基于双目视觉的无人飞行器目标跟踪与定位

针对小型无人飞行器跟踪目标的问题,提出了一种基于双目视觉和Camshift算法的无人飞行器目标跟踪以及定位算法。双目相机得到的左右图像通过Camshift算法处理可得到目标中心特征点,对目标中心特征点进行三维重建,得到机体坐标系下无人飞行器与目标间的相对位置和偏航角,应用卡尔曼滤波算法对测量值进行了优化,将所得估计值作为飞行控制系统的反馈输入值,实现了无人飞行器自主跟踪飞行。结果表明所提算法误差较小,具有较高的稳定性与精确性。     

基于Tiny-YOLOV3的无人机地面目标跟踪算法设计

为了提高四旋翼无人机对地面目标跟踪的稳定性和跟踪精度,提出了一种结合Tiny-YOLOV3和卡尔曼滤波的跟踪算法;首先分析了Tiny-YOLOV3的原理和网络结构,并基于Tiny-YOLOV3的目标检测结果,结合无人机状态和目标的几何关系建立了目标跟踪系统的数学模型;接着对目标相对运动关系进行分析,建立目标的运动学模型,考虑到目标检测结果受干扰影响较大,应用卡尔曼滤波器实现对目标轨迹的滤波和预测,进而提升目标跟踪的精度;最后根据经过卡尔曼滤波后的目标轨迹信息设计无人机控制律,在轨迹控制的同时引入对无人机偏航角的控制,从而实现无人机对目标的稳定跟踪;仿真结果表明无人机对目标的位置跟踪精度在0.5 m以内,速度跟踪误差在0.2 m/s以内,偏航角跟踪误差在3°以内,跟踪效果良好,从而论证了所提算法的有效性。     

Development of Unmanned Aerial Vehicle (UAV) system with waypoint tracking and vision-based reconnaissance

This paper introduces the developed UAV system for low cost operation and an EOS (Electro Optical System) laboratory. This paper highlights an autonomous navigation system based on microcontrollers that can track a target using images, take three-dimensional measurements of the target, and acquire high quality images. The hardware system and an algorithm for the EOS verify the performance of the image tracking system and 3-D measurement of the target’s position. 3-D position estimations for the target are solved using the mathematical relationship between the UAV and target. Although an on-board EOS can make errors in 3-D measurement, the proposed approach shows improved accuracy and confidence for 3-D target tracking using a postprocessing method.     

Adversarial Ground Target Tracking Using UAVs with Input Constraints

This paper deals with the problem of adversarial ground target tracking using Unmanned Aerial Vehicles (UAVs) subject to input constraints. For adversarial ground target tracking, tracking performance and UAV safety are two important considerations during tracking controller design. In this paper, a bang-bang heading rate controller is proposed to achieve circular tracking around the target. Exposure avoidance of the UAV to the target and minimizing the exposure time are studied respectively in terms of the initial state of the UAV. The performance of the proposed controller in both cases is also analyzed. Simulation results demonstrate the effectiveness of the proposed approach.     

Chance-constrained target tracking using sensors with bounded fan-shaped sensing regions

We present a robust target tracking algorithm for a mobile robot. It is assumed that a mobile robot carries a sensor with a fan-shaped field of view and finite sensing range. The goal of the proposed tracking algorithm is to minimize the probability of losing a target. If the distribution of the next position of a moving target is available as a Gaussian distribution from a motion prediction algorithm, the proposed algorithm can guarantee the tracking success probability. In addition, the proposed method minimizes the moving distance of the mobile robot based on the chosen bound on the tracking success probability. While the considered problem is a non-convex optimization problem, we derive a closed-form solution when the heading is fixed and develop a real-time algorithm for solving the considered target tracking problem. We also present a robust target tracking algorithm for aerial robots in 3D. The performance of the proposed method is evaluated extensively in simulation. The proposed algorithm has been successful applied in field experiments using Pioneer mobile robot with a Microsoft Kinect sensor for following a pedestrian.     

