基于彩色图像分割的机器人足球目标识别

研究了机器人足球视觉系统中基于彩色图像分割的目标识别的方法。为了适应光照条件的变化，采用分离出亮度信息的YUV颜色空间；将彩色图像分割分为离线的颜色分类和实时的分割、识别两个部分，并采用最大似然法完成颜色的自动分类，满足了机器人足球视觉系统实时、准确的要求。试验证明，在光照条件改变的情况下能够有效地进行目标识别。     

动态环境下四腿机器人图像处理的改进

视觉系统是咱主体机器人感知外界坏境信息的重要环节.颜色分割是视觉系统中图像处理的第一步.目前公认效果较好的方法采取的是颜色表查找法.本文以四腿机器人足球比赛为背景和研究平台,提出一种快速制作颜色表的方法,并在实时环境下运用了图像增强技术,改进了颜色分割的算法,提高了机器人视觉系统的鲁棒性,并应用于2006年的RoboCup中国机器人大赛中,得到了较好的效果.     

基于小波变换的足球机器人视觉系统的设计

文章介绍了足球机器人视觉系统的组成及工作原理,针对足球机器人比赛的实际要求,采用小波变换对图像进行去噪处理。根据足球机器人视觉系统是通过颜色信息进行目标定位且实时性要求高的特点,仅对小波系数进行处理,在准确性得到保证的前提下,可大大减少运算的数据量,实验结果表明该方法可以有效地提高视觉系统的实时性。     

基于颜色空间映射的自适应足球机器人视觉系统

为了解决足球机器人比赛中视觉信息检测问题,给出基于嵌入式多处理器体系结构的视觉系统框架,并研究了一种自适应阈值目标分割方法。该方法将视觉系统采集的图像从YCbCr颜色空间转换为一维颜色映射图像,并提出基于竞争学习的空间映射方法。同时使用小波变换方法来平滑颜色映射空间直方图和降低阈值搜索维数。然后使用基于映射图像直方图动态搜索阈值的方法来分割图像。最后,通过实验表明研究的视觉系统的有效性,能够实时准确地完成视觉处理任务。     

计算机应用研究

基于视觉的足球机器人系统融合了实时视觉信息处理、无线通讯、多机器人协作等多个领域的技术。提出了基于视觉的足球机器人系统的控制结构,设计并实现了足球机器人系统的视觉识别算法,以动作选择机构为核心建立了策略系统。     

足球机器人视觉系统光强自适应算法研究

研究足球机器人比赛问题,比赛环境的光强度变化严重影响足球机器人视觉系统的识别性能.为提高机器人视觉识别率,提出了一种基于HSI颜色空间模型的光强自适应算法.算法将比赛场地划分为若干区域,利用HSI颜色空间模型可以分离环境光强度信息的特点,在比赛中动态更新所有划分区域的HSI颜色空间,提高了机器人视觉系统对光强变化的自适应能力,实现了机器人对比赛场地信息的精确辨识.因此用算法优化机器人视觉识别系统进行仿真.结果表明,在实际比赛中,算法能够有效降低环境光强度变化,大大提高了对足球机器人视觉辨识性能.     

基于彩色图像分割的机器人的目标识别

以全自主型足球机器人的视觉系统为研究对象,介绍了一种基于彩色图像分割处理的目标识别方法.根据足球机器人系统中视觉系统的实际应用环境,分析对比了不同颜色空间的优缺点,采用HSV颜色空间作为视觉系统的工作空间,减弱了光照变化对视觉系统带来的影响.针对足球机器人系统中对视觉系统实时性的具体要求,提出了一种通过量化HSV颜色空间获得直方图阈值,再通过阈值进行彩色图像分割方法.这种方法大大减少了视觉系统的工作量,提高了系统的实时性.     

一种新的机器人三维视觉的设想

本文探讨了机器人视觉的固有矛盾,分析了机器人视觉方法的现状,得出:现行的视觉方法难于给出机器人操作必需的信息,难于兼顾实时性与通用性。基于此分析,构思了一种新的三维视觉系统,旨在解决机器人视觉的固有问题.     

