Multi‐UAV Path‐Coverage Task Allocation for Aircraft Surface Inspection Under Non‐Uniform Viewpoint Distribution: Density‐Adaptive Clustering‐Distance Method

ABSTRACT

For large‐aircraft surface inspection, nonuniform viewpoint distribution and wide task scope make Euclidean‐distance clustering difficult to jointly achieve path efficiency and workload balance. To address this, we propose a density‐adaptive clustering distance , allowing density information to influence cluster‐boundary adjustment throughout the iterative assignment process. Based on a C919 digital‐twin model, a nonuniform viewpoint set satisfying coverage and viewing‐angle constraints is generated; then, a Gaussian‐kernel local density on the viewpoint set (KDE‐style) and an S‐type mapping are used to construct an adaptive weight, and k‐means++ algorithm with is used to complete multi‐unmanned aerial vehicles (UAVs) task allocation under total‐distance minimisation, coefficient of variation (CV)‐controlled workload balance and single‐UAV energy constraints. For fair comparison, all methods share a unified ‘Nearest‐Neighbour + 2‐opt’ backend to evaluate each UAV closed‐loop length. Simulations on the C919 digital geometric model with 582 viewpoints and 3 UAVs show that DAF–CPP achieves the shortest mean total path (679.1 ± 2.6 m), the lowest mean maximum path (239.5 ± 13.4 m) and the lowest mean CV (0.1025 ± 0.0166) among eight allocation baselines under a unified 20‐seed protocol. Extended fairness metrics and Holm‐corrected tests further support the statistical significance of the allocation improvement.