Fish catching by adopting neural network and chaos to robotic intelligence

Gao Jingyu, Mamoru Minami, Yasushi Mae

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


In this paper we have dealt with prediction of fish motion under the vision system provided by CCD camera and embedded chaos motion into the system for more effective catching action. Fearing the tracking net attached at robot hand, the fish can suddenly change its escaping trajectory or speed up. Furthermore, as the time of tracking process flows, the fish can somewhat get accustomed to the environment and begin to learn new strategies about how to escape from the bothering net. For example, the fish tends to stay within a corner where it is forbidden for the net to reach for safety or stays away from the net by keeping a constant distance, which can be thought that the fishes know how to produce a steady-state error in a control loop of visual feedback. For the sake of such innate ability being widely existed in animal behavior, the effective intelligent method will need to be conceived to go beyond the fish intelligence. The purpose of this paper is to construct an intelligent system that is able to exceed the fish intelligence in order to track and catch the fish successfully like fish-eating animals in nature to survive.

Original languageEnglish
Title of host publication2006 SICE-ICASE International Joint Conference
Number of pages6
Publication statusPublished - 2006
Externally publishedYes
Event2006 SICE-ICASE International Joint Conference - Busan, Korea, Republic of
Duration: Oct 18 2006Oct 21 2006

Publication series

Name2006 SICE-ICASE International Joint Conference


Other2006 SICE-ICASE International Joint Conference
Country/TerritoryKorea, Republic of


  • 1-step GA
  • Chaos
  • Machine intelligence
  • Neural network
  • Visual servoing

ASJC Scopus subject areas

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


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