Information Entropy-assisted Hierarchical Framework for Unknown Environments Exploration

Abstract

Autonomous exploration of unknown environments is a critical task in robotic search and rescue operations. Recently, hierarchical planning frameworks have gained significant attention for their potential to enhance exploration efficiency. However, most existing approaches struggle with efficient exploration due to two key limitations: (1) neglecting subregion environmental information and (2) inconsistency between local and global paths. To overcome these challenges, we propose an Information Entropy-assisted Hierarchical Planning (IEHP) framework for efficient autonomous exploration. Specifically, we introduce an efficient subregion arrangement method that considers total travel distance, path similarity, and information entropy. Additionally, we propose a globally consistent frontier selection method to minimize redundant local paths, improving alignment between local and global planning. We validate the feasibility and efficiency of our approach through a series of complex simulation scenarios, with experimental results demonstrating the superiority of the proposed method.