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http://repositoriodigital.ipn.mx/handle/123456789/14685
Título : | Fast Object Recognition for Grasping Tasks using Industrial Robots |
Otros títulos : | Reconocimiento rápido de objetos para tareas de agarre usando robots industriales |
Autor : | Ismael, López-Juárez Reyes, Rios-Cabrera Mario, Peña-Cabrera Gerardo, Maximiliano Méndez Román, Osorio |
Palabras clave : | Keywords: Artificial neural networks, invariant object recognition, machine vision, robotics. |
Fecha de publicación : | 10-dic-2010 |
Editorial : | Revista Computación y Sistemas; Vol. 16 No. 4 |
Citación : | Revista Computación y Sistemas; Vol. 16 No. 4 |
Citación : | Revista Computación y Sistemas;Vol. 16 No. 4 |
Resumen : | Abstract: Working in unstructured assembly robotic environments, i.e. with unknown part location; the robot has to accurately not only to locate the part, but also to recognize it in readiness for grasping. The aim of this research is to develop a fast and robust approach to accomplish this task. We propose an approach to aid the learning of assembly parts on-line. The approach which is based on ANN and a reduced set of recurrent training patterns which speed up the recognition task compared with our previous work is introduced. Experimental learning results using a fast camera are presented. Some simple parts (i.e. circular, squared and radiused-square) were used for comparing different connectionist models (Backpropagation, Perceptron and FuzzyARTMAP) and to select the appropriate model. Later during experiments, complex figures were learned using the chosen FuzzyARTMAP algorithm showing a 93.8% overall efficiency and 100% recognition rate. Recognition times were lower than 1 ms, which clearly indicates the suitability of the approach to be implemented in real-world operations. |
URI : | http://www.repositoriodigital.ipn.mx/handle/123456789/14685 |
ISSN : | 1405-5546 |
Aparece en las colecciones: | Revistas |
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421_Art. 4_ 96.pdf | 1.02 MB | Adobe PDF | Visualizar/Abrir |
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