Generating Image Trajectories and Direct Image Jacobians for Visual Servoing

这是一个为期三年的标准奖项。视觉伺服，利用视觉反馈来控制机器人末端执行器，可以提供比示教悬挂器或远程操作系统提供的更好的机器人界面。当精确定位不切实际或不可能时，视觉伺服是有利的。在基于图像的伺服中，控制是在图像中实现的。这种方法的优点是，人们不必重建关于机器人环境的3D信息，并且存在对摄像机校准误差不太敏感的控制律。然而，为了开发健壮、稳定的系统，基于图像的伺服存在一些问题需要克服。该项目将解决阻碍基于图像的伺服应用的两个问题。首先，对图像特征点路径规划方法进行了研究和论证。以前的研究工作主要集中在“图像雅可比”的形式和可能的分解上，它将机械手的速度与图像运动联系起来。相反，该项目将开发方法来确定图像内的路径，以用作基于图像的“标准”调节器的输入。PI的方法将既提高控制器的性能，又确保在操纵任务期间图像特征保持在视场内。其次，将分析、评估、测试一个参数化的直接图像到驱动器模型，并将其与经典方法进行比较。来自图像的误差信号和图像雅可比通常映射到末端执行器速度，然后必须使用机器人运动学模型将其转换为执行器速度。将图像位移直接转换为执行器命令的控制器将省去中间步骤，从而在速度和精度方面提高系统性能。解决稳健性和敏感性问题将促进这些技术在实验室外的应用。该项目的成功将促进基于图像的伺服技术在生产操作中被接受为一种可行的控制方法。这项工作的主要科学贡献将包括：(1)引入通过图像路径规划创建的高级机器人运动命令生成；(2)引入适当了解模型局限性的参数化直接图像雅可比矩阵，从而在基于图像的视觉伺服控制器的设计方面取得进展。

This is a three year standard award. Visual servoing, the use of visual feedback in the control of a robot end-effector, can provide a superior robot interface than those offered by teach pendants or tele-operated systems. Visual servoing is advantageous when precise fixturing is impractical or impossible. In image based servoing, control is effected in the image. This method has the advantages that one does not have to reconstruct 3D information about the robot's environment, and that control laws exist that are not overly sensitive to camera calibration errors. However, there are problems with image based servoing that need to be overcome in order to develop robust, stable systems. This project will address two issues impeding the application of image-based servoing. First, image feature-point path planning methods will be developed and demonstrated. Previous research efforts have focused on the form, and possible decompositions, of the "Image Jacobian", relating manipulator velocities to image motion. In contrast, this project will develop methods to determining a path within the image to use as input to a "standard" image-based regulator. The PI's approach will both improve controller performance and also ensure the image features remain within the field of view during the manipulation task. Second, a parameterized direct image-to-actuator model will be analyzed, evaluated, tested, and compared with classical approaches. The error signal from the image together with the image Jacobian typically map to end-effector velocities which then must be converted to actuator velocities using the robot kinematic model. A controller that converts image displacements directly to actuator commands would eliminate the intermediate step thus enhancing system performance, both in terms of speed and accuracy. Addressing issues of robustness and sensitivity will facilitate application of these techniques outside of the laboratory. Success of this project will facilitate the acceptance of image based servo techniques as a viable control method in production operations. The key scientific contributions created by this effort will include advances in the design of image based visual servo controllers through (1) the introduction of superior robot motion command generation created through image path planning and (2) the introduction of a parameterized direct image Jacobian with appropriate understanding of the model's limitations.