Rapid Autonomous Exploration of Large-Scale Environments for Ground Robots Based on Region Partitioning

Autonomous exploration in large environments often leads to inefficient long backtracking, as distant targets are prioritized over closer ones. In this work, a hierarchical planning method is proposed, which employs region partitioning to systematically address the aforementioned issue. The space is dynamically partitioned at a coarse resolution, and as exploration progresses, regions with sufficient known areas are further subdivided to locate unknown areas more precisely. A utility function considering unknown area size, travel distance and sequence similarity is used, and the simulated annealing algorithm generates a subregion sequence for global guidance. Within each subregion, a linear acceleration model helps select target points. This method reduces computational load and minimizes long-distance backtracking, enabling more efficient high-frequency planning. Extensive simulations and real-world tests show that our method significantly improves exploration efficiency compared to existing vision-based techniques.