Segmentation of Environment and Formation of Graph-based Maps in Mobile Robots ; 移動ロボットにおける環境の分節化とグラフベース地図の構築

九州工業大学 ; 博士（工学） ; 1 Introduction||2 Overview of Related Studies||3 Task Segmentation using mnSOM and Clustering||4 Formation of Graph-based Maps||5 Experimental Results on Task Segmentation and Clustering||6 Experimental Results on the Formation of Graph basedMaps||7 Conclusions and Discussions||Bibliography ; A new approach in Artificial Intelligence (AI), which focuses on agent’s interaction with the world, is expected to solve difficulties in the classical AI. The interaction leads an agent to exhibit emergent behaviors, which are not preprogrammed by a designer. This is what biological agents (i.e. animals and humans) do in their daily life. This dissertation aims at finding mechanisms necessary for this. A mobile robot is used as a test bed for this purpose. The real world is completely different from a virtual world frequently used in the classical AI. The real world is always subject to complexity, noise, and nonlinearity. Information on the real world is often spatio-temporal in nature, and is hard to do information processing in real time. Solving real world problems often leads to unsatisfactory results due to its inherent difficulties. One promising approach to this is to segment the spatio-temporal information into meaningful elements. The purpose of the present thesis is to segment the world and to form a graph-based map for efficient processing. Task segmentation in navigation of a mobile robot based on sensory signals is important for realizing efficient navigation, hence attracted wide attention. In this research, a new approach to segmentation in a mobile robot by a modular network SOM (mnSOM) is proposed. In a mobile robot, the standard mnSOM is not applicable as it is, because it is based on an assumption that class labels are known a priori. In a mobile robot, however, 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 ...