Distributed Cooperative Path Planning for Tracking Ground Moving Target by Multiple Fixed-wing UAVs via DMPC-GVD in Urban Environment

In this paper, a real-time distributed path planning method is developed for cooperatively tracking ground moving target in urban by multiple fixed-wing unmanned aerial vehicles (UAVs). For reasons of changeable movement of target, the commanded speed and turning rate of each UAV are both taken as control input variables. In urban environment, buildings may occlude the line of sight of on-board sensor. Hence the target coverage degree is proposed as objective function instead of distance. To save energy of UAV system as much as possible, the control input cost and sensor energy consumption are also taken as objectives. For preemptive priority requirement, the objective functions are fuzzified and the satisfactory degree order is designed to model priority. To guarantee the feasibility of solution, the varying domain is introduced to replace the strict order constraint. On this basis, generalized varying domain (GVD) method is developed to balance optimization and priority. In terms of the maneuverability of UAVs, the diverse constraints are considered, including real speed and turning rate, control input saturation, collision avoidance between UAVs, and obstacle avoidance between UAV and buildings. Consequently, distributed model predictive control (DMPC) strategy is designed to calculate the optimal path of each UAV, where the state information in finite period of UAV is transferred to the adjacent ones. The simulations show the effectiveness of proposed method by comparing with hierarchical optimization (HO).

     

无线传感网络中基于误差椭圆的自适应节点 选择目标跟踪算法

追踪精度与传感器节点能耗是无线传感网络WSN(Wireless Sensor Network)中主要考虑的两个性能指标,现有的许多目标追踪算法在提高追踪精度、降低传感器节点能耗的同时缺乏对传感器节点位置与数目的考虑.因此,提出一种基于误差椭圆的自适应节点选择目标追踪算法,以误差椭圆为基准计算目标最可能出现的区域,然后根据误差判决调整区域内所需激活的传感器节点数量,完成对目标的跟踪.仿真结果表明,该算法可以在保证追踪精度的同时有效降低激活传感器节点数量.     

Cooperative Tracking a Moving Target Using Multiple Fixed-wing UAVs

A cooperative tracking scheme is presented in this paper for multiple fixed-wing unmanned aerial vehicles (UAVs) to track an uncooperative, moving target. It is comprised of a target loitering algorithm and a formation flight algorithm. The loitering algorithm enables a constant speed UAV to circle around a moving target, whose speed is allowed to vary up to the UAV’s speed. The formation algorithm enables cooperative tracking using multiple UAVs by keeping them flying in a circular formation with equal inter-vehicle angular separation. Under this formation algorithm, the formation center can be controlled independently to perform target loitering, and the admissible range of the target’s speed would not be affected for given UAVs. The performance of the proposed tracking system is verified in numerical simulations.     

TDOA/FDOA based target tracking with imperfect position and velocity data of distributed moving sensors

This paper addresses the target tracking problem using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measured by moving sensor network whose position and velocity are noise contaminated. It is a known fact that the existing approaches to this problem still have two unsolved technical issues; the unsatisfactory convergence behavior of the tracking filter mainly caused by severe nonlinearity of the problem itself and the tracking performance degradation due to the sensor position and velocity errors. In order to resolve these matters radically, the given target tracking problem is formulated as the robust state estimation problem of the linear system with stochastic uncertainties in its measurement matrix and solved by using the robust Kalman filter theory. The proposed scheme enables us to overcome the inherent limitations of the conventional nonlinear filters for its linear filter structure. It can also prevent the performance degradation due to imperfect sensor position and velocity information. Through the simulations, the effectiveness and reliable target tracking performance of the proposed method are demonstrated.     

紧耦合多传感器混合跟踪算法

在增强现实应用中实现对运动目标的准确跟踪是一个具有挑战性的任务。基于混合跟踪通过对多传感器信息的融合通常比单一传感器跟踪算法更为优越的特性,提出了一种新的紧耦合混合跟踪算法实现视觉与惯性传感器信息的实时融合。该算法基于多频率的测量数据同步,通过强跟踪滤波器引入时变衰减因子自适应调整滤波预测误差协方差,实现对运动目标位置数据的准确估计。通过标示物被遮挡状态下的跟踪实验结果表明,该方法能有效改善基于扩展卡尔曼滤波器的混合跟踪算法对运动目标位置信息预测估计的准确性,提高跟踪快速移动目标的稳定性,适用于大范围移动条件下的增强现实系统。     

