Experiments in unmanned aerial vehicle/unmanned ground vehicle radiation search

This paper discusses the results of a field experiment conducted at Savannah River National Laboratory to test the performance of several algorithms for the localization of radioactive materials. In this multirobot system, both an unmanned aerial vehicle, a custom hexacopter, and an unmanned ground vehicle (UGV), the ClearPath Jackal, equipped with γ‐ray spectrometers, were used to collect data from two radioactive source configurations. Both the Fourier scattering transform and the Laplacian eigenmap algorithms for source detection were tested on the collected data sets. These algorithms transform raw spectral measurements into alternate spaces to allow clustering to detect trends within the data which indicate the presence of radioactive sources. This study also presents a point source model and accompanying information‐theoretic active exploration algorithm. Field testing validated the ability of this model to fuse aerial and ground collected radiation measurements, and the exploration algorithm’s ability to select informative actions to reduce model uncertainty, allowing the UGV to locate radioactive material online.     

Flying on point clouds: Online trajectory generation and autonomous navigation for quadrotors in cluttered environments

Micro aerial vehicles (MAVs), especially quadrotors, have been widely used in field applications, such as disaster response, field surveillance, and search‐and‐rescue. For accomplishing such missions in challenging environments, the capability of navigating with full autonomy while avoiding unexpected obstacles is the most crucial requirement. In this paper, we present a framework for online generating safe and dynamically feasible trajectories directly on the point cloud, which is the lowest‐level representation of range measurements and is applicable to different sensor types. We develop a quadrotor platform equipped with a three‐dimensional (3D) light detection and ranging (LiDAR) and an inertial measurement unit (IMU) for simultaneously estimating states of the vehicle and building point cloud maps of the environment. Based on the incrementally registered point clouds, we online generate and refine a flight corridor, which represents the free space that the trajectory of the quadrotor should lie in. We represent the trajectory as piecewise Bézier curves by using the Bernstein polynomial basis and formulate the trajectory generation problem as a convex program. By using Bézier curves, we can constrain the position and kinodynamics of the trajectory entirely within the flight corridor and given physical limits. The proposed approach is implemented to run onboard in real‐time and is integrated into an autonomous quadrotor platform. We demonstrate fully autonomous quadrotor flights in unknown, complex environments to validate the proposed method.     

基于SoC的无人机航测视频信息叠加设计与实现

采用无人机航测平台标校光学助降系统光学下滑道指示角度时,需要确定无人机航拍穿越光学下滑道时刻航测相机镜头成像中心位置。首先需要获取无人机标校平台航测相机视频帧成像瞬时的精确时间,然后根据成像时刻GPS天线相位中心的坐标,结合无人机平台的姿态信息通过坐标转换得到拍照时刻航测相机镜头几何中心的坐标。本文设计并实现了一种基于SoC的无人机航测视频信息叠加方法,充分利用SoC内的嵌入式ARM核和媒体处理平台资源,ARM核心处理器的主要功能是完成与外设的通信及控制,专用的媒体处理平台完成音视频解码,采用SoC专用解码芯片的方案具有开发灵活和集成度高等优点。     

基于李雅普诺夫指数的四旋翼无人飞行器结构参数优化稳定性分析

为了提高四旋翼无人飞行器的运动稳定性,通过改变机械结构参数,研究了变结构参数对系统稳定性的影响.本文以四旋翼无人飞行器的偏航阶段为对象,根据基于动力学模型的李雅普诺夫指数计算过程,得到了影响系统稳定性的主要机械结构参数,并采用李雅普诺夫指数法建立其主要机械结构参数与系统运动稳定性之间的量化关系,以期指导系统的机械结构设计及控制系统优化,提高系统的可靠性和稳定性,最后,通过实验验证理论仿真分析过程中的结论.该方法与李雅普诺夫的第二法相比,其主要优势是计算指数方法的可构建性,并可以使复杂非线性系统的稳定性分析成为可能.     

