基于单目视觉的自主足球机器人自定位研究

本文介绍了一种在颜色场地信息已经由视觉识别的前提下,通过单目视觉测距,实现全自主足球机器人自定位的几何方法;对摄像机CCD像面的量化、镜头的非线性畸变和摄像机定标等可能的引起误差的因素进行了详细分析,并且对应地提出了解决办法.尤其是引入了偏最小二乘回归法对摄像机内部参数进行定标,避免由变量的相关性导致的定标误差;同时也更适于实际的比赛情况.实验结果表明它是可行的.     

图像传感器OV7620在自主足球机器人中的应用

在简单介绍了全自主足球机器人比赛系统的基础上，分析了传统视觉系统的缺点，给出了用CMOS图像传感器OV7620、SRAM帧存储器IS61LV25616、CPLD／FPGA控制器EPF10K10LC84—3以及DSP器件TMS320VC5416设计的新型嵌入式图像采样处理系统的设计方案。提出了RGB空间到HIS空间的变换方法，从而明显提高了足球机器人视觉系统的速度和可靠性。     

仿人足球机器人目标定位与追踪算法改进

对于仿人足球机器人来说,视觉功能是极其重要的。在足球机器人的各种关键技术中,机器视觉是应用范围最广,最为基本的技术之一。移动机器人视觉的研究主要集中在颜色模型建立、目标识别、定位以及跟踪等方面。仿人机器人视觉系统的识别与定位算法也是目前的研究热点,目标的实时识别与定位是足球机器人在足球赛中精确踢球的前提。文章主要是针对目前足球机器人在视觉系统上所存在的问题进行了颜色模型建立及目标定位算法的改进,加入了目标追踪算法,确保目标识别与定位的准确。在i Kid足球机器人上进行试验并调试,试验结果具有较好的实时性和准确性。     

中型组足球机器人全向视觉定位技术

自定位是移动机器人正常工作所必须具备的能力.综述了RoboCup中型组足球机器人比赛中机器人的基于视觉定位技术,包括机器人自定位技术和多机器人协作物体定位技术,并对这些定位方法特点进行了分析探讨.首先介绍算法相对简单且应用广泛的基于路标的几何定位法;其次分析基于贝叶斯滤波的概率定位方法,它融合了多传感器信息,能够达到较好的定位性能;最后探讨了多机器人协作物体的定位方法.最后对机器人视觉定位的发展趋势进行了展望.     

基于天花板的移动机器人视觉定位方法的研究

为了实现室内移动机器人的自定位,考虑到室内的复杂程度和环境特色,设计一种基于单目视觉的移动机器人定位系统。机器人在室内带有天花板路标信息的环境下,以颜色和形状结合的路标作为特征,通过机器人头顶与天花板垂直的摄像机,运用彩色图像分割轮廓矩匹配的方法识别路标,解析路标携带的世界坐标信息,从而计算出机器人的全局位置和航向角。该方法大大提高了图像处理和特征提取的速度,通过实验验证,其满足定位系统的实时性和鲁棒性要求。     

基于视觉信息的移动机器人控制系统设计

机器人的视觉相当于机器人的眼睛,机器人视觉系统是当下研究热点。本文主要研究基于视觉信息的机器人系统,通过对获得的图像信息的处理来实现对机器人的控制。详细讨论了摄像机标定、图像处理等方面的设计,实现了对机器人控制系统的设计。     

机器人视觉定位抓取教学案例设计

本文围绕当下机器人视觉定位抓取技术的应用热点,结合机器视觉课程教学大纲内容,设计了机器人视觉定位抓取教学案例。通过案例驱动式理实一体化教学,使学生加深对机器人视觉定位抓取系统中摄像机标定、工件图像分割、中心点坐标和偏转角计算、视觉系统与机器人系统通讯等技术的理解,激发学生学习兴趣,培养学生问题分析、方案设计以及基本算法的综合应用能力,进而提升学生解决实际工程问题的能力。     

一种手眼机器人视觉伺服控制方法的设计

针对手眼机器人视觉伺服系统中的末端摄像机速度确定问题,文章采用一般模型法,通过求解最优化控制问题来设计控制器,即摄像机的速度,基于图像误差实现了定位控制,该方法能够使机器人在工作空间内从任一初始位置运动到期望位置,确保了系统全局渐近稳定且不需要物体的几何模型.最后给出的仿真实例表明了本方法的有效性.     

