The research on spatial accuracy-driven adaptive measurement station planning for laser trackers based on Monte Carlo simulations and GA-BP neural networks

Abstract In the process of aircraft measurement-assisted assembly, the laser beam path between laser trackers and target points is prone to obstruction by large components and complex tooling, making it difficult to meet assembly requirements in terms of measurement accuracy and efficiency. This paper proposes a spatial accuracy-driven adaptive laser tracker station planning method based on Monte Carlo and genetic algorithm-backpropagation (GA-BP) neural networks to address this issue. First, the measurement uncertainty of laser trackers was analyzed, and a GA-BP neural network-based rapid uncertainty prediction model was established. The analysis efficiency improved by 100 times compared with Monte Carlo methods when processing million-level data points, while maintaining prediction accuracy consistently within 0.001 mm. Subsequently, a spatial accuracy-driven adaptive laser tracker station planning method was developed. Integrated with the rapid uncertainty prediction model and measurement obstruction detection algorithms, this method adaptively plans measurement stations. Compared with traditional station planning approaches, it demonstrates at least double the efficiency while achieving the minimum number of stations and maximum target point coverage. Finally, experimental verification was conducted in a simulated aircraft wing-body docking scenario. Results show that the average difference between simulated and actual measurement uncertainties is less than 0.002 mm, satisfying the requirement of 0.01 mm discrepancy. The fitting residuals of target points under two measurement stations all remain within uncertainty thresholds and below actual precision constraints. The proposed laser tracker station planning method proves effective in achieving high-efficiency and high-precision measurement requirements for aircraft assembly.