A New Approach to Task Segmentation in Mobile Robots by mnSOM
Muhammad Aziz Muslim, Masatoshi Ishikawa, Tetsuo Furukawa · The 2006 IEEE International Joint Conference on Neural Network Proceedings · 2006
Proposed is a new task segmentation method in navigation of mobile robots by a modular network SOM (mnSOM). mnSOM is an extension of SOM in that a function module instead of a vector unit is used to increase its representation capability. It has the ability of both segmentation and interpolation. During learning, modules in mnSOM compete with each other to become an expert for a subset of data. To increase temporal continuity of winner modules, winner decision algorithms using an MSE based threshold are proposed to improve standard mnSOM. We also propose methods for labeling modules based on MSE. The resulting mnSOM demonstrates good segmentation performance of 89.3% for a novel dataset.