自主移动机器人视觉系统研究
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摘要
随着图像处理技术和计算机视觉算法的成熟,视觉在移动机器人研究中得到广泛应用。本文以一种通用的四轮式自主移动机器人为对象,研究了它的视觉系统,文章包括三部分内容。
第一部分介绍了一种基于圆形信标的全局定位方法。首先运用图像处理技术快速准确地实现椭圆的提取与识别,然后利用摄像机成像模型对空间圆恢复,再将该结果与圆形信标的先验知识相结合,即可得到位置解。该视觉定位方法具有较高地定位精度,可以实时为移动机器人提供局域环境中的绝对位置和方向。
第二部分提出一种以YUV颜色空间作为目标特征向量的跟踪算法,并给出了在移动机器人在全局视觉定位中的应用。文中提出的自适应颜色模板克服了传统颜色识别方法性能受光照变化影响大的缺点,目标位置预测结合颜色识别的思想解决了多个目标点颜色相近的问题,螺旋形搜索和区域生长的策略则进一步加快了目标跟踪速度。
第三部分给出了一种依靠视觉和模糊控制跟踪直线的方法。由于室内环境具有很多的直线边缘,为此提出了一种复杂环境下的目标直线的快速检测算法。在详细推导了四轮式移动机器人的运动学模型后,设计了一个能模仿人驾驶汽车行为的模糊控制器来实现直线跟踪控制。实验表明,该方法能够完成移动小车在室内环境中沿直线行走的任务。
This dissertation researches the computer vision of a four-wheeled autonomous mobile robot, it contains mainly three parts.In the first part, a global location method based on circular beacon is proposed. With imaging model, a space circle is reconstructed after quick recognition of the ellipse in the image. The mobile robot is located by combining the result with prior knowledge. This location method based on vision provides higher precision.The second part provides a novel multi-objects tracking algorithm with YUV color space as character vector space and gives the application in global vision. The self-adaptive color template overcomes the defects of traditional color identification algorithm whose performance is affected greatly by diverse light conditions. The idea that combines position prediction and color identification solves the problem that multi-objects have identical color. The tracking process is accelerated by spiral search and regional growth method.The third part presents a line-tracking method dependent on computer vision and fuzzy control. Since there are many lines in indoor environment, we give a quick recognition of target line. After deducing the kinematical model of the four-wheeled mobile robot, a fuzzy controller, which can imitate human in driving, is designed to control the mobile robot to track the line. Finally, the result shows that the algorithm is effective in line-tracking.
第一部分介绍了一种基于圆形信标的全局定位方法。首先运用图像处理技术快速准确地实现椭圆的提取与识别,然后利用摄像机成像模型对空间圆恢复,再将该结果与圆形信标的先验知识相结合,即可得到位置解。该视觉定位方法具有较高地定位精度,可以实时为移动机器人提供局域环境中的绝对位置和方向。
第二部分提出一种以YUV颜色空间作为目标特征向量的跟踪算法,并给出了在移动机器人在全局视觉定位中的应用。文中提出的自适应颜色模板克服了传统颜色识别方法性能受光照变化影响大的缺点,目标位置预测结合颜色识别的思想解决了多个目标点颜色相近的问题,螺旋形搜索和区域生长的策略则进一步加快了目标跟踪速度。
第三部分给出了一种依靠视觉和模糊控制跟踪直线的方法。由于室内环境具有很多的直线边缘,为此提出了一种复杂环境下的目标直线的快速检测算法。在详细推导了四轮式移动机器人的运动学模型后,设计了一个能模仿人驾驶汽车行为的模糊控制器来实现直线跟踪控制。实验表明,该方法能够完成移动小车在室内环境中沿直线行走的任务。
This dissertation researches the computer vision of a four-wheeled autonomous mobile robot, it contains mainly three parts.In the first part, a global location method based on circular beacon is proposed. With imaging model, a space circle is reconstructed after quick recognition of the ellipse in the image. The mobile robot is located by combining the result with prior knowledge. This location method based on vision provides higher precision.The second part provides a novel multi-objects tracking algorithm with YUV color space as character vector space and gives the application in global vision. The self-adaptive color template overcomes the defects of traditional color identification algorithm whose performance is affected greatly by diverse light conditions. The idea that combines position prediction and color identification solves the problem that multi-objects have identical color. The tracking process is accelerated by spiral search and regional growth method.The third part presents a line-tracking method dependent on computer vision and fuzzy control. Since there are many lines in indoor environment, we give a quick recognition of target line. After deducing the kinematical model of the four-wheeled mobile robot, a fuzzy controller, which can imitate human in driving, is designed to control the mobile robot to track the line. Finally, the result shows that the algorithm is effective in line-tracking.
