FASTEX: Fast UAV Exploration in Large-Scale Environments Using Dynamically Expanding Grids and Coverage Paths

Abstract

Autonomous exploration is essential for the effective deployment of quadrotors in various applications. However, existing approaches face significant challenges in large-scale environments, particularly in balancing global coverage efficiency and computational overhead. These limitations often result in poor adaptability to environmental changes and redundant revisits to previously explored areas, reducing overall exploration efficiency. To address these issues, we propose FASTEX, a fast UAV exploration framework designed for large-scale environments, using dynamically expanding grids and coverage paths to improve exploration efficiency. To support efficient exploration planning in large-scale scenarios, we introduce an efficient environment preprocessing method, including a dynamic grid expansion mechanism and a sparse roadmap. Furthermore, we present a hierarchical exploration planning framework that integrates an incremental global planner with a local planner, ensuring high coverage and computational efficiency. Extensive simulation tests demonstrate the superior performance and robustness of the proposed method compared to the state-of-the-art methods. In addition, we conduct various real-world experiments to validate the feasibility of our autonomous exploration system.