Map-Change-Driven Closed-Loop Replanning for UAV Navigation in Unknown Indoor Environments

Autonomous Unmanned Aerial Vehicle (UAV) navigation in unknown indoor environments is challenged by incremental map revelation and non-uniform geometric changes, which frequently invalidate preplanned trajectories. Existing time-triggered replanning strategies are poorly aligned with such irregular environmental evolution, often resulting in either redundant computation or delayed responses to critical structural variations. To overcome these limitations, this paper proposes a map-change-driven closed-loop replanning mechanism (MCR) embedded within a distance-field-based hierarchical exploration–planning–control framework. The proposed approach explicitly monitors local Euclidean Signed Distance Field (ESDF) structural changes and exploration goal updates, triggering replanning only when significant geometric or task-level variations are detected. This event-driven design enables timely trajectory adaptation while effectively suppressing unnecessary replanning. Extensive experiments conducted in a high-fidelity indoor warehouse simulation environment demonstrate that the proposed method consistently outperforms single-shot planning and fixed-interval replanning baselines in terms of task success rate, trajectory smoothness, safety margin, and replanning efficiency. These results validate the effectiveness of using map structural evolution as the core driver for replanning in unknown indoor UAV navigation.