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

在本研究中，一个通用的计划来表示动态图像和摄像机和/或对象的运动之间的关系的应用程序的视觉控制的robots.We考虑的情况下，移动摄像机观察到移动物体在一个静态的场景。相机首先获得在场景内移动的对象的图像。然后，相机和对象的姿态以及所获得的图像的可能组合不是任意的，而是相互约束的。在这里，我们将此约束表示为它们的运动控制参数和图像数据的整个组合的乘积空间中的低维超曲面。视觉控制被解释为在该表面上找到通向它们的姿势的路径，其中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，利用这种表面的切线属性。首先，我们表示图像的组成的一个小数目的“本征图像”，通过使用K-L（Karhunen-Loeve）扩展。然后，我们考虑重建本征空间（本征图像空间），以实现高效和直接的控制。空间的这种重构导致约束表面在本征空间内大部分是平坦的。通过这种方法，视觉控制的机器人在一个复杂的配置是不需要图像处理，以提取和对应的动态图像中的图像特征。该方法也不需要相机或手眼校准。大量的视觉伺服实验结果表明，我们提出的新方法的可行性和适用性，同时控制摄像机的自运动和物体的运动。

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.