基于双计算机的仿人机器人的视觉跟踪系统

运动目标的实时跟踪是机器人视觉的关键技术之一。设计了仿人机器人的视觉跟踪系统,系统采用双计算机,分别负责视觉信息的处理和运动单元的控制,两台计算机通过Memolink进行通讯。基于Windows的视觉信息处理子系统实现运动目标的分割,状态估计和预测。运动控制子系统采用RTlinux实时操作系统,利用PD控制器控制关节运动。实验验证了系统的稳定性和实时性。     

排爆机器人双目立体视觉系统的研究和开发

为了辅助公安人员更好地完成排爆工作，设计和开发了一个带双目立体视觉系统的排爆机器人。该视觉系统使用Matlab7.0和机器视觉软件EVision的EasyMatch库进行开发，能实时捕获排爆机器人周围的图像信息、进行摄像机标定、图像预处理、立体图像匹配、求取可疑目标物的特征点的立体坐标，并把图像实时显示在控制台，控制排爆机器人准确地抓取可疑目标物。该机器人视觉系统成功地抓取实验，表明了它在精度上能够满足排爆机器人的项目要求。     

一种用于彩色图像目标识别的自适应阈值分割方法

机器人视觉系统利用颜色、形状等信息来识别环境目标,但是难点在于识别的鲁棒性和实时性的保证。采用移动机器人做平台,提出一种基于颜色学习的实时目标识别系统,并提出了一种目标颜色学习和分割算法,该算法基于自适应阈值分割图像,考虑环境的光照变化进行调整,改善了系统的实时性和鲁棒性。     

A multipurpose neural processor for machine vision systems

A multitask neural network is proposed as a plausible visual information processor for performing a variety of real-time operations associated with the early stages of vision. The computational role performed by the processor, named the positive-negative (PN) neural processor, emulates the spatiotemporal information processing capabilities of certain neural activity fields found along the human visual pathway. The state-space model of this visual information processor corresponds to a bilayered two-dimensional array of densely interconnected nonlinear processing elements (PE's). An individual PE represents the neural activity exhibited by a spatially localized subpopulation of excitatory or inhibitory nerve cells. Each PE may receive inputs from an external signal space as well as from itself and the neighboring PE's within the network. The information embedded in the external input data which originates from a video camera or another processor is extracted by the feedforward subnet. The feedback subnet of the PN neural processor generates a variety of transient and steady-state activities. Their various computational roles are applicable to gray level, edge, texture, or color information processing. Computer simulations involving gray level image processing are used to illustrate the versatility of the PN neural processor architecture for machine vision system design.     

3-D imaging systems and high-speed processing for robot control

Bin picking by a robot in real time requires the performance of a series of tasks that are beyond the capabilities of commercially available state-of-the-art robotic systems. In this paper, a laser-ranging sensor for real-time robot control is described. This sensor is incorporated into a robot system that has been applied to the bin-picking or random-parts problem. This system contains new technological components that have been developed recently at the Environmental Research Institute of Michigan (ERIM). These components (the 3-D imaging scanner and a recirculating cellular-array pipeline processor) make generalized real-time robot vision a practical and viable technology. This paper describes these components and their implementation in a typical real-time robot vision system application.     

Vision-based robotics: a challenge to real world Artificial Intelligence

Vision is a key function not only for robotics but also for AI more generally. Today real-time visual processing is becoming possible; this means that vision based behavior can become more dynamic, opening fertile areas for applications. One aspect of this is real-time visual tracking. We have built a real-time tracking system and incorporated it in an integrated robot programming environment. Using this, we have performed experiments in vision based robot behavior and human-robot interactions. In particular, we have developed a robotic system capable of 'learning by seeing'. In general, it is important for the AI community not to lose sight of the problems and progress of robotics. After all, an AI system which acts in real-time in the real-world is no less (and no more) than an intelligent robot.     

