Feature-Aware Bidirectional Raster Path Planning for Robotic Line-Laser Scanning

Robotic line-laser scanning of large components with spatially dispersed inspection features requires effective scanning-region identification and path organization to ensure measurement completeness. This study proposes a feature-aware bidirectional raster path-planning method based on discrete feature-point clouds. First, a region-growing algorithm is employed to divide the measurement surface into subregions according to geometric characteristics and surface-normal similarity. Subsequently, a two-level partitioning strategy is introduced to identify scanning execution units containing target feature points. Local bidirectional scanning paths are generated within the retained execution units to improve the coverage of relevant measurement features. Finally, a greedy endpoint-selection strategy is adopted to organize the scanning sequence and connect the local paths. An aircraft nose-frame flange is used to evaluate the proposed method. Under the adopted geometric coverage criterion, the scanning coverage rate increases from 84% using the conventional bidirectional raster method to 97% using the proposed approach, representing an improvement of 13 percentage points. The results demonstrate the potential of feature-aware region partitioning to reduce unscanned target areas and improve the completeness of geometric scanning coverage. The study provides a path-planning framework for robotic line-laser inspection of large components. Further validation of robot-level reachability, collision avoidance, and practical scanning performance is required.