Self-Adaptive Output Tracking with Applications to Active Binocular Tracking View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2003-02

AUTHORS

Sisil Kumarawadu, Keigo Watanabe, Kazuo Kiguchi, Kiyotaka Izumi

ABSTRACT

In this article we present a neurally-inspired self-adaptive active binocular tracking scheme and an efficient mathematical model for online computation of desired binocular-head trajectories. The self-adaptive neural network (NN) model is general and can be adopted in output tracking schemes of any partly known robotic systems. The tracking scheme ingeniously combines the conventional Resolved Velocity Control (RVC) technique and an adaptive compensating NN model constructed using SoftMax basis functions as nonlinear activation function. Desired trajectories to the servo controller are computed online by the use of a suitable linear kinematics mathematical model of the system. Online weight tuning algorithm guarantees tracking with small errors and error rates as well as bounded NN weights. More... »

PAGES

129-147

References to SciGraph publications

  • 1993-10. The role of fixation in visual motion analysis in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 1996-02. Active fixation for scene exploration in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 1993-06. Model-based object tracking in monocular image sequences of road traffic scenes in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2003. Adaptive Neural Network Based Approach for Active Flow Control in MANIPULATION AND CONTROL OF JETS IN CROSSFLOW
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1023/a:1022620623402

    DOI

    http://dx.doi.org/10.1023/a:1022620623402

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1016332121


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