In response to increasing demands for high-efficiency robotic drilling of medium- and large- scale structures such as aircraft fuselages, this study proposes a novel path planning algorithm that enhances traditional greedy approaches by incorporating grouping mechanisms. The proposed "Grouped Greedy Method" classifies machining points based on spatial distribution into linear, intersection, and high-density regions. Each group is then processed with localized greedy optimization, while the sequence of groups is determined using centroid-based greedy traversal, enabling both global and local efficiency in path construction. Compared to the conventional greedy algorithm, which tends to suffer from local optimality and inefficiencies in complex configurations, the proposed method demonstrates significant improvement in path quality for irregular or clustered point arrangements. Simulated evaluations under two representative hole distribution patterns confirm that, although the grouped method incurs higher computational cost (approximately five times longer on average), it achieves up to 24% reduction in path length in certain cases. Time complexity analysis indicates that both methods maintain O(k∙n2) scaling, where n is the number of points and k is the number of trials, while the proposed method leads to higher constant factors due to its multi-level optimization process. These results suggest that the grouped greedy approach offers a practical trade-off between path quality and computation time, making it especially effective in precision-critical machining scenarios with complex structural constraints.
Ota et al. (Wed,) studied this question.