基于扩张状态观测器的四旋翼无人机快速非奇异终端滑模轨迹跟踪控制

针对受多源干扰影响的四旋翼无人机系统的轨迹跟踪控制问题进行研究,充分考虑位置回路和姿态回路动态特性,提出一种全回路复合快速非奇异终端滑模轨迹跟踪控制方案.首先,通过变换将轨迹跟踪问题转化为位置回路和姿态回路的指令跟踪控制问题;然后,将各通道之间的耦合以及多源干扰影响视作集总干扰,并基于扩张状态观测器对其进行估计;接着,基于干扰估计信息和快速非奇异终端滑模控制算法,分别在位置回路和姿态回路构造复合快速非奇异终端滑模控制器;最后,基于位置回路和姿态回路虚拟控制量解得无人机真实控制量旋翼转速.仿真结果表明,所提出控制方案显著提升了四旋翼无人机轨迹跟踪的响应速度和抗干扰性能.     

Tracking a Ground Moving Target with a Quadrotor Using Switching Control

An unmanned aerial vehicle (UAV) stabilization strategy based on computer vision and switching controllers is proposed. The main goal of this system is to perform tracking of a moving target on ground. The architecture implemented consists of a quadrotor equipped with an embedded camera which provides real-time video to a computer vision algorithm where images are processed. A vision-based estimator is proposed, which makes use of 2-dimensional images to compute the relative 3-dimensional position and translational velocity of the UAV with respect to the target. The proposed estimator provides the required states measurements to a micro-controller for stabilizing the vehicle during flight. The control strategy consists of switching controllers, which allows making decisions when the target is lost temporarily or when it is out of the camera’s field of view. Real time experiments are presented to demonstrate the performance of the target-tracking system proposed.     

基于机动目标模型的无人机视场跟踪仿真研究

为了跟踪地面机动目标,对无人机的视场进行了研究;分析了视场中目标位置的解算以及飞机、目标与视场的运动关系,并建立了三者的相对位置关系模型,推导出视场对目标进行跟踪的控制量;简要介绍了机动目标的"当前"统计模型及其自适应滤波算法,在该算法对目标跟踪的基础上,可以更准确地计算出视场校正控制量;对视场跟踪进行了仿真验证,利用飞行航迹和目标轨迹模拟,无人机盘旋跟踪地面运动目标,实时计算出偏差角控制量并调整视场;仿真结果表明,视场对机动目标的跟踪效果良好,算法稳定可靠.     

无人机低空滑翔抗攻击突防控制律优化设计

为了提高无人机的低空滑翔抗攻击突防和控制能力,提出一种基于快速模型预测的无人机低空滑翔抗攻击突防控制技术。采用融合传感识别技术进行无人机的姿态和位置参数信息采集,分析无人机的低空滑翔控制的物理环境参数模型,构建无人机飞行轨迹地图模型,使用标准卡尔曼滤波器进行无人机低空滑翔抗攻击突防控制信息的融合处理,根据信息融合结果进行控制指令设计。采用动态基元轨迹跟踪方法,得到无人机低空突防控制的滑模面,在有限Morrey空间内采用串联弹性驱动控制方法求得在控制约束参量分布模型的最优解。根据无人机低空突防段的初始位姿参数进行快速模型预测和飞行轨迹跟踪,实现低空滑翔抗攻击突防控制。仿真结果表明,采用该方法进行无人机低空滑翔抗攻击突防控制的精度较高,无人机的姿态参数的自适应调节性能较好。     

Indoor low-cost localization system for controlling aerial robots

This paper presents a low-cost localization system to guide an Unmanned Aerial Vehicle (UAV) in indoor flights, considering an environment with invariant texture and typical indoor illumination. The first contribution of the paper is the proposal of a system to estimate the position and orientation of the UAV, through a multi-sensor fusion scheme, dealing with data provided by a RGB-D sensor, an inertial measurement unit (IMU), an ultrasonic sensor and optical flow-based velocity estimates. A second contribution of the paper is the proposal of a high-level control system to guide the UAV in path-following tasks, involving two controllers: a kinematic one, responsible for generating reference velocities for the vehicle, and a PD one, responsible for tracking such reference velocities, thus characterizing a cascade controller. Experiments with such a localization and control systems, during which abrupt disturbances are applied, were carried out to check the effectiveness of the developed capture and control systems, whose results validate the proposed framework.     

