Some Aspects Concerning Geometric Forms Automatically Find Images and Ordering them Using RobotStudio Simulation

Author:

Cohal Viorel1

Affiliation:

1. “Gheorghe Asachi” Technical University of Iasi-Romania

Abstract

The paper presents finding certain geometric objects in an image of the work area. Using offline programming of robots ABB RobotStudio program is ordered using these objects in a predetermined order. A significant number of high-performance algorithms used in image processing using methods and techniques of artificial intelligence. On the other hand, artificial intelligence requires intersection area and building systems capable of performing the functions of the human intellect: experiential learning, natural language understanding and the use of reasoning to solve problems or make decisions. All these imply, however, accumulating a certain amount of information (knowledge base, environmental information, etc.). This information is taken from the intelligent sensors, and creates an image of the environment when taking data. The image thus obtained is extracted useful information. However the field of artificial vision is a common discipline both image processing and artificial intelligence. In the first stage, objects placed in the normal position as to determine the geometrical shape feature. Determine a range of appropriate methods for recognizing geometric shapes (square, circle, rectangle, etc.). Using ABB robot with gripper each object is positioned firmly in place .

Publisher

Trans Tech Publications, Ltd.

Subject

General Engineering

Reference6 articles.

1. Cohal, V., Proiectarea stației de lucru cu roboți pentru sudare în RobotStudio, Editura Politehnium, Iaşi, ISBN 978-973-621-260-4, (2008).

2. Cohal, V., Programarea roboților pentru sudare și simulare în RobotStudio, Editura Politehnium, Iaşi, ISBN 978-973-621-261-1, (2008).

3. Operating Manual RobotStudio 5. 60, ABB AB Robotics Products, SE-721 68, Västerås, Sweden, (2013).

4. Hagedoorn, M., Pattern Matching Using Similarity Measures, PhD Thesis, Universiteit Utrecht, (2000).

5. Sezgin, M. and Sankur, B. Survey over Image Thresholding Techniques and Quantitative Performance Evaluation , Journal of Electronic Imaging, vol 13, no. 1, pp.146-168, (2004).

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