Parallel robot vision using genetic algorithm and object centroid

Kale, A; Chaczko, Z; Rudas, I

Parallel robot vision using genetic algorithm and object centroid

Kale, A Chaczko, Z

Rudas, I

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Publication Type:: Chapter
Citation:: 2012, 6928 LNCS pp. 170 - 178
Issue Date:: 2012-02-20

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Field	Value	Language
dc.contributor.author	Kale, A	en_US
dc.contributor.author	Chaczko, Z https://orcid.org/0000-0002-2816-7510	en_US
dc.contributor.author	Rudas, I	en_US
dc.date.issued	2012-02-20	en_US
dc.identifier.citation	2012, 6928 LNCS pp. 170 - 178	en_US
dc.identifier.isbn	9783642275784	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17746
dc.description.abstract	Parallel Robots are playing a very important role in the medical, automotive, food and many manufacturing applications. Due to its high speed and efficient operation, it is gaining an increasing popularity in these application domains. For making the parallel robots more automated and an intelligent a machine vision system with robust performance is needed. Here, a Machine Vision Algorithm based on Genetic Evolutionary principles for object detection in the Delta Parallel Robot based systems is proposed. The solution applies a simple, robust and high speed algorithm to accurately detect objects for the application domain. The Image Acquisition of a robot's workspace is performed by using a camera mounted on the end-effector of the robot. The system is trained with the object database and with the most significant visual features of every class of objects. Images are assessed periodically for detecting the Region of Interest (ROI) within an image of the robot's workspace. The ROI is defined as an area in which a presence of object features is detected. The ROI detection is achieved by applying a random sampling of pixels and an assessment of color threshold of every pixel. The color intensity is assumed as one of the features for classification that is based on the training data. After classification process, the Genetic Algorithm is applied to locate the centroid of an object in every class. In a given application class, the Centroid is considered as the most important feature. Knowledge of an approximate location of the Centroid of objects helps to maintain a high speed and reliable pick and place operations of the Delta robot system. The proposed algorithm is tested by detecting presence of electronic components in the workspace. Experimental results show that the suggested approach offers a reliable solution for the Delta robot system. © 2012 Springer-Verlag.	en_US
dc.relation.isbasedon	10.1007/978-3-642-27579-1_22	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Parallel robot vision using genetic algorithm and object centroid	en_US
dc.type	Chapter
utslib.citation.volume	6928 LNCS	en_US
utslib.for	080110 Simulation and Modelling	en_US
utslib.for	080106 Image Processing	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	6928 LNCS	en_US

Abstract:

Parallel Robots are playing a very important role in the medical, automotive, food and many manufacturing applications. Due to its high speed and efficient operation, it is gaining an increasing popularity in these application domains. For making the parallel robots more automated and an intelligent a machine vision system with robust performance is needed. Here, a Machine Vision Algorithm based on Genetic Evolutionary principles for object detection in the Delta Parallel Robot based systems is proposed. The solution applies a simple, robust and high speed algorithm to accurately detect objects for the application domain. The Image Acquisition of a robot's workspace is performed by using a camera mounted on the end-effector of the robot. The system is trained with the object database and with the most significant visual features of every class of objects. Images are assessed periodically for detecting the Region of Interest (ROI) within an image of the robot's workspace. The ROI is defined as an area in which a presence of object features is detected. The ROI detection is achieved by applying a random sampling of pixels and an assessment of color threshold of every pixel. The color intensity is assumed as one of the features for classification that is based on the training data. After classification process, the Genetic Algorithm is applied to locate the centroid of an object in every class. In a given application class, the Centroid is considered as the most important feature. Knowledge of an approximate location of the Centroid of objects helps to maintain a high speed and reliable pick and place operations of the Delta robot system. The proposed algorithm is tested by detecting presence of electronic components in the workspace. Experimental results show that the suggested approach offers a reliable solution for the Delta robot system. © 2012 Springer-Verlag.

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