Self-adaptive output tracking with applications to active binocular tracking

Sisil Kumarawadu, Keigo Watanabe, Kazuo Kiguchi, Kiyotaka Izumi

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)129-147
Number of pages19
JournalJournal of Intelligent and Robotic Systems: Theory and Applications
Volume36
Issue number2
DOIs
Publication statusPublished - Feb 1 2003
Externally publishedYes

Keywords

  • Active vision
  • Adaptive tracking
  • Neural networks
  • Robotic systems
  • SoftMax function

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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