Acquisition of optimal action selection to avoid moving obstacles in autonomous mobile robot

The principal aim of this study is to show how an autonomous mobile robot can acquire the optimal action to avoid moving obstacles through the interaction with the real world. In this paper, we propose a new architecture using the hierarchical fuzzy rules, fuzzy evaluation system and learning automata. By using our proposed method the robot acquires the fine behavior on how to move to the goal avoiding moving obstacles using the steering and velocity control inputs, simultaneously. Also we show the experimental results to confirm the feasibility of our method.