未知環境における自律分散群ロボットの協調探索手法

抄録

This study examines methods for efficiently collecting environmental information, such as obstacle size and location, as an initial response to disaster occurrences. Specifically, we develop and evaluate a decentralized exploration algorithm using multiple mobile robots in an unknown environment. With disaster sites and hazardous areas in mind, we propose a method to explore the entire environment quickly and effectively. In this method, homogeneous autonomous distributed robots determine their exploration direction by combining two clustering techniques based on their self-location and exploration status. Additionally, when robots are in close proximity, they perform local communication to share information. Furthermore, after the exploration is completed, clustering and ellipse fitting are applied to the observed obstacle point cloud data, enabling the estimation of environmental information even from incomplete obstacle data. To verify the effectiveness of these methods, we conducted simulations.