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Mobile Robot Autonomous Environment Exploration Method based on Internal Motivation

机译：基于内部动机的移动机器人自主环境探索方法

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摘要

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.
机译：使用自主移动机器人探索未知环境的任务至关重要。本文基于内在激励机制与香农熵理论相结合。考虑到当前地图可能会产生有关环境的重要信息，提出了一种有效检测未知环境的导航策略。该策略基于当前建立的地图中的本地信息，以便机器人可以预测未知环境，使用香农熵来估计要选择的每个边界的信息增益，并将信息增益用作机器人的内部动机。机器人使机器人选择要获得的最大信息增益的边界。实验结果表明，该方法可以有效地帮助机器人探索未知环境，实现环境认知，并建立完整，准确的环境图。

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