PACE: Passability and Memory Integrated Exploration for UAVs in Large-Scale and Cluttered Environments

Abstract

Autonomous exploration in large-scale and cluttered environments remains a severe challenge for uncrewed aerial vehicles (UAVs). Existing methods still suffer from high computational costs, long-distance revisits, and discontinuous flight. To address these issues, we propose PACE, a hierarchical exploration framework designed for high-speed flight in large-scale and cluttered environments. First, we introduce a passability-based frontier classification mechanism to distinguish potential channels from local dead-ends, providing essential priors for decision-making. On this basis, a memory-guided global planner is proposed, which maintains the consistency of long-term global intent with low computation cost through an adaptive sliding window and anchor point constraints. Furthermore, it employs an adaptive priority adjustment method to adjust the global order more reasonably in scenarios of various scales to avoid future revisits. Finally, an intent-aware local planner is proposed to achieve agile and fluid flight by switching between traverse and link modes, tightly integrating global guidance with local maneuvers. Extensive simulation experiments demonstrate that the proposed method significantly outperforms SOTA methods in flight velocity and exploration efficiency. Real-world experiments further validate the value of the proposed method in practical applications.