针对大尺寸三维形貌测量中高覆盖率与高精度的要求,提出了一种兼顾测量覆盖率和三维不确定度的智能组网规划方法。结合视觉测量要求,建立了视觉测量网络的离散化几何模型,确定了组网规划的决策变量,给出了视觉测量网络覆盖率和目标点三维不确定度两个概念。通过分析多种摄像机位姿约束条件,应用多目标遗传算法对组网决策变量进行全局性搜索,最终实现了多视觉的精确组网。对螺旋桨主体结构模型进行了仿真,结果表明测量网络覆盖率可以达到99.72%,三维不确定度可以收敛至0.0326 mm。通过单视觉多站式测量实验,验证了该策略的有效性和可行性。

In order to meet the requirements of high coverage rate and high precision in large-size three-dimensional profile measurement, an intelligent network planning method considering the measurement coverage rate and three-dimensional uncertainty is proposed. Combined with the requirements of the visual measurement, the discretization geometry model of visual measurement network is determined, the decision variables of network planning are established, and two concepts of the visual measurement network coverage rate and the three-dimensional uncertainty of the target are also given. The multi-visual network is realized accurately by the analysis of several constraint conditions of camera position and globally searching on decision variables through multi-objective genetic algorithm. Simulation of the propeller main structural model is conducted. It is concluded that the coverage rate of measurement network can reach 99.72%, and the three-dimensional uncertainty can converge to 0.0326 mm. The effectiveness and feasibility of the strategy are verified through single vision multi-station measurement experiment.