Fish catching by robot using gazing GA visual servoing

Mamoru Minami, Hidekazu Suzuki, Julien Agbanhan

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

This paper presents a new method of scene recognition for manipulator real time visual servoing, which utilizes a hybrid genetic algorithm (GA) in combination with a model shaping a target of known shape, and the unprocessed gray scale image of a scene. The scene recognition method presented here is concerned with the simultaneous recognition of the shape and detection of the position and orientation in the two-dimensional raw-image, of a three-dimensional target being imaged. The proposed hybrid GA employs the "global" search feature of a two-point crossover of a GA, to search a target, together with a GA-based "local" search that focuses on the target of interest found so far, in order to detect accurate target's position in a short time by intensive searching. In order to appraise the proposed hybrid GA recognition method, experiments to pick up a natural fish swimming in a pool by hand net of a robot manipulator by using the visual servoing, have been conducted to show the performances with respect to recognition accuracy in time response and the real-time feature.

Original languageEnglish
Pages (from-to)1198-1206
Number of pages9
JournalNihon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C
Volume68
Issue number4
Publication statusPublished - Apr 2002
Externally publishedYes

Fingerprint

Visual servoing
Fish
Genetic algorithms
Robots
Manipulators
End effectors
Experiments

Keywords

  • Gazing GA Recognition
  • Model-Based Matching
  • Visual Servoing

ASJC Scopus subject areas

  • Mechanical Engineering

Cite this

Fish catching by robot using gazing GA visual servoing. / Minami, Mamoru; Suzuki, Hidekazu; Agbanhan, Julien.

In: Nihon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C, Vol. 68, No. 4, 04.2002, p. 1198-1206.

Research output: Contribution to journalArticle

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