Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-26T07:19:15.942Z Has data issue: false hasContentIssue false

Robotic grasping of complex shapes: is full geometrical knowledge of the shape really necessary?

Published online by Cambridge University Press:  09 March 2009

M. A. Rodrigues
Affiliation:
Department of Computer Science, The University of Wales, Aberystwyth SY233DB, Wales (UK)
Y. E. Li
Affiliation:
Department of Computer Science, The University of Wales, Aberystwyth SY233DB, Wales (UK)
M. H. Lee
Affiliation:
Department of Computer Science, The University of Wales, Aberystwyth SY233DB, Wales (UK)
J. J. Rowland
Affiliation:
Department of Computer Science, The University of Wales, Aberystwyth SY233DB, Wales (UK)

Summary

This paper aims at contributing to a sub-symbolic, feedback-based “theory of robotic grasping” where no full geometrical knowledge of the shape is assumed. We describe experimental results on grasping 2D generic shapes without traditional geometrical processing. Grasping algorithms are used in conjunction with a vision system and a robot manipulator with a three-fingered gripper is used to grasp several different shapes. The altorithms are run on the shape as it appears on the computer screen (i.e. directly from a vision system). Simulated gripper ringer with virtual sensors are configured and positioned on the screen whose inputs are controlled by moving their position relative to the image until an equilibrium is reached among the control systems involved.

Type
Articles
Copyright
Copyright © Cambridge University Press 1995

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Gilbert, E.G. and Ong, C.J., “New Distances for the Separation and Penetration of Objects” IEEE Conf. on Robotics and Automation, San Diego, USA (04 813, 1994) pp. 579586.Google Scholar
2.Thomas, F. and Torras, C., “Interference Detection between Non-Convex Polyhedra Revisited with a Practical Aim” IEEE Conf. on Robotics and Automation, San Diego, USA (05 813, 1004) pp. 587594.Google Scholar
3.Lin, M.C., Manocha, D. and Canny, J., “Fast Contact Determination in Dynamic Environments” IEEE Conf. on Robotics and Automation, San Diego, USA (05 813 1994) pp.602608Google Scholar
4Burton, M. and Shadbolt, N., POP-11 Programming for Artificial Intelligence (Addison-Wesley Publisher, Reading, Mass., 1987).Google Scholar
5.Li, Y., Lee, M., Rodrigues, M.A., and Rowland, J., “A Visually Guided Robot System for Food Handling Applications” IEEE Conf. on Robotics and Automation, San Diego, USA (05 813,1994) pp 25912597.Google Scholar
6.Powers, W.T., Behavior: the Control of Perception (Aldine de Gruyter, New York, 1973).Google Scholar
7.Rodrigues, M.A. and Lee, M.H., “Nouvelle Al and Perceptual Control Theory” In: Prospects for Artificial Intelligence (Sloman, A. et al. eds. IOS Press, Amsterdam. 1993) pp. 168178.Google Scholar
8.Rodrigues, M.A. and Lee, M.H. (eds.), “Perceptual Control Theory” Proceedings of 1st European Workshop, The University of Wales, Aberystwyth, ISBN 0–903878–19–4, 1994).Google Scholar
9.Rodrigues, M.A., Loftus, C., Ratcliffe, M., and Li, Y.F.. “Structure Notation of Dynamic Systems: a Pictorial Language Approach” Proceedings of IEEE Computer Society 1994 Conference on Computer Languages, Toulouse, France (May 16–19, 1994) pp. 219228Google Scholar