Abstract
In this paper, a large environment is divided into sub-areas to enable a robot to apply precise localization technology efficiently in real time. Sub-area features are represented in a feature information system so that conventional machine learning or data mining approaches can be applied to identify the sub-areas. However, conventional representations with a single body of knowledge encounter many problems when the sub-area features are changed. In order to deal with changing environments, the multi-knowledge approach is applied to the identification of environments. Multi-knowledge is extracted from a feature information system by means of multiple reducts (feature sets) so that a robot with multi-knowledge is capable of identifying an environment with some changing features. A case-study demonstrates that a robot with multi-knowledge can cope better with the identification of an environment with changing features than conventional single body of knowledge.
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Wu, Q., McGinnity, T., Prasad, G., Bell, D., Glackin, B. Multi-knowledge Approach for Mobile Robot Identification of a Changing Environment. In: Christensen, H.I. (eds) European Robotics Symposium 2006. Springer Tracts in Advanced Robotics, vol 22. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11681120_14
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DOI: https://doi.org/10.1007/11681120_14
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32689-2
Online ISBN: 978-3-540-32689-2
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