Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Workshop on visual servoing, IEEE International Conference on Intelligent Robots and Systems, IROS2002, Lausanne, Switzerland, 2002
Dodds, Z., Jägersand, M., Hager G., and Toyama K.: A hierarchical vision architecture for robotic manipulation tasks, International Conference on Computer Vision Systems, ICVS’99, pp. 312–331, 1999
Petersson, L., Jensfelt, P., Tell, D., Strandberg, M., Kragic, D. and Christensen, H.: Systems Integration for Real-World Manipulation Tasks, IEEE International Conference on Robotics and Automation, ICRA 2002, pp. 2500–2505, vol 3, 2002
Hutchinson, S., Hager, G., and Corke P.: A tutorial on visual servo control, IEEE Transactions on Robotics and Automation, 12(5), pp. 651–670, 1996.
Kragic, D.: Visual servoing for Manipulation: Robustness and Integration Issues, PhD thesis, Computational Vision and Active Perception Laboratory (CVAP), Royal Institute of Technology, June, 2001.
Kragic, D. and Christensen, H.: Model Based Techniques for Robotic Servoing and Grasping, IEEE International Conference on Intelligent Robots and Systems, IROS2002, pp. 2002
Roobaert, D.: Pedagogical Support Vector Learning: A Pure Learning Approach to Object Recognition, PhD thesis, Computational Vision and Active Perception Laboratory (CVAP), Royal Institute of Technology, May, 2001.
T.W. Drummond and R. Cipolla. Real-time tracking of multiple articulated structures in multiple views. ECCV’00, 2:20–36.
L. Petersson, D. Austin, and H.I. Christensen. “DCA: A Distributed Control Architecture for Robotics”, IEEE International Conference on Intelligent Robots and Systems, IROS2001, pp. 2361–2368, vol 3, 2001
Malis, E., Chaumette, F., and Boudet, S.: Positioning a coarse-calibrated camera with respect to an unknown object by 2-1/2-d visual servoing. IEEE International Conference on Robotics and Automation, ICRA98, pp. 1352–1359, 1998.
Deguchi, K.: Optimal motion control for image-based visual servoing by decoupling translation and rotation, IEEE International Conference on Intelligent Robots and Systems, IROS1998, pp. 705–711, 1998.
Tell, D.: Wide baseline matching with applications to visual servoing, PhD thesis, Computational Vision and Active Perception Laboratory (CVAP), Royal Institute of Technology, June, Stockholm, Sweden, 2002.
Crinier, S.: Behavior-based Control of a Robot Hand Using Tactile Sensors, Master thesis, Centre for Autonomous Systems, Royal Institute of Technology, December, 2002.
Katz, R.H.: Contemporary logic design, Benjamin Cummings/Addison Wesley Publishing Company, 1993
Horswill, I.D.: Behavior-Based Robotics, Behavior Design, Technical report CS 395, Northwestern University, 2000
Tarr, M.J., and Bulthoff, H.: Object recognition on man, monkey and machine, International Journal of Cognitive Science, Vol. 69, No. 1–2, July 1998.
Edelman, S.: Representation and Recognition in Vision, MIT Press, Cambridge, MA. 1999.
Palmer, S.E.: Vision Science: Photons to Phenomenology, MIT Press, Cambridge, MA. 1999
Goodman, S.R. and Gottlieb, G.G.: Analysis of kinematic invariances of multijoint reaching movements, Biological Cybernetics, vol 73, pp.311–322, 1995
Hu, Y. and Goodale, M.: Constraints in Human visuomotor systems, IEEE International Conference on Intelligent Robots and Systems, IROS 2000, pp. 1633–1638, vol 2, 2000
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kragic, D., Christensen, H.I. (2003). A Framework for Visual Servoing. In: Crowley, J.L., Piater, J.H., Vincze, M., Paletta, L. (eds) Computer Vision Systems. ICVS 2003. Lecture Notes in Computer Science, vol 2626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36592-3_33
Download citation
DOI: https://doi.org/10.1007/3-540-36592-3_33
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00921-4
Online ISBN: 978-3-540-36592-1
eBook Packages: Springer Book Archive