Intelligent Selective Compliance Articulated Robot Arm robot with object recognition in a multi-agent manufacturing system

Nowadays, industry tends to adopt the smart factory concept in their production. Technology intelligence is applied to use all the resources efficiently. Robots and vision system are masters in this kind of industry. However, information transfer between the robot controller and the vision system po...

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Bibliographic Details
Published in:International journal of advanced robotic systems 2019-03, Vol.16 (2), p.1-6
Main Authors: Zakhama, Afef, Charrabi, Lotfi, Jelassi, Khaled
Format: Article
Language:English
Subjects:
Multiagent systems
Object recognition
Robot arms
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Summary:Nowadays, industry tends to adopt the smart factory concept in their production. Technology intelligence is applied to use all the resources efficiently. Robots and vision system are masters in this kind of industry. However, information transfer between the robot controller and the vision system poses a great challenge. Data exchange between these two systems shall be secure, and the transfer must be with a very high level of accuracy. In this article, a multi-platform software application using a vision system is performed to control a Selective Compliance Articulated Robot Arm robot. The software solution includes the detection of defaults in a product by calculating a compliance rate using an efficient algorithm. An analysis of four different algorithms related to histogram-based similarity functions is set. Then, the most efficient algorithm is integrated into the application that provides a secure communication between three different operating systems. Experiments in a multi-agent manufacturing center validate the effectiveness of the proposed method. Tests demonstrate the efficiency of the data transfer between the vision system and the multi-platform software application and the Selective Compliance Articulated Robot Arm robot. This data transfer can be controlled in a high accuracy manner without any additional manual parameters tuning.
ISSN:1729-8806
1729-8814
DOI:10.1177/1729881419841145