Task segmentation in a mobile robot by mnSOM: a new approach to training expert modules

Authors:
M. Aziz Muslim;Masumi Ishikawa;Tetsuo Furukawa
Affiliations:
Kyushu Institute of Technology, Department of Brain Science and Engineering, 2-4 Hibikino, Wakamatsu, 808-0196, Kitakyushu, Japan;Kyushu Institute of Technology, Department of Brain Science and Engineering, 2-4 Hibikino, Wakamatsu, 808-0196, Kitakyushu, Japan;Kyushu Institute of Technology, Department of Brain Science and Engineering, 2-4 Hibikino, Wakamatsu, 808-0196, Kitakyushu, Japan
Venue:
Neural Computing and Applications
Year:
2007

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Abstract

Proposed is a new approach to task segmentation in a mobile robot by a modular network SOM (mnSOM). In a mobile robot the standard mnSOM is not applicable as it is, because it is based on the assumption that class labels are known a priori. In a mobile robot, only a sequence of data without segmentation is available. Hence, we propose to decompose it into many subsequences, supposing that a class label does not change within a subsequence. Accordingly, training of mnSOM is done for each subsequence in contrast to that for each class in the standard mnSOM. The resulting mnSOM demonstrates good segmentation performance of 94.05% for a novel dataset.