Large-Scale Measurement Layout Optimization Method Based on Laser Multilateration

Abstract

Laser multilateration is a measurement method based on the distance intersection of multiple laser trackers which has been widely used in large-scale measurements. However, the layout of laser trackers has a great impact on the final measurement accuracy. In order to improve the overall measurement accuracy, firstly, a measurement uncertainty model based on laser multilateration is established. Secondly, a fast laser intersection detection constraint algorithm based on a k-DOPS bounding box and an adaptive target ball incident angle constraint detection algorithm are established for large-scale measurement scenes. Finally, the constrained layout optimization of the laser trackers is realized by using an improved cellular genetic algorithm. The results show that the optimized system layout can achieve the full coverage of measurement points and has higher measurement accuracy. Compared with the traditional genetic algorithm, the improved cellular genetic algorithm converges faster and obtains a better position layout.