基于广义预测控制的移动机器人视觉导航

研究了室内环境下移动机器人的视觉导航问题。由单目传感器获取场景图像,利用颜色信息提取路径,采用最小二乘法拟合路径参数,简化图像处理过程,提高了算法的实时性。通过消除相对参考路径的距离偏差和角度偏差来修正机器人的位姿状态,实现机器人对路径的跟踪。为消除机器视觉识别和传输的耗时,达到实时控制,采用改进的多变量广义预测控制方法预测下一时刻控制信号的变化量来修正系统滞后。仿真和实验结果证明了控制算法的可靠性。     

A real-time vision module for interactive perceptual agents

Abstract. Interactive robotics demands real-time visual information about the environment. Real-time vision processing, however, places a heavy load on the robot's limited resources, which must accommodate multiple other processes running simultaneously. This paper describes a vision module capable of providing real-time information from ten or more operators while maintaining at least a 20-Hz frame rate and leaving sufficient processor time for a robot's other capabilities. The vision module uses a probabilistic scheduling algorithm to ensure both timely information flow and a fast frame capture. In addition, it tightly integrates the vision operators with control of a pan-tilt-zoom camera. The vision module makes its information available to other modules in the robot architecture through a shared memory structure. The information provided by the vision module includes the operator information along with a time stamp indicating information relevance. Because of this design, our robots are able to react in a timely manner to a wide variety of visual events.     

基于彩色图像的移动机器人定位

本文利用彩色图像对移动机器人进行定位，将颜色由 Ｒ Ｇ Ｂ 空间转换到 Ｈ Ｓ Ｉ空间中，颜色成为图像的唯一特征，简化了图像的特征抽取，大大提高了图像的处理速度和特征抽取的鲁棒性．在结构上采用全局静态视觉，避免了对摄像机的控制，并使得定位时在环境中只需一个着色标记．在移动机器人运动过程中，利用动态窗口进一步减少数据处理量．考虑视觉系统的延时，推广卡尔曼滤波完成视觉与推算航行数据的融合．该定位方法已成功地应用于我们研制的移动机器人控制系统中，效果良好．     

A real-time fuzzy reasoning based control system for catching a moving goldfish

Designing a real-time visual tracking system to catch a goldfish is a complex task because of the large amount of streaming video data that must be transmitted and processed immediately when tracking the goldfish. Usually, building such visual servoing systems requires the application of high-cost specialized hardware and the development of complicated visual control software. In this paper, a novel low-cost, real-time visual servo control system is presented. The system uses stereo vision consisting of two calibrated cameras to acquire images of the goldfish, and applies the continuously adaptive mean shift (CAMSHIFT) vision-tracking algorithm to provide feedback of a fish’s real-time position at a high frame rate. It then employs a 5-axis robot manipulator controlled by a fuzzy reasoning system to catch the fish. This visual tracking and servoing system is less sensitive to lighting influences and thus performs more efficiently. Experiments with the proposed method yielded very good results, as the system’s real-time 3D vision successfully tracked two fish and guided the manipulator, which has a net attached to its end effector, to catch one of them.     

Stereo vision system for moving object detecting and locating based on CMOS image sensor and DSP chip

Currently, most of the stereo vision systems are constructed on PC-based or multi-CPU combination structures with two CCD cameras. It is difficult to be applied in movable plants for stand-alone requirement. Due to electronic technology development, the complementary metal-oxide semiconductor (CMOS) image sensor has been widely used in a lot of electronic commercial products and the digital signal processor (DSP) operation speed and capacity are good enough for stereo vision system requirement. Here, a new stereo vision platform is designed with TMS320C6416 DSK board integrated with two CMOS color image sensors for detecting and locating moving objects. The data communication interface, system monitoring timing flow, and image pre-processing software programs are developed, too. This system can be used to detect and track any moving object without object color and shape limitations of previous study. Experimental results are used to evaluate this system’s dynamic performance. This low cost stereo vision system can be employed in movable platform for stand-alone application, i.e., mobile robot.