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

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.
Type de document :
Communication dans un congrès
ICRA: International Conference on Robotics and Automation, Apr 2007, Roma, Italy. IEEE, pp.2817-2822, 2007, 〈10.1109/ROBOT.2007.363898〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00366218
Contributeur : Chao Liu <>
Soumis le : vendredi 6 mars 2009 - 11:08:37
Dernière modification le : jeudi 11 janvier 2018 - 06:26:07

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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. IEEE, pp.2817-2822, 2007, 〈10.1109/ROBOT.2007.363898〉. 〈lirmm-00366218〉

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