双目视觉模型移动目标跟踪系统

为了使机器人能够对目标进行自主智能的跟踪,基于仿生学原理建立双目视觉模型,设计出四自由度的仿人眼颈跟踪系统,通过上位机与下位机的配合以及2台摄像机与4台直流电机,实现双目视觉移动目标跟踪系统.该系统通过双目摄像机对目标进行图像采集,由PC机对目标进行特征提取以及一系列处理后计算出目标的移动范围,将目标移动数据传输至下位机后通过PID控制算法平滑控制直流电机的运转方向与运转速度,从而对移动目标实现良好的跟踪性能.     

小型无人机地面目标跟踪系统机载云台自适应跟踪控制

本文针对小型无人机地面目标跟踪系统,提出了一种机载云台自适应跟踪控制算法.该算法在摄像机外参数未知的情况下,利用图像信息和机载传感器得到的无人机状态进行反馈,最终实现了对云台摄像机姿态的控制,使得无人机在跟踪目标飞行过程中,地面目标可以始终保持在摄像机的图像中心.为此,论文首先通过分析无人机、目标和摄像机三者的相对位姿关系,建立了目标点在图像平面的运动学模型.在此基础上,基于李雅普诺夫稳定性理论设计了自适应控制算法.理论分析与仿真结果表明本文所设计的摄像机姿态控制器在摄像机外参数未知的情况下,可以使被跟踪目标始终保持在图像中心.     

视觉跟踪模糊自调节PI控制算法

视觉跟踪系统是一个非线性时变系统,传统控制方法往往难以胜任。因此,本文提出一种新的视觉跟踪控制算法——模糊自调节PI控制算法,以跟踪三维平动刚体目标。与传统基于图像的视觉伺服控制算法相比,该控制算法有两个优点：①以二值目标图像的矩特征为图像特征,因而无需图像特征匹配;②模糊调节器根据闲环系统响应的不同阶段,在线调节PI参数,从而改善了闭环系统的整体性能。仿真结果表明,本文提出的模糊自调节PI控制算法不仅简单快速,且具有较高控制精度。     

基于人类视觉智能和粒子滤波的鲁棒目标跟踪算法

与计算机视觉目标跟踪算法遇到的各种困难和瓶颈不同的是,鲁棒视觉目标跟踪仅仅是人类视觉系统的一项基本功能.为此,基于自顶向下的视觉注意机制,构建了一种相应的计算模型,提出一种利用检测视觉注意力焦点区域的方法来模拟人类视觉系统的全局搜索过程,并用粒子滤波跟踪来模拟人类视觉系统的局部跟踪过程的目标跟踪算法.多组对比实验结果表明了所提出的算法在视觉目标跟踪中的优越性.     

基于Adept机器人的视觉伺服控制系统

机器人视觉伺服控制在理论和应用等方面还有许多问题需要研究,例如特征选择、系统标定和伺服控制算法等.针对Adept机器人,提出了一种简单快速的不需要精确标定摄像机内外部参数的摄像机标定方法,完成了从被观测物体表面所在的视觉平面坐标系到机器人基坐标系的坐标变换.使用图像的全局特征,即图像矩特征进行伺服跟踪;利用所推导的图像雅可比矩阵,设计了由图像反馈与目标运动自适应补偿组成的视觉伺服控制器.将算法对静态目标的定位实验进行了验证,然后又将其应用到移动目标的跟踪上,通过调节和优选控制参数,实现了稳定的伺服跟踪和抓取.实验结果表明采用图像矩作为图像特征能够避免复杂的特征匹配过程,并且能够获得较好的跟踪精度.     

视觉量子目标跟踪方法

为了解决变结构目标跟踪过程中目标失跟率较高的问题,提出了一种基于视觉量子（vision quantum,简称VQ）的目标跟踪方法.该方法首先在图像内自上而下地辐射视觉量子采集灰度信息,统计量子内部概率密度较大的灰度级和分布区域;然后计算视觉量子的量子频率,归一化量子频率系数,滤除系统噪音和杂波干扰,利用频率阶跃不变性移动视觉量子至平衡状态,将达到量子平衡状态的视觉量子组成量子簇;最后,以该量子簇作为候选目标信息,采用极大似然估计预测运动目标状态,以预测结果作为下一帧图像中视觉量子移动的参考值,并进一步验证移动后的视觉量子是否达到量子平衡状态,以确保目标跟踪有效性.该方法抓住了变结构运动目标前景与背景交界处具有量子频率阶跃不变性的特点,继而将阶跃不变特征采用具有独立性和约束性的视觉量子进行描述,可以有效地消除形状变化、尺度变化等变结构因素对运动目标跟踪的影响,失跟率较低.同时,由于视觉量子数据量较小,计算复杂度较低,其跟踪实时性较高.大量实验测试结果表明,该方法对变结构目标跟踪具有很好的适应性、实时性和鲁棒性.     