引文
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[5] 欧青立,何克忠.室外智能移动机器人的发展及其关键技术研究[J].机器人,2000,22(6):519-526.
[6] Brooks R. A robust layered control system for a mobile robot[J]. IEEE Journal of Robotics and Atomation. 1986, 2(1): 14-23.
[7] Hu H, Brady M. A Parallel Processing Architecture for Sensor-based Control of Intelligent Mobile Robot[J]. Robotics and Autonomous Systems, 1996, 17: 235-257
[8] 王耀南,李树涛.多传感器信息融合及其应用综述[J].控制与决策,2001,16(5):518-522.
[9] 刘伟军,董再励.基于立体视觉的移动机器人自主导航定位系统[J].高技术通讯,2001,11(10):91-94
[10] 吴卫国,陈辉堂.基于彩色图像的移动机器人定位[J].机器人,1999,21(5):340—346.
[11] 郝颖明.路标定位中的优化选择策略[J].高技术通讯,2001,11(8):82—85.
[12] ULF Larsaon, Johan Forsberg, Ake Wemersaon. On Robot Navigation Using Identical Landmarks: Integrating Measurements from a Time-of-flight Laser[J]. IEEE International Conference on multisensor Fusion and Intelligent System, (MFI'94)1994: 2-5
[13] 王卫华.基于活动单眼定位的移动机器人物体收集[J].华中科技大学学报,2001,3(3):45—47
[2] 张朋飞,何克忠等.多功能室外智能移动机器人实验平台——THMR—V[J].机器人,2002,24(2):97-101.
[3] 蔡鹤皋.机器人将是21世纪技术发展的热点[J].中国机械工程,2000,11(1):58-60.
[4] 王炎,周大威.移动式服务机器人的发展现状及我们的研究[J].电气传动,2000,4:3-7.
[5] 欧青立,何克忠.室外智能移动机器人的发展及其关键技术研究[J].机器人,2000,22(6):519-526.
[6] Brooks R. A robust layered control system for a mobile robot[J]. IEEE Journal of Robotics and Atomation. 1986, 2(1): 14-23.
[7] Hu H, Brady M. A Parallel Processing Architecture for Sensor-based Control of Intelligent Mobile Robot[J]. Robotics and Autonomous Systems, 1996, 17: 235-257
[8] 王耀南,李树涛.多传感器信息融合及其应用综述[J].控制与决策,2001,16(5):518-522.
[9] 刘伟军,董再励.基于立体视觉的移动机器人自主导航定位系统[J].高技术通讯,2001,11(10):91-94
[10] 吴卫国,陈辉堂.基于彩色图像的移动机器人定位[J].机器人,1999,21(5):340—346.
[11] 郝颖明.路标定位中的优化选择策略[J].高技术通讯,2001,11(8):82—85.
[12] ULF Larsaon, Johan Forsberg, Ake Wemersaon. On Robot Navigation Using Identical Landmarks: Integrating Measurements from a Time-of-flight Laser[J]. IEEE International Conference on multisensor Fusion and Intelligent System, (MFI'94)1994: 2-5
[13] 王卫华.基于活动单眼定位的移动机器人物体收集[J].华中科技大学学报,2001,3(3):45—47