退火递归神经网络极值搜索算法及其在无人机紧密编队飞行控制中的应用

针对无人机紧密编队飞行问题,以气动干扰引起的僚机俯仰角υω作为极值搜索变量,利用退火递归神经网络极值搜索算法,使僚机干扰俯仰角υω收敛至其极值,从而解决了无人机紧密编队飞行中僚机所需动力最小化的问题.将退火递归神经网络与极值搜索算法相结合,消除了传统极值搜索算法中控制量的来回切换问题和输出"颤动"现象,改善了系统的动态性能,同时简化了系统的稳定性分析.通过对无人机紧密匕行编队的仿真,验证了该算法的有效性.     

改进的航拍图像序列背景运动补偿技术

针对航拍图像序列提出用于提取图像特征点的最小亮度变化算法。利用特征点进行运动矢量估计,使用自适应十字模板搜索算法找到各个特征点的匹配点,结合随机样本一致算法估计背景运动模型的仿射变换参数,实现一种改进的背景运动补偿技术。该技术保障了航拍图像中运动目标检测的有效性。     

无人机的自动着陆控制

无人机的自动着陆控制是无人机控制中的一个难点,文中给出了一种无人机自动着陆的控制方法,并在某型无人机上得到了应用.基于无人机真实的风洞试验数据,利用MATLAB建立了无人机自动着陆的线性化模型和非线性模型,设计了无人机自动着陆导引控制方案及控制律,并在实际飞行试验中得到了验证,最终的飞行试验结果表明了所设计的自动着陆控制律能满足自动着陆的要求,并具有很好的静态和动态特性.     

Antenna array synthesis with clusters of unmanned aerial vehicles

In this paper, we investigate the use of formations of Unmanned Aerial Vehicles (UAVs) as phased antenna arrays. This will help to improve communications with clusters of small unmanned aerial vehicles which are currently constrained by on-board power limitations. The problem of maximizing the power output from the array in the direction of the receiver is posed as an optimization problem which happens to be non-convex; a relaxation of this problem is then solved as a computationally tractable (convex) Second-Order Conic Program (SOCP). The performance obtained by the simplified approach is then tested against rigorous numerical bounds obtained using Semidefinite Programming (SDP) duality theory; these bounds are of independent interest in antenna theory. In order to maintain the objective value close to the optimal when the vehicles deviate from their positions (due to wind gusts, for example), a simple linear control law is proposed. Simulation results are given to show the effectiveness of the proposed approaches.     

POS辅助航空摄影测量技术及应用

机载POS系统利用惯性导航技术与GPS等技术的特征优势,能够准确获取航空摄影相机曝光时刻的姿态参数与位置参数等外方位元素,实现了在省略空中三角测量加密与地面控制点等工序基础上的定向测量,生产效率与作业成本等随之优化。文章主要对POS辅助航空摄影测量技术及应用进行阐述,分析系统误差来源与应用需求等,希望对航空摄影测量技术的优化发展起到积极的参照作用。     

Autonomous aerial cinematography in unstructured environments with learned artistic decision‐making

Aerial cinematography is revolutionizing industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely piloting a drone while filming a moving target in the presence of obstacles is immensely taxing, often requiring multiple expert human operators. Hence, there is a demand for an autonomous cinematographer that can reason about both geometry and scene context in real‐time. Existing approaches do not address all aspects of this problem; they either require high‐precision motion‐capture systems or global positioning system tags to localize targets, rely on prior maps of the environment, plan for short time horizons, or only follow fixed artistic guidelines specified before the flight. In this study, we address the problem in its entirety and propose a complete system for real‐time aerial cinematography that for the first time combines: (a) vision‐based target estimation; (b) 3D signed‐distance mapping for occlusion estimation; (c) efficient trajectory optimization for long time‐horizon camera motion; and (d) learning‐based artistic shot selection. We extensively evaluate our system both in simulation and in field experiments by filming dynamic targets moving through unstructured environments. Our results indicate that our system can operate reliably in the real world without restrictive assumptions. We also provide in‐depth analysis and discussions for each module, with the hope that our design tradeoffs can generalize to other related applications. Videos of the complete system can be found at https://youtu.be/ookhHnqmlaU .