FLARE: Fast Large-Scale Autonomous Exploration Guided by Unknown Regions

Abstract

Autonomous exploration is a critical foundation for unmanned aerial vehicle (UAV) applications such as search and rescue. However, existing methods typically focus only on known spaces or frontiers without considering unknown regions or providing further guidance for the global path, which results in low exploration efficiency. This paper proposes FLARE, which enables Fast UAV exploration in LARge-scale and complex unknown Environments. The incremental unknown region partitioning method partitions the unexplored space into multiple unknown regions in real-time by integrating known information with the sensor perception range. Building on this, the hierarchical planner first computes a global path that encompasses all unknown regions and then generates safe and feasible local trajectories for the UAV. We evaluate the performance of FLARE through extensive simulations and real-world experiments. The results show that, compared to existing state-of-the-art algorithms, FLARE significantly improves exploration efficiency, reducing exploration time by 16.8% to 27.9% and flight distance by 15.8% to 25.5%. The source code of FLARE will be released to benefit the community.