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Empirical Characterization of Convergence Properties for Kernel-based Visual Servoing

  • John P. Swensen
  • Vinutha Kallem
  • Noah J. Cowan
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 401)

Abstract

Visual servoing typically involves separate feature tracking and control processes. Feature tracking remains an art, and is generally treated as independent of the underlying controller. Kernel-based visual servoing (KBVS) is a categorically different approach that eliminates explicit feature tracking. This chapter presents an experimental assessment of the convergence properties (domain of attraction and steady-state error) of the proposed approach. Using smooth weighting functions (the kernels) and Lyapunov theory, we analyze the controllers as they act on images acquired in controlled environments. We ascertain the domain of attraction by finding the largest positive invariant set of the Lyapunov function, inside which its time derivative is negative definite. Our experiments show that KBVS attains a maximum pixel error of one pixel and is commonly on the order of one tenth of a pixel.

Keywords

Lyapunov Function Feature Tracking Visual Servoing Rigid Body Rotation Image Moment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer London 2010

Authors and Affiliations

  • John P. Swensen
    • 1
  • Vinutha Kallem
    • 2
  • Noah J. Cowan
    • 1
  1. 1.Department of Mechanical EngineeringJohns Hopkins UniversityBaltimoreUSA
  2. 2.GRASP LaboratoryUniversity of PennsylvaniaPhiladelphiaUSA

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