Hierarchical Exploration of Mobile Robots by Unknown Region Division With Multiple Environment Representations

Fast exploration is crucial for mobile robots to improve their autonomy and range of applications. To enhance exploration efficiency by utilizing environmental information more effectively, we propose a frontier-based hierarchical planning approach that incorporates information on unknown regions. Probabilistic roadmap and hgrid are used to maintain the information on the explored and unknown regions, facilitating the path search and frontier management. On this basis, a rational distinctive exploration region (DER) division method is devised, laying foundation for global tour planning. For its resolution, a two-opt algorithm incorporating historical information is employed. Guided by the global tour order, local tour planning is carried out within the target region. Subsequently, viewpoints are generated around the target sub-cluster that best matches the local tour order, and the optimal one is selected for trajectory planning. Both simulations and real-world experiments are given to illustrate the effectiveness of the proposed method.