Machine vision methods for autonomous micro-robotic systems

AMAVASAI, B. P., CAPARRELLI, Fabio, SELVAN, A,, BOISSENIN, M., TRAVIS, J. R. and MEIKLE, S. (2005). Machine vision methods for autonomous micro-robotic systems. Kybernetes, 34 (9-10), 1421-1439.

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Abstract

Purpose - To develop customised machine vision methods for closed-loop micro-robotic control systems. The micro-robots have applications in areas that require micro-manipulation and micro-assembly in the micron and sub-micron range. Design/methodology/approach - Several novel techniques have been developed to perform calibration, object recognition and object tracking in real-time under a customised high-magnification camera system. These new methods combine statistical, neural and morphological approaches. Findings - An in-depth view of the machine vision sub-system that was designed for the European MiCRoN project (project no. IST-2001-33567) is provided. The issue of cooperation arises when several robots with a variety of on-board tools are placed in the working environment. By combining multiple vision methods, the information obtained can be used effectively to guide the robots in achieving the pre-planned tasks. Research limitations/implications - Some of these techniques were developed for micro-vision but could be extended to macro-vision. The techniques developed here are robust to noise and occlusion so they can be applied to a variety of macro-vision areas suffering from similar limitations. Practical implications - The work here will expand the use of micro-robots as tools to manipulate and assemble objects and devices in the micron range. It is foreseen that, as the requirement for micro-manufacturing increases, techniques like those developed in this paper will play an important role for industrial automation. Originality/value - This paper extends the use of machine vision methods into the micron range.

Item Type:	Article
Additional Information:	Workshop of IEEE Systems, Man and Cybernetics United Kingdom and Ireland, SEP, 2003, Reading, ENGLAND
Research Institute, Centre or Group - Does NOT include content added after October 2018:	Materials and Engineering Research Institute > Materials Analysis and Research Services
Identification Number:	https://doi.org/10.1108/03684920510614740
Page Range:	1421-1439
Depositing User:	Ann Betterton
Date Deposited:	12 Jul 2010 15:09
Last Modified:	18 Mar 2021 09:45
URI:	https://shura.shu.ac.uk/id/eprint/2331

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