On the Design of a Multimodal Cognitive Architecture for Perceptual Learning in Industrial Robots

Robots can be greatly benefited from the integration of artificial senses in order to adapt to changing worlds. To be effective in complex unstructured environments robots have to perceive the environment and adapt accordingly. In this paper, it is introduced a biology inspired multimodal architectu...

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Published inLecture notes in computer science pp. 1062 - 1072
Main Authors Lopez-Juarez, Ismael, Ordaz-Hernández, Keny, Peña-Cabrera, Mario, Corona-Castuera, Jorge, Rios-Cabrera, Reyes
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Adaptive Resonance Theory
Applied sciences
Computer science; control theory; systems
Control theory. Systems
Data Fusion Technique
Exact sciences and technology
Fuzzy ARTMAP
Multisensory Neuron
Perceptual Learn
Robotics
Industrial robot
Adaptive resonance theory
Perceptive learning
Robotics
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Summary:Robots can be greatly benefited from the integration of artificial senses in order to adapt to changing worlds. To be effective in complex unstructured environments robots have to perceive the environment and adapt accordingly. In this paper, it is introduced a biology inspired multimodal architecture called M2ARTMAP which is based on the biological model of sensorial perception and has been designed to be a more versatile alternative to data fusion techniques and non-modular neural architectures. Besides the computational overload compared to FuzzyARTMAP, M2ARTMAP reaches similar performance. This paper reports the results found in simulated environments and also the observed results during assembly operations using an industrial robot provided with vision and force sensing capabilities.
ISBN:9783540298960
3540298967
ISSN:0302-9743
1611-3349
DOI:10.1007/11579427_108