Brief paper: Adaptive Jacobian vision based control for robots with uncertain depth information

  • Authors:

  • Chien Chern Cheah;Chao Liu;Jean Jacques E. Slotine

  • Affiliations:

  • School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, S(639798), Republic of Singapore;Department of Robotics, LIRMM, UMR 5506, French National Center for Scientific Research (CNRS), 161 Rue Ada, 34095 Montpellier, France;Nonlinear Systems Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Avenue Cambridge, MA 02139, USA

  • Venue:
  • Automatica (Journal of IFAC)
  • Year:
  • 2010

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Abstract

This paper presents a simple vision based setpoint controller with adaptation to uncertainty in depth information. Depth uncertainty plays a special role in vision based control 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 vision regulation task in presence of uncertainties in depth information, robot kinematics and dynamics. Simulation results are presented to illustrate the performance of the proposed controller.