基于仿真机器人的双目视觉定位

为了提升仿真机器人的视觉定位功能,对基于机器人系统的双目视觉定位功能的实现进行了研究.首先通过实验求解摄像机镜头内外参数,通过软件仿真验证算法和标定结果的正确性,然后将内参数配置到仿真机器人平台的软件系统中,在此系统中实现双目视觉定位功能.实验结果表明,仿真机器人的双日视觉定位结果精确,提升了机器人视觉系统的定位功能.     

Fira中基于较短轴的色标分块及朝向角辨识算法

视觉是集控式足球机器人系统（Fira）获得外部信息的唯一途径，快速、准确地获得视觉信息是高层决策的基础。首先，给出了集中视觉系统的结构框图；接着。根据队标的对称性特点，提出了一种基于较短轴分割的色标分块方法；最后，给出了相应的朝向角补偿逼近辨识算法。实验结果表明，新算法辨识结果相当准确。     

自主足球机器人全维视觉系统的设计与建模

对全维视觉系统（OPVS）及其成像模型进行了研究。利用单视点约束条件和方程设计了自主足球机器人全维视觉传感器的镜面参数,确定全维视觉传感器的尺寸,建立了包括反光镜的反射模型和摄像头的针孔成像模型在内的一整套完备的数学模型,确定像点和物点间的对应关系,为全维成像提供了必要的数学模型和算法。用仿真实验和真实图像实验验证了系统的实用性。     

Determining the absolute orientation in a corridor using projectivegeometry and active vision

The capability of a mobile robot to determine its position in the environment (self-localization) is a prerequisite for achieving autonomous navigation. An approach is proposed for determining the absolute orientation of an autonomous robot in a system of corridors, based on the projective geometry and active computer vision. In the proposed approach, the common direction of longitudinal corridor edges is inferred by detecting the vanishing point of the corresponding straight line segments in the image. It is assumed that the knowledge about the vertical direction in the scene is available, so that the image coordinates of these vanishing points are considerably constrained. However, longitudinal corridor edges are not visible in images acquired for many viewing directions, so that the processing in a localization procedure has to be performed on a sequence of images acquired from the given position, for regularly arranged orientations of the camera. Extensive experimentation was performed on real scenes and the obtained results are provided     

自主机器人足球系统的通信机制及通信语言设计

自主移动机器人系统涉及到在未知的动态环境中多智能体的协同工作，如何实现各移动机器人之间的高效实时通信是一个关键的问题。通信需要解决对话管理、通信语言和通信协议三个问题。其中，通信语言是智能体之间传送信息和交换知识的媒介。本文建立了全自主移动机器人系统的通信机制，为多机器人系统的协作问题求解和知识共享提供一套基于KQML的简单实用可靠的通信语言。实验证明该设计方法可行。     

自主机器人视觉与行为模型及避障研究

主动的障碍物探测与避障行为控制一直是自主机器人的重点研究课题.在以往的研究中,机器人的视觉系统与电动机驱动系统往往是各自独立的构成控制体系,人们很少关注它们之间的关系,这样就从本质上割裂了两者之间的紧密耦合关系,减少了获取更多信息的途径.本文主要研究如何建立机器人视觉与电动机驱动系统关系的视觉与行为模型,通过这种紧密的耦合关系为机器人运动提供更丰富的信息来源.此外,在该模型的基础上引入了强化学习,引导机器人进行动态避障.实验表明该方法是可靠的,能够满足实时性要求.     

An efficient omnidirectional vision system for soccer robots: From calibration to object detection

Robotic soccer is nowadays a popular research domain in the area of multi-robot systems. In the context of RoboCup, the Middle Size League is one of the most challenging. This paper presents an efficient omnidirectional vision system for real-time object detection, developed for the robotic soccer team of the University of Aveiro, CAMBADA. The vision system is used to find the ball and white lines, which are used for self-localization, as well as to find the presence of obstacles. Algorithms for detecting these objects and also for calibrating most of the parameters of the vision system are presented in this paper. We also propose an efficient approach for detecting arbitrary FIFA balls, which is an important topic of research in the Middle Size League. The experimental results that we present show the effectiveness of our algorithms, both in terms of accuracy and processing time, as well as the results that the team has been achieving: 1st place in RoboCup 2008, 3rd place in 2009 and 1st place in the mandatory technical challenge in RoboCup 2009, where the robots have to play with an arbitrary standard FIFA ball.     

