A Framework for Visual Servoing

We consider typical manipulation tasks in terms of a service robot framework. Given a task at hand, such as "Pick up the cup from the dinner table", we present a number of different visual systems required to accomplish the task.A standard robot platform with a PUMA560 on the top is used for experimental evaluation. The classical approach-align-grasp idea is used to design a manipulation system. Here, both visual and tactile feedback is used to accomplish the given task. In terms of image processing, we start by a recognition system which provides a 2D estimate of the object position in the image. Thereafter, a 2D tracking system is presented and used to maintain the object in the field of view during an approach stage. For the alignment stage, two systems are available. The first is a model based tracking system that estimates the complete pose/velocity of the object. The second system is based on corner matching and estimates homography between two images. In terms of tactile feedback, we present a grasping system that, at this stage, performs power grasps. The main objective here is to compensate for minor errors in object position/orientation estimate caused by the vision system.

[1]  Ronald C. Arkin,et al.  An Behavior-based Robotics , 1998 .

[2]  François Chaumette,et al.  Positioning a coarse-calibrated camera with respect to an unknown object by 2D 1/2 visual servoing , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[3]  Lars Petersson,et al.  DCA: a distributed control architecture for robotics , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[4]  Alex M. Andrew,et al.  Object Recognition in Man, Monkey, and Machine , 2000 .

[5]  Simon R. Goodman,et al.  Analysis of kinematic invariances of multijoint reaching movement , 1995, Biological Cybernetics.

[6]  Ieee Robotics Proceedings, 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems, September 30-October 4, EPFL Lausanne, Switzerland , 2002 .

[7]  Danny Roobaert Pedagogical support vector learning : a pure learning approach to object recognition , 2001 .

[8]  Koichiro Deguchi,et al.  Optimal motion control for image-based visual servoing by decoupling translation and rotation , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[9]  Danica Kragic,et al.  Model based techniques for robotic servoing and grasping , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Lars Petersson,et al.  Systems integration for real-world manipulation tasks , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[11]  Melvyn A. Goodale,et al.  Constraints in human visuomotor systems , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[12]  Peter I. Corke,et al.  A tutorial on visual servo control , 1996, IEEE Trans. Robotics Autom..

[13]  Gregory D. Hager,et al.  A Hierarchical Vision Architecture for Robotic Manipulation Tasks , 1999, ICVS.

[14]  Danica Kragic Visual Servoing for Manipulation : Robustness and Integration Issues , 2001 .

[15]  Roberto Cipolla,et al.  Real-Time Tracking of Multiple Articulated Structures in Multiple Views , 2000, ECCV.

[16]  Jan-Olof Eklundh,et al.  Computational Vision and Active Perception Laboratory, CVAP , 1998 .

[17]  Shimon Edelman,et al.  Representation and recognition in vision , 1999 .

[18]  Randy H. Katz,et al.  Contemporary Logic Design , 2004 .

[19]  Dennis Tell,et al.  Wide baseline matching with applications to visual servoing , 2002 .

[20]  Heinrich H Bülthoff,et al.  Image-based object recognition in man, monkey and machine , 1998, Cognition.