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Study of Visual Servoing Robot Control without High Level Image Processing Technique

无高级图像处理技术的视觉伺服机器人控制研究

基本信息

批准号：
10555141
负责人：
DEGUCHI Koichiro
金额：
$ 5.95万
依托单位：
Tohoku University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B).
财政年份：
1998
资助国家：
日本
起止时间：
1998 至 2000
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-10555141/
关键词：
Robot Control Computer Vision Visual Servoing Image Processing Active Vision Image Compression Motion Image Analysis Behavior Control 遠隔制御

项目摘要

In this research, a general scheme to represent the relation between dynamic images and camera and/or object motions is developed for applications to visual control of robots.We consider the case where a moving camera observes moving objects in a static scene. The camera first obtains images of the objects moving within the scene. Then, the possible combinations of the camera and the objects' poses and the obtained images are not arbitrary but constrained to each other. Here we represent this constraint as a lower dimensional hyper surface in the product space of the whole combination of their motion control parameters and image data. The visual control is interpreted as to find a path on this surface leading to their poses where a given goal image will be obtained. In this paper, we propose a visual control method to utilize tangential properties of this surface. First, we represent images with a composition of a small number of "eigen images" by using K-L (Karhunen-Loeve) expansion. Then, we consider to reconstruct the eigen space (the eigen image space) to achieve efficient and straightforward controls. Such reconstruction of the space results in the constraint surface being mostly flat within the eigen space. By this method, visual control of robots in a complex configuration is achieved without image processing to extract and correspond image features in dynamic images. The method also does not need camera or hand-eye calibrations. Many experimental results of visual servoing with the proposed method has been done, and they show the feasibility and applicability of our newly proposed to a simultaneous control of camera self-motion and object motions.

在本研究中，为了应用于机器人的视觉控制，开发了一个表示动态图像与相机和/或物体运动之间关系的一般方案。我们考虑在静态场景中移动摄像机观察移动物体的情况。相机首先获得场景中运动物体的图像。这样，相机和物体姿态的可能组合以及获得的图像就不是任意的，而是相互约束的。在这里，我们将该约束表示为其运动控制参数和图像数据整体组合的积空间中的低维超曲面。视觉控制被解释为在这个表面上找到一条路径，通向他们的姿势，在那里将获得给定的目标图像。在本文中，我们提出了一种视觉控制方法来利用该表面的切向特性。首先，我们使用K-L （Karhunen-Loeve）展开，用少量的“特征图像”组成图像来表示图像。然后，我们考虑重构特征空间（特征图像空间），以实现高效、直观的控制。这样的空间重构使得约束表面在本征空间内基本是平坦的。该方法无需对动态图像进行图像特征提取和对应，即可实现复杂构型机器人的视觉控制。该方法也不需要相机或手眼校准。用该方法进行的视觉伺服实验结果表明，该方法对摄像机自运动和物体运动的同步控制具有可行性和适用性。