基于CamShift的跟踪系统的设计

介绍了一套目标跟踪系统.上位机采用基于CamShih算法,利用目标的颜色直方图模型得到每帧图像的颜色投影图,并根据上一帧得到的结果自适应调整搜索窗口的位置和大小,得到当前图像中目标的尺寸和中心位置,然后计算中心位置距离图像中心的偏移量;下位机是利用DSP设计的一套运动控制系统,由自主研发的DSP型伺服放大器、直流伺服电机和云台、伺服驱动单元组成,能够根据运动偏差控制云台跟踪运动目标的移动.实验证明:本系统可以较好地实现简单背景下物体的跟踪.     

基于神经网络的机器人视觉伺服控制

视觉伺服可以应用于机器人初始定位自动导引、自动避障、轨线跟踪和运动目标跟踪等控制系统中。传统的视觉伺服系统在运行时包括工作空间定位和动力学逆运算两个过程,需要实时计算视觉雅可比矩阵和机器人逆雅可比矩阵,计算量大,系统结构复杂。本文分析了基于图像的机器人视觉伺服的基本原理,使用BP神经网络来确定达到指定位姿所需要的关节角度,将视觉信息直接融入伺服过程,在保证伺服精度的情况下大大简化了控制算法。文中针对Puma560工业机器人的模型进行了仿真实验,结果验证了该方法的有效性。     

移动机器人崎岖地面静态目标瞄准跟踪系统

目标跟踪对自主移动机器人是一项重要的任务,但往往系统成本高、体积大、耗能高,跟踪控制算法复杂,不适合于小型移动机器人的应用场合。本文结合一种低成本的嵌入式颜色视觉系统,提出了一种简单实用的目标跟踪方法,基于图像的HSV阈值分割,经过颜色分割识别物体,通过实时对比目标中心在图像平面的坐标与图像平面中心的偏差,产生相应控制命令驱动云台电机保持目标中心坐标始终处在图像平面中心矩形框内,从而实现移动机器人对目标持续有效的瞄准跟踪。通过移动机器人在不同崎岖地面运动对所提出目标跟踪控制方法进行了验证。     

基于有限时间扰动观测器的无人水面艇精确航迹跟踪控制

针对复杂航行环境下的无人水面艇系统,提出一种基于有限时间扰动观测器的无人水面艇精确航迹跟踪控制策略.该控制方法具有以下显著特点:能够精确补偿未知海洋干扰,可实现精确跟踪控制;相比传统的渐近收敛控制算法,有限时间稳定性确保跟踪控制系统具有更快的收敛速度和更强的扰动抑制能力;能够同时确保扰动观测误差和航迹跟踪误差在有限时间内精确收敛到零.仿真结果验证了所提出控制方法的有效性和优越性.     

Robust face tracking control of a mobile robot using self‐tuning Kalman filter and echo state network

This paper presents a novel design of face tracking algorithm and visual state estimation for a mobile robot face tracking interaction control system. The advantage of this design is that it can track a user's face under several external uncertainties and estimate the system state without the knowledge about target's 3D motion‐model information. This feature is helpful for the development of a real‐time visual tracking control system. In order to overcome the change in skin color due to light variation, a real‐time face tracking algorithm is proposed based on an adaptive skin color search method. Moreover, in order to increase the robustness against colored observation noise, a new visual state estimator is designed by combining a Kalman filter with an echo state network‐based self‐tuning algorithm. The performance of this estimator design has been evaluated using computer simulation. Several experiments on a mobile robot validate the proposed control system. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

恒定光照下基于LFPL的APTZ调节

为提高光照恒定情况下视觉系统中PTZ调节的主动性和稳定性,提出恒定光照下基于LFPL的APTZ调节方法。采用基于局部粒子滤波的目标预定位方法,对运动目标实现自动估计的标定,提高系统调节的主动性,解决非线性跟踪问题。动态选取光照不变特征滤波粒子克服了光照变化和噪声等因素对目标预定位方法的影响,增强视觉系统的鲁棒性。对水平角和抑角采用Fuzzy控制方法,提高视觉跟踪系统的稳定性。实验结果表明,该方法是正确有效的,使用该系统对变速运动目标的长距离跟踪结果较传统方法更稳定,在光照变化和噪声条件下的运动目标跟踪实验也取得较好的结果。     

Robust visual tracking control system of a mobile robot based on a dual-Jacobian visual interaction model

This paper presents a novel design of a robust visual tracking control system, which consists of a visual tracking controller and a visual state estimator. This system facilitates human–robot interaction of a unicycle-modeled mobile robot equipped with a tilt camera. Based on a novel dual-Jacobian visual interaction model, a robust visual tracking controller is proposed to track a dynamic moving target. The proposed controller not only possesses some degree of robustness against the system model uncertainties, but also tracks the target without its 3D velocity information. The visual state estimator aims to estimate the optimal system state and target image velocity, which is used by the visual tracking controller. To achieve this, a self-tuning Kalman filter is proposed to estimate interesting parameters and to overcome the temporary occlusion problem. Furthermore, because the proposed method is fully working in the image space, the computational complexity and the sensor/camera modeling errors can be reduced. Experimental results validate the effectiveness of the proposed method, in terms of tracking performance, system convergence, and robustness.     

