个工作,我们建议为 UAV 的一个基于成群的路由协议叫了 BR-AODV。协议为当数据正在被播送时,维持的连接和线路为雷纳兹机制的按需的线路计算,和 Boids 利用一个众所周知的特定的路由协议。而且,自动扎根的基础车站发现机制为为即时应用程序的上下文需要的一个积极雄蜂和地面网络协会被介绍了。BR-AODV 的表演被评估,与古典 AODV 路由的相比,协议和结果证明 BR-AODV 以延期,产量和包损失超过 AODV。     

基于PH曲线的无人机路径规划算法

研究了无人机可飞行路径规划问题,传统路径规划算法不能生成满足无人机运动学约束的可飞行路径.为解决上述问题,提出了将曲率连续的Pythagorean Hodograph (PH)曲线(又称勾股速端曲线)直接用于无人机路径规划的方法,利用PH曲线曲率连续、曲线平滑以及有理特性,在已知威胁的环境中可直接得到满足最大曲率约束且曲率连续的无人机可飞行路径.采用遗传模拟退火算法搜索避开环境中威胁体,并满足约束条件的最优PH路径,使仿真过程结合遗传算法和模拟退火算法的优点,取长补短,提高搜索效率,加强全局搜索能力.仿真结果表明,遗传模拟退火算法能在较少的进化代数下得到最优路径解,且所得的满足约束条件的PH路径曲率连续,为无人机飞行控制提供了依据.     

A collision-free formation reconfiguration control approach for Unmanned Aerial Vehicles

Formation flying of Unmanned Aerial Vehicles (UAVs) has gained a lot of interest due to its many potential advantages. Flying in formation allows wider sensing coverage area and in effect, this leads to improved surveillance and enhanced situational awareness. Also flying in formation eases coordination and data fusion. This paper presents the control architecture for fixed-wing UAV reconfiguration control using a novel combination of known techniques. The current premise is for the UAVs to assume their final target states within a specified time interval while avoiding collisions with one another or with an obstacle during the process. Some simulations are performed to assess the performance of the reconfiguration control scheme. The effects of the control parameters on the reconfiguration trajectories are also examined.