Mobile Robot Autonomous Environment Exploration Method based on Internal Motivation

The task of exploring unknown environments using autonomous mobile robots is essential. This paper is based on the internal motivation mechanism combined with the Shannon entropy theory. Considering that the current map may generate important information gains about the environment, a navigation strategy for effectively detecting unknown environments is proposed. The strategy is based on the local information in the currently established map, so that the robot could predict the unknown environment, uses the Shannon entropy to estimate the information gain of each frontier to be selected, and uses the information gain as the internal motivation of the robot to make the robot choose a frontier with the largest information gain to reach. The experimental results show that the proposed method can effectively help the robot to explore the unknown environment, realize environment cognition, and establish a complete and accurate environmental map.