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Relative end-effector control using Cartesian position based visual servoing

Wilson, W.J., Williams Hulls, C.C., Bell, G.S.·1996·IEEE Transactions on Robotics and Automation

Abstract

This paper presents a complete design methodology for Cartesian position based visual servo control for robots with a single camera mounted at the end-effector. Position based visual servo control requires the explicit calculation of the relative position and orientation (POSE) of the workpiece object with respect to the camera. This is accomplished using image plane measurements of a number of known feature points on the object, and then applying an extended Kalman filter to obtain a recursive solution of the photogrammetric equations, and to properly combine redundant measurements. The control is then designed by specifying the desired trajectories with respect to the object and forming the control error in the end-effector frame. The implementation using a distributed computer architecture is described. An experimental system has been built and used to evaluate the performance of the POSE estimation and the position based visual servo control. Several results for relative trajectory control and target tracking are presented. Results of the experiments showing the effect of loss of some of the redundant features are also presented.

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Metadata

Authors
Wilson, W. J., Williams Hulls, C. C., & Bell, G. S.
Venue
IEEE Transactions on Robotics and Automation
Published
1996
Volume
12
Issue
5
Pages
684-696
ISSN
1042-296X (ISSN-L)
OpenAlex
W2136492961
Cited by
623
References
0
FWCI
9.66
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Relative end-effector control using Cartesian position based visual servoing. https://app.cochat.ai/papers/10.1109/70.538974
The paper
Wilson, W. J., Williams Hulls, C. C., & Bell, G. S. (1996). Relative end-effector control using Cartesian position based visual servoing. IEEE Transactions on Robotics and Automation. https://doi.org/10.1109/70.538974

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.