World modeling on an MSL robotic soccer team

When a team of robots is built with the objective of playing soccer, the coordination and control algorithms must reason, decide and actuate based on the current conditions of the robot and its surroundings. This is where sensor and information fusion techniques appear, providing the means to build an accurate model of the world around the robot, based on its own limited sensor information and the also limited information obtained through communication with the team mates. One of the most important elements of the world model is the robot self-localization, as to be able to decide what to do in an effective way, it must know its position in the field of play. In this paper, the team localization algorithm is presented focusing on the integration of visual and compass information. An important element in a soccer game, perhaps the most important, is the ball. To improve the estimations of the ball position and velocity, two different techniques have been developed. A study of the visual sensor noise is presented and, according to this analysis, the resulting noise variation is used to define the parameters of a Kalman filter for ball position estimation. Moreover, linear regression is used for velocity estimation purposes, both for the ball and the robot. This implementation of linear regression has an adaptive buffer size so that, on hard deviations from the path (detected using the Kalman filter), the regression converges faster. A team cooperation method based on sharing the ball position is presented. Other important data during the soccer game is obstacle data. This is an important challenge for cooperation purposes, allowing the improvement of team strategy with ball covering, dribble corridor estimation, pass lines, among other strategic possibilities. Thus, detecting the obstacles is ceasing to be enough and identifying which obstacles are team mates and opponents is becoming a need. An approach for this identification is presented, considering the visual information, the known characteristics of the team robots and shared localization among team members. The described work was implemented on the CAMBADA team and allowed it to achieve particularly good performances in the last two years, with a 1st and a 3rd place in the world championship RoboCup 2008 and RoboCup 2009 editions, respectively, as well as distinctively achieve 1st place in 2008 and 2009 editions of the Portuguese Robotics Open.     

Optimal control at energy performance index of the mobile robots following dynamically created trajectories

In practice, the problem of motion control of the wheeled mobile robots is often neglected. Wheeled mobile robots are strongly nonlinear systems and restricted by non-holonomic constraints. Motion control of such systems is not trivial task and usage of non-optimal control signals can lead to deterioration of the overall robot system's performance. In case of autonomous application of the mobile robots all parts of its control system should work perfectly. The paper presents the theory and application of the optimal control method at the energy performance index towards motion control of the two-wheeled mobile robot during the realisation of complex, dynamically created trajectories. With the use of the proposed control method the two-wheeled mobile robot can realise effectively the desired trajectory, which is generated ad-hoc by the navigation system of the robot. Thus the proposed method can be used for motion control of autonomous or semi-autonomous wheeled mobile robots. The presented results of both computer simulations and experiments indicate that the proposed method works effectively from the point of view of the motion control of two-wheeled mobile robot. Movement of the mobile robot appeared reliable and predictable during all the tests.     

A robust omnidirectional vision sensor for soccer robots

How to make robot vision work robustly under varying lighting conditions and without the constraint of the current color-coded environment are two of the most challenging issues in the RoboCup community. In this paper, we present a robust omnidirectional vision sensor to deal with these issues for the RoboCup Middle Size League soccer robots, in which two novel algorithms are applied. The first one is a camera parameters auto-adjusting algorithm based on image entropy. The relationship between image entropy and camera parameters is verified by experiments, and camera parameters are optimized by maximizing image entropy to adapt the output of the omnidirectional vision to the varying illumination. The second one is a ball recognition method based on the omnidirectional vision without color classification. The conclusion is derived that the ball on the field can be imaged to be an ellipse approximately in our omnidirectional vision, and the arbitrary FIFA ball can be recognized by detecting the ellipse imaged by the ball. The experimental results show that a robust omnidirectional vision sensor can be realized by using the two algorithms mentioned above.