ART-Based Fusion of Multi-modal Information for Mobile Robots

Abstract : Robots operating in complex environments shared with humans are confronted with numerous problems. One important problem is the identification of obstacles and interaction partners. In order to reach this goal, it can be beneficial to use data from multiple available sources, which need to be processed appropriately. Furthermore, such environments are not static. Therefore, the robot needs to learn novel objects. In this paper, we propose a method for learning and identifying obstacles based on multi-modal information. As this approach is based on Adaptive Resonance Theory networks, it is inherently capable of incremental online learning.
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Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.1-10, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_1〉
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Elmar Berghöfer, Denis Schulze, Marko Tscherepanow, Sven Wachsmuth. ART-Based Fusion of Multi-modal Information for Mobile Robots. Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.1-10, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_1〉. 〈hal-01571378〉

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