传感器网络下机动目标动态协同跟踪算法

对传感器网络下的机动目标跟踪问题提出一种分布式传感器节点动态分簇、协同跟踪算法. 通过在线优化目标跟踪的性能函数和通讯代价, 自适应地选择节点并动态分簇, 通过多传感器节点的协同感知以及信息融合提高了跟踪精度. 由于问题的非线性和传感器节点的随机性, 本文基于粒子滤波器在线预测和估计目标状态的概率分布, 使用混合高斯粒子滤波器以及选择最短路径用于传感器节点之间的信息交换节约了通讯能量, 通过一种有效的粒子方法逼近目标状态的预测方差以实现传感器节点的最优选择. 仿真结果表明, 与 IDSQ 算法相比较, 本文提出的动态分簇算法实现了对机动目标的高精度跟踪.     

多标记室内小型无人机定位与姿态估计方法

随着无人机（Unmanned Aerial Vehicle,UAV）小型化、轻便化的发展,因其价格低廉,以及在娱乐和服务领域的广泛使用的特点,使得如何实现一个便捷且易实现的自主飞行跟踪系统成为关注点。由于无人机在室内GPS信号弱,使得跟踪与姿态获取成为进一步室内无人机自主控制的重点与难点。与动辄几十万美元搭建的无人机跟踪系统相比,采用低成本单目摄像机的无人机跟踪系统具有更高的科研价值和更广泛的应用前景。针对目前流行的基于增强现实（Augmented Reality,AR）技术的ArUco标记算法和颜色空间域标记算法,设计了一种多标记的无人机跟踪系统。在无人机目标跟踪过程中比较两种方法,验证了两种方法非接触式深度传感器无人机跟踪和姿态估计的效果,并比较了两种方法对空间亮度与空间颜色复杂度的鲁棒性,以及不同跟踪距离下视频中无人机检出率与跟踪精度。实验结果表明,基于深度摄像机获得的无人机位置和姿态数据,无人机可以进行自主的PID控制飞行,且AR标记在复杂环境下无人机的检出率、跟踪实时性、姿态估计精度以及鲁棒性都优于颜色标记,为之后室内无人机在非接触式传感的控制、路径规划、自主规避等进一步实验研究提供了无人机的位置和姿态数据。     

多无人机模糊多目标分布式地面目标协同追踪

针对城市环境中多约束条件下多无人机协同追踪地面目标问题,综合考虑具有不同重要性等级的多个优化目标,提出了一种基于分布式预测控制的模糊多目标航迹规划方法.首先,考虑城市环境中建筑物对无人机视线遮挡、无人机和传感器能量消耗等因素,分别采用目标覆盖度、控制输入代价和开关量形式传感器能耗等为目标函数,将多无人机协同追踪航迹规划转化为多目标优化问题;然后,基于分布式预测控制框架,利用每架无人机未来有限时域内的预测状态,构建多无人机之间的避碰约束,并结合最小转弯半径等约束,形成分布式协同航迹规划模型;最后,针对多个优化目标的不同重要性等级要求,利用模糊满意优化思想将目标模糊化,并根据更重要目标具有更重要满意度的原则,将优先等级表示为松弛满意度序,通过在线求解得到有限时域内每架无人机的局部航迹;与传统多目标加权算法仿真结果对比,验证了所提方法的有效性,充分说明了该方法能够获得同时满足目标优化和重要性等级要求的最优航迹.     

基于观测优化的双机协同控制与避障

为实现不确定环境下无人机对远程超视距目标的精确指示,考虑空中移动和静止障碍物,提出基于观测优化的双机协同控制与避障算法,从而增加测量信息,减小目标状态估计的不确定度.以费舍尔信息矩阵(FIM)表征所获取的目标信息,理论推导出三维空间中双机最优观测的指标函数,并设计无人机协同控制律,得到优化的无人机观测航迹,增强无人机协...     

基于超宽带定位的四旋翼目标跟踪技术研究

针对无人机室内地面目标跟踪问题,论文提出了一种基于超宽带室内定位技术的跟踪方法.首先建立了四旋翼和地面机动目标的动力学模型.然后,通过超宽带定位技术得到目标的量测信息.其次,利用扩展卡尔曼滤波估计目标的状态信息达到减少跟踪误差的目的,并且基于解算的信息提出了人工势场法作为四旋翼的路径规划算法.最后利用反步法控制策略解决...