バスの車内環境に関するQ-Learningを用いた自律走行口ボットの経路計画

抄録

Due to the high development of artificial technology, mechanization of robotic technology and automation of household chores or building maintenance, our living quality has been dramatically improved. It is expected that introduction of autonomous robots to indoor is more rapidly proceed than ever by this trend. In this work, we propose the integration of two of the most widely used approaches for the implementation of autonomous navigation systems: the reinforcement learning for path finding, along with SLAM (Simultaneous Localization and Mapping). These two approaches are integrated to address the problem of how a robot should explore a complex environment while it collects perception features in order to locate itself and, at the same time, to obtain information clues about cost traversability of an area. Therefore, this research is a new suggestion for improving the efficiency of current clearing robots.