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Adaptive Vision based Tracking Control of Robots with Uncertainty in Depth Information

Chien Chern Cheah 1 Chao Liu 2 Jean-Jacques E. Slotine 3
1 EEE - School of Electrical and Electronic Engineering
2 DEXTER - Conception et commande de robots pour la manipulation
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
3 MIT - Massachusetts Institute of Technology
Abstract : In this paper, a vision based tracking controller with adaptation to uncertainty in depth information is presented. Depth uncertainty plays a special role in visual tracking as it appears nonlinearly in the overall Jacobian matrix and hence cannot be adapted together with other uncertain kinematic parameters. We propose a novel parameter update law to update the uncertain parameters of the depth. It is proved that system stability can be guaranteed for the visual tracking task in presence of uncertainties in depth information, robot kinematics and dynamics. Simulation results are presented to illustrate the performance of the proposed controller.
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Conference papers
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00366218
Contributor : Chao Liu <>
Submitted on : Friday, March 6, 2009 - 11:08:37 AM
Last modification on : Tuesday, March 9, 2021 - 5:09:20 PM

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CNRS | DEXTER | LIRMM | MIPS | UNIV-MONTPELLIER

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Chien Chern Cheah, Chao Liu, Jean-Jacques E. Slotine. Adaptive Vision based Tracking Control of Robots with Uncertainty in Depth Information. ICRA: International Conference on Robotics and Automation, Apr 2007, Roma, Italy. pp.2817-2822, ⟨10.1109/ROBOT.2007.363898⟩. ⟨lirmm-00366218⟩

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