A Simulation Framework for Pre-Flight Coverage and Quality Assessment of UAV Bridge Inspection Routes Under Wind Disturbance

Abstract

A numerical method is proposed to assess the visual coverage and imaging quality of a pre-designed unmanned aerial vehicle (UAV) inspection route prior to field deployment. The modeled inspection domain includes 336 m2 of deck-underside, exterior-girder, and pier surfaces, 5424 area-weighted cells, fixed physical simulated-defect coordinates, explicit camera targets, and non-imaging transfer legs. For each of the three candidate flight strategies, 30 seeded Monte Carlo trials are conducted at each of four wind-disturbance levels. The retained observation should meet the field-of-view, range, incidence-angle, ground-sampling-distance (GSD), and repeated-capture requirements. Under no wind, valid coverage is 21.43%, 49.33%, and 93.97% for Basic, Improved, and Quality-aware, respectively; under the strongest wind level tested, the corresponding values are 16.64%, 47.38%, and 90.01%. Valid coverage is 0.275, 0.309 and 0.414 percentage points per meter for Basic, Improved and Quality-aware under no wind, respectively, and 0.214, 0.297 and 0.396 percentage points per meter under the highest wind level tested. The mean quality-adjusted resolution proxy for the Quality-aware route is raised from 0.983 to 1.409 mm/pixel over the range of tested winds. The results indicate that component-specific underside and pier observations offer significantly more coverage than exterior-only inspection, but at the cost of longer missions and greater distance travel. The framework is a numerical proof-of-concept for pre-flight route assessment rather than a field-validated engineering inspection tool; calibration against measured image, pose, wind, and defect-observation data is required before operational inspection performance can be inferred.