Autonomous Multi-Floor and Narrow Indoor Exploration Using Multi-Criteria Decision-Making Approach

Exploring narrow and multi-floor indoor environments presents significant challenges due to their confined spaces and structural complexity. This paper introduces a novel exploration strategy based on Multi-Criteria DecisionMaking (MCDM) to address these challenges effectively. The proposed algorithm dynamically manages exploration coverage and utilizes ray-casting techniques tailored to the size of the environment to identify exploration candidates efficiently. Additionally, it incorporates a robust staircase detection and traversal mechanism using 3D LiDAR sensors, enabling seamless exploration across multiple floors. Experimental validation in real-world maze-like environments demonstrated the algorithm's capability to thoroughly explore confined spaces, detect and overcome staircases, and resume exploration on new floors. The results confirmed the algorithm's effectiveness in achieving comprehensive exploration and robust performance, validated through experiments conducted under diverse and challenging environmental conditions.