Fish catching by visual servoing using neural network prediction

Toshiaki Yoshida, Mamoru Minami, Yasushi Mae

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


This paper presents a method to predict a fish motion by Neural Network (N.N.) with on-line learning when a robot is pursuing fish-catching by a net at hand through hand-eye robot visual servoing. We assume the motion trajectory of a fish swimming in a pool be approximated by a circle with time varying radius and center position. We try to improve prediction accuracy of a fish motion by using N.N. whose inputs are radii and angular velocities in past three control-times and outputs are radius and angular velocity in the following control period. Using radius and angular velocity obtained by circular approximation, we confirmed that the proposed N.N. prediction system can maintain good prediction performances under the proposed on-line learning process.

Original languageEnglish
Title of host publicationSICE Annual Conference, SICE 2007
Number of pages7
Publication statusPublished - 2007
Externally publishedYes
EventSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu, Japan
Duration: Sep 17 2007Sep 20 2007

Publication series

NameProceedings of the SICE Annual Conference


OtherSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007


  • Back propagation
  • Gazing-GA
  • Neural network
  • Prediction

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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