Growing Neural Gas Based Topological Environmental Map Building and Path Planning in Unknown Environment

Abstract

An autonomous mobile robot needs to many tasks such as a self-localization, collision detection, and path planning to a target position in an unknown environment. Therefore, it is important for the robot to build environmental maps with different resolutions in each work space. In addition, the robot requires the path planning capability in the unknown environment for applying the robot to various environment such as a disaster site and commercial construction. This research proposes a Growing Neural Gas based topological environmental map building method from a metric map with high resolution map for using the self-localization. Our proposed method enables to build the topological map with occupancy information of the metric map and preserve the geometric feature of the map simultaneously. Next, the path planning method in the unknown environment is proposed by utilizing the occupancy information of the topological map. Finally, we conduct on several experiments for evaluating our proposed method by comparing to other conventional approaches, and discuss the effectiveness of our proposed method.