Real-Time Dynamic Visual Tracking Using PSD Sensors and Extended Trapezoidal Motion Planning

A real-time visual servo tracking system for an industrial robot has been implemented using PSD (Position Sensitive Detector) cameras, neural networks, and an extended trapezoidal motion planning method. PSD and directly transduces the light's projected position on its sensor plane into an analog current and lends itself to fast real-time tracking. A neural network, after proper training, transforms the PSD sensor reading into a 3D position of the target, which is then input to an extended trapezoidal motion planning algorithm. This algorithm implements a continuous motion update strategy in response to an ever-changing sensor information from the moving target, while greatly reducing the tracking delay. This planning method is found to be very useful for sensor-based control such as moving target tracking or weld-seam tracking in which the robot needs to change its motion in real time in response to incoming sensor information. Further, for real-time usage of the neural net, a new architecture called LANN (Locally Activated Neural Network) has been developed based on the concept of CMAC input partitioning and local learning. Experimental evidence shows that an industrial robot can smoothly track a moving target of unknown motion with speeds of up to 1 m/s and with oscillation frequency up to 5 Hz.     

基于全景视觉的监控系统设计

设计了一个基于全景视觉的多摄像机监控网络。全景相机视野广,可以实现大范围的目标检测与跟踪。云台摄像机视角具有一定的自由度,可以捕捉目标的高分辨率图像。将全景相机与云台相机相互配合,通过多传感器的数据融合,分层次的跟踪算法及多相机调度算法,实现了大范围的多个运动目标的检测与跟踪,并能捕获目标的清晰图像。实验验证了该系统的有效性和合理性。     

动态云台摄像机无人机检测与跟踪算法

为应对小型无人机的黑飞、滥飞对个人隐私、公共安全造成的威胁,本文采用高清云台摄像机定点巡航的方式对近地动态复杂背景中的无人机进行检测与跟踪,并提出了一种适用于动态云台摄像机的闭环无人机检测与跟踪算法,包含检测与跟踪两种模式。在检测模式下,本文设计了一种基于运动背景补偿的运动目标检测算法来提取分类候选区域,然后利用基于神经网络结构搜索得到的轻量级卷积神经网络对候选区域进行分类识别,可在不缩小高清视频图像的条件下实现无人机检测;在跟踪模式下,本文提出了一种结合卡尔曼滤波的局部搜索区域重定位策略改进了核相关滤波跟踪算法,使之在高清云台伺服追踪过程中仍能对目标进行快速稳定的跟踪;为将检测模式与跟踪模式结合在闭环框架中,本文还提出了一种基于检测概率和跟踪响应图状态的自适应检测与跟踪切换机制。实验表明,本文算法可应用于定点巡航状态的高清云台摄像机,实现近地复杂动态背景中无人机的实时准确检测、识别与快速跟踪。     

六足机器人动态目标检测与跟踪系统研究

动态目标检测与目标跟踪是图像领域的热点研究问题,为研究其在移动机器人领域的应用价值,设计了六足机器人动态目标检测与跟踪系统。针对非刚体运动目标容易被检测为多个分散区域的问题提出区域合并算法,并通过对称匹配、自适应外点滤除对运动背景进行精确补偿,最终基于背景补偿法实现对运动目标的精确检测。研究了基于KCF（Kernel Correlation Filter）的目标跟踪算法在六足机器人平台上的应用,设计了自适应跟踪算法实现六足机器人对运动目标的角度跟踪。将运动目标检测及跟踪算法应用于六足机器人系统。实验表明,在六足机器人移动过程中,系统可对运动目标进行精确检测与跟踪。     

Navigation system including associative memory

A new adaptive linear robot control system for a robot work cell that can visually track and intercept stationary and moving objects undergoing arbitrary motion anywhere along its predicted trajectory within the robot's workspace is presented in this paper. The proposed system was designed by integrating a stationary monocular CCD camera with off-the-shelf frame grabber and an industrial robot operation into a single application on the MATLAB platform. A combination of the model based object recognition technique and a learning vector quantization network is used for classifying stationary objects without overlapping. The optical flow technique and the MADALINE network are used for determining the target trajectory and generating the predicted robot trajectory based on visual servoing, respectively. The necessity of determining a model of the robot, camera, all the stationary and moving objects, and environment is eliminated. The location and image features of these objects need not be preprogrammed, marked and known before, and any change in a task is possible without changing the robot program. After the learning process on the robot, it is shown that the KUKA robot is capable of tracking and intercepting both stationary and moving objects at an optimal rendezvous point on the conveyor accurately in real-time.     

Finite-time stability of cascaded time-varying systems

The uniform global finite-time stability is discussed for a cascaded time-varying system consisting of two uniformly finite-time stable subsystems. It is shown that a forward completeness condition is enough to ensure the uniform global finite-time stability of the system. For ease of reference, a particular result with a growth rate condition is also deduced. These stability results are applied to the tracking control problem of a non-holonomic wheeled mobile robot in kinematic model. Two tracking control laws are developed respectively for two different cases of the desired rotate velocity. Both control laws are continuous and can control the mobile robot to track the desired trajectory in finite time. Simulation results are provided to show the effectiveness of the method.